var bibbase_data = {"data":"\"Loading..\"\n\n
\n\n \n\n \n\n \n \n\n \n\n \n \n\n \n\n \n
\n generated by\n \n \"bibbase.org\"\n\n \n
\n \n\n
\n\n \n\n\n
\n\n Excellent! Next you can\n create a new website with this list, or\n embed it in an existing web page by copying & pasting\n any of the following snippets.\n\n
\n JavaScript\n (easiest)\n
\n \n <script src=\"https://bibbase.org/show?bib=https://ngcharithperera.github.io/bibbase.github.io/finalize.bib&jsonp=1&jsonp=1\"></script>\n \n
\n\n PHP\n
\n \n <?php\n $contents = file_get_contents(\"https://bibbase.org/show?bib=https://ngcharithperera.github.io/bibbase.github.io/finalize.bib&jsonp=1\");\n print_r($contents);\n ?>\n \n
\n\n iFrame\n (not recommended)\n
\n \n <iframe src=\"https://bibbase.org/show?bib=https://ngcharithperera.github.io/bibbase.github.io/finalize.bib&jsonp=1\"></iframe>\n \n
\n\n

\n For more details see the documention.\n

\n
\n
\n\n
\n\n This is a preview! To use this list on your own web site\n or create a new web site from it,\n create a free account. The file will be added\n and you will be able to edit it in the File Manager.\n We will show you instructions once you've created your account.\n
\n\n
\n\n

To the site owner:

\n\n

Action required! Mendeley is changing its\n API. In order to keep using Mendeley with BibBase past April\n 14th, you need to:\n

    \n
  1. renew the authorization for BibBase on Mendeley, and
  2. \n
  3. update the BibBase URL\n in your page the same way you did when you initially set up\n this page.\n
  4. \n
\n

\n\n

\n \n \n Fix it now\n

\n
\n\n
\n\n\n
\n \n \n
\n
\n  \n 2018\n \n \n (3)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Designing Privacy-aware Internet of Things Applications.\n \n \n \n\n\n \n Perera, C.; Barhamgi, M.; Bandara, A. K.; Ajmal, M.; Price, B.; and Nuseibeh, B.\n\n\n \n\n\n\n Technical Report, Newcastle University. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@article{perera2017designing,\n title={Designing Privacy-aware Internet of Things Applications},\n author={Charith Perera and Mahmoud Barhamgi and Arosha K. Bandara and Muhammad Ajmal and Blaine Price and Bashar Nuseibeh},\n journal={Technical Report, Newcastle University},\n year={2018},\n keywords={Internet of Things Architectures, Privacy},\n keywords={Software Engineering},\n}\n\n%=============Book 1============\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Tensor-based Big Data Management Scheme for Dimensionality Reduction Problem in Smart Grid Systems: SDN Perspective.\n \n \n \n\n\n \n Kaur, D.; Aujla, G. S.; Kumar, N.; Zomaya, A.; Perera, C.; and Ranjan, R.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering,1-1. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@ARTICLE{J022, \nauthor={Devinder Kaur and Gagangeet Singh Aujla and Neeraj Kumar and Albert Zomaya and Charith Perera and Rajiv Ranjan}, \njournal={IEEE Transactions on Knowledge and Data Engineering}, \ntitle={Tensor-based Big Data Management Scheme for Dimensionality Reduction Problem in Smart Grid Systems: SDN Perspective}, \nyear={2018}, \nvolume={}, \nnumber={}, \npages={1-1}, \nabstract={Smart grid (SG) is an integration of traditional power grid with advanced information and communication infrastructure for bidirectional energy flow between grid and end users. A huge amount of data is being generated by various smart devices deployed in SG systems. Such a massive data generation from various smart devices in SG systems may generate issues such as-congestion, and available bandwidth on the networking infrastructure deployed between users and the grid. Hence, an efficient data transmission technique is required for providing desired QoS to the end users in this environment. Generally, the data generated by smart devices in SG has high dimensions in the form of multiple heterogeneous attributes, values of which are changed with time. The high dimensions of data may affect the performance of most of the designed solutions in this environment. Most of the existing schemes reported in the literature have complex operations for data dimensionality reduction problem which may deteriorate the performance of any implemented solution for this problem. To address these challenges, in this paper, a tensor-based big data management scheme is proposed for dimensionality reduction problem of big data generated from various smart devices. In the proposed scheme, firstly the Frobenius}, \nkeywords={Big Data;Data models;Electronic mail;Proposals;Smart devices;Tensile stress;Throughput;Big data;Dimensionality reduction;Flow table management;Smart grid;Software-defined networks;Tensors}, \ndoi={10.1109/TKDE.2018.2809747}, \nISSN={1041-4347}, \nmonth={},}\n\n\n%=============J021============\n
\n
\n\n\n
\n Smart grid (SG) is an integration of traditional power grid with advanced information and communication infrastructure for bidirectional energy flow between grid and end users. A huge amount of data is being generated by various smart devices deployed in SG systems. Such a massive data generation from various smart devices in SG systems may generate issues such as-congestion, and available bandwidth on the networking infrastructure deployed between users and the grid. Hence, an efficient data transmission technique is required for providing desired QoS to the end users in this environment. Generally, the data generated by smart devices in SG has high dimensions in the form of multiple heterogeneous attributes, values of which are changed with time. The high dimensions of data may affect the performance of most of the designed solutions in this environment. Most of the existing schemes reported in the literature have complex operations for data dimensionality reduction problem which may deteriorate the performance of any implemented solution for this problem. To address these challenges, in this paper, a tensor-based big data management scheme is proposed for dimensionality reduction problem of big data generated from various smart devices. In the proposed scheme, firstly the Frobenius\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n The elimination-selection based algorithm for efficient resource discovery in Internet of Things environments.\n \n \n \n\n\n \n Nunes, L. H.; Estrella, J. C.; Perera, C.; Reiff-Marganiec, S.; and Delbem, A. C. B.\n\n\n \n\n\n\n In 2018 15th IEEE Annual Consumer Communications Networking Conference (CCNC), pages 1-7, Jan 2018. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@INPROCEEDINGS{C020, \nauthor={Nunes, Luiz H. and Julio C. Estrella and Charith Perera and Stephan Reiff-Marganiec and Alexandre C. B. Delbem}, \nbooktitle={2018 15th IEEE Annual Consumer Communications Networking Conference (CCNC)}, \ntitle={The elimination-selection based algorithm for efficient resource discovery in Internet of Things environments}, \nyear={2018}, \nvolume={}, \nnumber={}, \npages={1-7}, \nabstract={Every day more and more objects are connected to the Internet to sense or actuate in some environment, composing the Internet of Things. IoT platforms will play a key role, as they will be responsible for managing low-level devices and data acquisition processes, and also support the development of new applications. One of the main challenges in IoT platforms will be the search and discovery of resources in large-scale and heterogeneous environments for reuse by other applications to support their specific requirements. In this paper, we propose an elimination-selection algorithm for search and discovery of resources in IoT environments. Our case study considers a real agricultural problem to be solved by the ViSIoT tool. The results show that our approach improves the quality of the proposed solution adding a small time overhead when compared to the TOPSIS algorithm used by ViSIoT.}, \nkeywords={Internet of Things Architectures},\nkeywords={Search and Discovery},\ndoi={10.1109/CCNC.2018.8319280}, \nISSN={}, \nmonth={Jan},}\n\n%=============C019============\n
\n
\n\n\n
\n Every day more and more objects are connected to the Internet to sense or actuate in some environment, composing the Internet of Things. IoT platforms will play a key role, as they will be responsible for managing low-level devices and data acquisition processes, and also support the development of new applications. One of the main challenges in IoT platforms will be the search and discovery of resources in large-scale and heterogeneous environments for reuse by other applications to support their specific requirements. In this paper, we propose an elimination-selection algorithm for search and discovery of resources in IoT environments. Our case study considers a real agricultural problem to be solved by the ViSIoT tool. The results show that our approach improves the quality of the proposed solution adding a small time overhead when compared to the TOPSIS algorithm used by ViSIoT.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2017\n \n \n (10)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Privacy Guidelines for Internet of Things: A Cheat Sheet.\n \n \n \n\n\n \n Perera, C.\n\n\n \n\n\n\n Technical Report, Newcastle University. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@article{perera2017privacy,\n  title={Privacy Guidelines for Internet of Things: A Cheat Sheet},\n  author={Perera, Charith},\n  journal={Technical Report, Newcastle University},\n  year={2017},\n  keywords={Internet of Things Architectures, Privacy},\n  keywords={Software Engineering},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n A Contextual Investigation of Location in the Home Using Bluetooth Low Energy Beacons.\n \n \n \n\n\n \n Perera, C.; Aghaee, S.; Faragher, R.; Harle, R.; and Blackwell, A.\n\n\n \n\n\n\n Technical Report, Newcastle University. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@article{perera2017contextual,\n title={A Contextual Investigation of Location in the Home Using Bluetooth Low Energy Beacons},\n author={Perera, Charith and Aghaee, Saeed and Faragher, Ramsey and Harle, Robert and Blackwell, Alan},\n journal={Technical Report, Newcastle University},\n year={2017},\n keywords={Internet of Things Architectures, Other},\n keywords={Human Computer Interaction},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Sensing as a Service for Internet of Things: A Roadmap.\n \n \n \n \n\n\n \n Perera, C.\n\n\n \n\n\n\n Leanpub, 1 edition, 2017.\n \n\n\n\n
\n\n\n\n \n \n \"Sensing website\n  \n \n \n \"Sensing link\n  \n \n \n \"Sensing buy\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@Book{Book1,\n title     = {Sensing as a Service for Internet of Things: A Roadmap},\n publisher = {Leanpub},\n year      = {2017},\n author    = {Perera, Charith},\n edition   = {1},\n pages     = {1--107},  \n keywords  = {Internet of Things Architectures, Sensing as a Service, Fog Computing, Privacy}, \n url_Website = {https://sensingasaservice.wordpress.com/},\n url_Link = {https://leanpub.com/sensingasaservice},\n url_buy = {http://www.lulu.com/shop/charith-perera/sensing-as-a-service-for-internet-of-things-a-roadmap/paperback/product-23040416.html},\n abstract = {Are you a undergraduate student, masters student, PhD student, or a researcher interested in Internet of Things? Looking for some novel ideas or research challenges in the field of Internet of Things? Have heard about Sensing as a Service, but not sure what it is? This book is for you.},\n}\n\n%=============B001============\n
\n
\n\n\n
\n Are you a undergraduate student, masters student, PhD student, or a researcher interested in Internet of Things? Looking for some novel ideas or research challenges in the field of Internet of Things? Have heard about Sensing as a Service, but not sure what it is? This book is for you.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n .\n \n \n \n\n\n \n ur Rehman, M. H.; Jayaraman, P. P.; and Perera, C.\n\n\n \n\n\n\n The Emergence of Edge-centric DIstributed IoT Analytics Platforms, pages 1-18. Qusay Hassan, A. u. R. K., editor(s). Internet of Things: Concepts, Technologies, Applications, and Implementations, CRC Press, 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n
@Inbook{B004,\n Title                    = {The Emergence of Edge-centric DIstributed IoT Analytics Platforms},\n Author                   = {Muhammad Habib ur Rehman and  Prem Prakash Jayaraman and Charith Perera},\n Booktitle                = {Internet of Things: Concepts, Technologies, Applications, and Implementations},\n Publisher                = {Internet of Things: Concepts, Technologies, Applications, and Implementations, CRC Press},\n Year                     = {2017},\n Editor                   = {Qusay Hassan, Atta ur Rahman Khan, Sajjad Madani},\n doi                      = {},\n keywords                 = {Internet of Things Architectures, Fog Computing}, \n Pages                    = {1-18}\n}\n\n%=============J022============\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Cross-Layer Optimization for Cooperative Content Distribution in Multihop Device-to-Device Networks.\n \n \n \n\n\n \n Xu, C.; Feng, J.; Zhou, Z.; Wu, J.; and Perera, C.\n\n\n \n\n\n\n IEEE Internet of Things Journal, PP(99): 1-10. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n
@ARTICLE{J021, \n author={Chen Xu and Junhao Feng and Zhenyu Zhou and Jun Wu and Charith Perera}, \n journal={IEEE Internet of Things Journal}, \n title={Cross-Layer Optimization for Cooperative Content Distribution in Multihop Device-to-Device Networks}, \n year={2017}, \n volume={PP}, \n number={99}, \n pages={1-10}, \n keywords={Internet of Things Architectures, Fog Computing},\n doi={10.1109/JIOT.2017.2741718}, \n month={},\n}\n\n\n%=============J020============\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Fog Computing for Sustainable Smart Cities: A Survey.\n \n \n \n \n\n\n \n Perera, C.; Qin, Y.; Estrella, J. C.; Reiff-Marganiec, S.; and Vasilakos, A. V.\n\n\n \n\n\n\n ACM Computer Survey, 50(3): 32:1–32:43. June 2017.\n \n\n\n\n
\n\n\n\n \n \n \"FogPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n
@article{J020,\n author = {Perera, Charith and Qin, Yongrui and Estrella, Julio C. and Reiff-Marganiec, Stephan and Vasilakos, Athanasios V.},\n title = {Fog Computing for Sustainable Smart Cities: A Survey},\n journal = {ACM Computer Survey},\n issue_date = {June 2017},\n volume = {50},\n number = {3},\n month = jun,\n year = {2017},\n issn = {0360-0300},\n pages = {32:1--32:43},\n articleno = {32},\n numpages = {43},\n url = {https://arxiv.org/pdf/1703.07079.pdf},\n doi = {10.1145/3057266},\n acmid = {3057266},\n publisher = {ACM},\n address = {New York, NY, USA},\n keywords={Internet of Things Architectures, Fog Computing},\n abstract = {The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, especially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g., network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build a sustainable IoT infrastructure for smart cities. In this article, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges toward implementing them, to shed light on future research directions on realizing Fog computing for building sustainable smart cities.},\n}\n\n%=============J019============\n
\n
\n\n\n
\n The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, especially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g., network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build a sustainable IoT infrastructure for smart cities. In this article, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges toward implementing them, to shed light on future research directions on realizing Fog computing for building sustainable smart cities.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Valorising the IoT Databox: creating value for everyone.\n \n \n \n \n\n\n \n Perera, C.; Wakenshaw, S. Y. L.; Baarslag, T.; Haddadi, H.; Bandara, A. K.; Mortier, R.; Crabtree, A.; Ng, I. C. L.; McAuley, D.; and Crowcroft, J.\n\n\n \n\n\n\n Transactions on Emerging Telecommunications Technologies, 28(1): 1-17. 2017.\n ett.3125\n\n\n\n
\n\n\n\n \n \n \"ValorisingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n
@Article{J018,\n author    = {Perera, Charith and Wakenshaw, Susan Y. L. and Baarslag, Tim and Haddadi, Hamed and Bandara, Arosha K. and Mortier, Richard and Crabtree, Andy and Ng, Irene C. L. and McAuley, Derek and Crowcroft, Jon},\n title     = {Valorising the IoT Databox: creating value for everyone},\n journal   = {Transactions on Emerging Telecommunications Technologies},\n year      = {2017},\n volume    = {28},\n number    = {1},\n pages     = {1-17},\n issn      = {2161-3915},\n note      = {ett.3125},\n doi       = {10.1002/ett.3125},\n publisher = {John Wiley \\& Sons, Ltd},\n url       = {https://arxiv.org/pdf/1609.03312.pdf},\n abstract  = {The Internet of Things is expected to generate large amounts of heterogeneous data from diverse sources including physical sensors, user devices and social media platforms. Over the last few years, significant attention has been focused on personal data, particularly data generated by smart wearable and smart home devices. Making personal data available for access and trade is expected to become a part of the data-driven digital economy. In this position paper, we review the research challenges in building personal Databoxes that hold personal data and enable data access by other parties and potentially thus sharing of data with other parties. These Databoxes are expected to become a core part of future data marketplaces.},\n keywords={Internet of Things Architectures, Sensing as a Service, Privacy},\n}\n\n\n%=============J017============\n
\n
\n\n\n
\n The Internet of Things is expected to generate large amounts of heterogeneous data from diverse sources including physical sensors, user devices and social media platforms. Over the last few years, significant attention has been focused on personal data, particularly data generated by smart wearable and smart home devices. Making personal data available for access and trade is expected to become a part of the data-driven digital economy. In this position paper, we review the research challenges in building personal Databoxes that hold personal data and enable data access by other parties and potentially thus sharing of data with other parties. These Databoxes are expected to become a core part of future data marketplaces.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Analytics-as-a-service in a multi-cloud environment through semantically-enabled hierarchical data processing.\n \n \n \n \n\n\n \n Jayaraman, P. P.; Perera, C.; Georgakopoulos, D.; Dustdar, S.; Thakker, D.; and Ranjan, R.\n\n\n \n\n\n\n Software: Practice and Experience, 47(8): 1139–1156. 2017.\n spe.2432\n\n\n\n
\n\n\n\n \n \n \"Analytics-as-a-servicePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n
@article {J014,\n author = {Jayaraman, Prem Prakash and Perera, Charith and Georgakopoulos, Dimitrios and Dustdar, Schahram and Thakker, Dhavalkumar and Ranjan, Rajiv},\n title = {Analytics-as-a-service in a multi-cloud environment through semantically-enabled hierarchical data processing},\n journal = {Software: Practice and Experience},\n issn = {1097-024X},\n url = {https://arxiv.org/pdf/1606.07935.pdf},\n doi = {10.1002/spe.2432},\n pages = {1139--1156},\n keywords={Internet of Things Architectures,  Sensing as a Service},\n keywords={Knowledge Representation, Search and Discovery},\n year = {2017},\n note = {spe.2432},\n volume   = {47},\n number   = {8},\n abstract = {A large number of cloud middleware platforms and tools are deployed to support a variety of Internet of Things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their primary and predefined objectives, where raw and processed data are only consumed by them. However, allowing third parties to access processed data to achieve their own objectives significantly increases integration, cooperation, and can also lead to innovative use of the data. Multicloud, privacy-aware environments facilitate such data access, allowing different parties to share processed data to reduce computation resource consumption collectively. However, there are interoperability issues in such environments that involve heterogeneous data and analytics-as-a-service providers. There is a lack of both - architectural blueprints that can support such diverse, multi-cloud environments, and corresponding empirical studies that show feasibility of such architectures. In this paper, we have outlined an innovative hierarchical data processing architecture that utilises semantics at all the levels of IoT stack in multicloud environments. We demonstrate the feasibility of such architecture by building a system based on this architecture using OpenIoT as a middleware, and Google Cloud and Microsoft Azure as cloud environments. The evaluation shows that the system is scalable and has no significant limitations or overheads.},\n}\n\n%=============J013============\n
\n
\n\n\n
\n A large number of cloud middleware platforms and tools are deployed to support a variety of Internet of Things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their primary and predefined objectives, where raw and processed data are only consumed by them. However, allowing third parties to access processed data to achieve their own objectives significantly increases integration, cooperation, and can also lead to innovative use of the data. Multicloud, privacy-aware environments facilitate such data access, allowing different parties to share processed data to reduce computation resource consumption collectively. However, there are interoperability issues in such environments that involve heterogeneous data and analytics-as-a-service providers. There is a lack of both - architectural blueprints that can support such diverse, multi-cloud environments, and corresponding empirical studies that show feasibility of such architectures. In this paper, we have outlined an innovative hierarchical data processing architecture that utilises semantics at all the levels of IoT stack in multicloud environments. We demonstrate the feasibility of such architecture by building a system based on this architecture using OpenIoT as a middleware, and Google Cloud and Microsoft Azure as cloud environments. The evaluation shows that the system is scalable and has no significant limitations or overheads.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n A Hybrid Approach for Data Analytics for Internet of Things.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n In Proceedings of the 7th ACM International Conference on the Internet of Things, of IoT '17, pages 7:1–7:8, New York, NY, USA, 2017. ACM\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n
\n
\n\n\n
\n The vision of the Internet of Things is to allow currently unconnected physical objects to be connected to the internet. There will be an extremely large number of internet connected devices that will be much more than the number of human being in the world all producing data. These data will be collected and delivered to the cloud for processing, especially with a view of finding meaningful information to then take action. However, ideally the data needs to be analysed locally to increase privacy, give quick responses to people and to reduce use of network and storage resources. To tackle these problems, distributed data analytics can be proposed to collect and analyse the data either in the edge or fog devices. In this paper, we explore a hybrid approach which means that both innetwork level and cloud level processing should work together to build effective IoT data analytics in order to overcome their respective weaknesses and use their specific strengths. Specifically, we collected raw data locally and extracted features by applying data fusion techniques on the data on resource constrained devices to reduce the data and then send the extracted features to the cloud for processing. We evaluated the accuracy and data consumption over network and thus show that it is feasible to increase privacy and maintain accuracy while reducing data communication demands.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n An Automated Negotiation Agent for Permission Management.\n \n \n \n \n\n\n \n Baarslag, T.; Alan, A. T.; Gomer, R.; Alam, M.; Perera, C.; Gerding, E. H.; and schraefel , m.\n\n\n \n\n\n\n In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, of AAMAS '17, pages 380–390, Richland, SC, 2017. International Foundation for Autonomous Agents and Multiagent Systems\n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n \n \"An slides\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n
@inproceedings{C017,\n author = {Baarslag, Tim and Alan, Alper T. and Gomer, Richard and Alam, Muddasser and Perera, Charith and Gerding, Enrico H. and schraefel, m.c.},\n title = {An Automated Negotiation Agent for Permission Management},\n booktitle = {Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems},\n series = {AAMAS '17},\n year = {2017},\n location = {Sao Paulo, Brazil},\n pages = {380--390},\n numpages = {11},\n url = {https://eprints.soton.ac.uk/405608/1/An_Automated_Negotiation_Agent_for_Permission_Management.pdf},\n acmid = {3091184},\n publisher = {International Foundation for Autonomous Agents and Multiagent Systems},\n address = {Richland, SC},\n keywords={Privacy},\n keywords={Artificial Intelligence, Human Computer Interaction},\n abstract = {The digital economy is based on data sharing yet citizens have little control about how their personal data is being used. While data management during web and app-based use is already a challenge, as the Internet of Things (IoT) scales up, the number of devices ac-cessing and requiring personal data will go beyond what a person can manually assess in terms of data access requests. Therefore, new approaches are needed for managing privacy preferences at scale and providing active consent around data sharing that can improve fidelity of operation in alignment with user intent. To address this challenge, we introduce a novel agent-based approach to negotiate the permission to exchange private data between users and services. Our agent negotiates based on learned preferences from actual users. To evaluate our agent-based approach, we developed an experimental tool to run on people's own smartphones, where users were asked to share their private, real data (e.g. photos, contacts , etc) under various conditions. The agent autonomously negotiates potential agreements for the user, which they can refine by manually continuing the negotiation. The agent learns from these interactions and updates the user model in subsequent interactions. We find that the agent is able to effectively capture the preferences and negotiate on the user's behalf but, surprisingly, does not reduce user engagement with the system. Understanding how interaction interplays with agent-based automation is a key component to successful deployment of negotiating agents in real-life settings and within the IoT context in particular. An Automated Negotiation Agent for Permission Management.},\n  url_Slides =   {slides/C017.pdf},\n}\n\n%=============C016============\n
\n
\n\n\n
\n The digital economy is based on data sharing yet citizens have little control about how their personal data is being used. While data management during web and app-based use is already a challenge, as the Internet of Things (IoT) scales up, the number of devices ac-cessing and requiring personal data will go beyond what a person can manually assess in terms of data access requests. Therefore, new approaches are needed for managing privacy preferences at scale and providing active consent around data sharing that can improve fidelity of operation in alignment with user intent. To address this challenge, we introduce a novel agent-based approach to negotiate the permission to exchange private data between users and services. Our agent negotiates based on learned preferences from actual users. To evaluate our agent-based approach, we developed an experimental tool to run on people's own smartphones, where users were asked to share their private, real data (e.g. photos, contacts , etc) under various conditions. The agent autonomously negotiates potential agreements for the user, which they can refine by manually continuing the negotiation. The agent learns from these interactions and updates the user model in subsequent interactions. We find that the agent is able to effectively capture the preferences and negotiate on the user's behalf but, surprisingly, does not reduce user engagement with the system. Understanding how interaction interplays with agent-based automation is a key component to successful deployment of negotiating agents in real-life settings and within the IoT context in particular. An Automated Negotiation Agent for Permission Management.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2016\n \n \n (10)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Sensing as a Service (S2aaS): Buying and Selling IoT Data.\n \n \n \n\n\n \n Perera, C.\n\n\n \n\n\n\n IEEE Internet of Things eNewsletters. nov 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n
@Article{J019,\n author  = {Charith Perera},\n title   = {Sensing as a Service (S2aaS): Buying and Selling IoT Data},\n journal = {IEEE Internet of Things eNewsletters},\n year    = {2016},\n month   = {nov},\n  keywords={Internet of Things Architectures, Sensing as a Service},\n}\n\n\n%=============J018============\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Multi-criteria IoT resource discovery: A comparative analysis.\n \n \n \n \n\n\n \n Nunes, L. H.; Estrella, J. C.; Perera, C.; Reiff-Marganiec, S.; and Botazzo Delbem, A. C.\n\n\n \n\n\n\n Software: Practice and Experience,n/a–n/a. 2016.\n spe.2469\n\n\n\n
\n\n\n\n \n \n \"Multi-criteriaPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@article {J017,\n author    = {Nunes, Luiz Henrique and Estrella, Julio Cezar and Perera, Charith and Reiff-Marganiec, Stephan and Botazzo Delbem, Alexandre Cláudio},\n title     = {Multi-criteria IoT resource discovery: A comparative analysis},\n journal   = {Software: Practice and Experience},\n publisher = {John Wiley & Sons, Ltd},\n issn      = {1097-024X},\n url       = {https://arxiv.org/pdf/1611.05172.pdf},\n doi       = {10.1002/spe.2469},\n pages     = {n/a--n/a},\n keywords={Internet of Things Architectures},\n keywords={Search and Discovery},\n year      = {2016},\n note      = {spe.2469},\n abstract  = {The growth of real world objects with embedded and globally networked sensors allows to consolidate the Internet of Things paradigm and increase the number of applications in the domains of ubiquitous and context-aware computing. The merging between Cloud Computing and Internet of Things named Cloud of Things will be the key to handle thousands of sensors and their data. One of the main challenges in the Cloud of Things is context-aware sensor search and selection. Typically, sensors require to be searched using two or more conflicting context properties. Most of the existing work uses some kind of multi-criteria decision analysis to perform the sensor search and selection, but does not show any concern for the quality of the selection presented by these methods. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision methods and their quality of selection comparing them with the Pareto-optimality solutions. The gathered results allow to analyse and compare these algorithms regarding their behaviour, the number of optimal solutions and redundancy.},\n}\n\n%=============J016============\n
\n
\n\n\n
\n The growth of real world objects with embedded and globally networked sensors allows to consolidate the Internet of Things paradigm and increase the number of applications in the domains of ubiquitous and context-aware computing. The merging between Cloud Computing and Internet of Things named Cloud of Things will be the key to handle thousands of sensors and their data. One of the main challenges in the Cloud of Things is context-aware sensor search and selection. Typically, sensors require to be searched using two or more conflicting context properties. Most of the existing work uses some kind of multi-criteria decision analysis to perform the sensor search and selection, but does not show any concern for the quality of the selection presented by these methods. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision methods and their quality of selection comparing them with the Pareto-optimality solutions. The gathered results allow to analyse and compare these algorithms regarding their behaviour, the number of optimal solutions and redundancy.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n A knowledge-based resource discovery for Internet of Things.\n \n \n \n \n\n\n \n Perera, C.; and Vasilakos, A. V.\n\n\n \n\n\n\n Knowledge-Based Systems Journal, 109: 122 - 136. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n
@article{J016,\n title = {A knowledge-based resource discovery for Internet of Things},\n journal = {Knowledge-Based Systems Journal},\n volume = {109},\n pages = {122 - 136},\n year = {2016},\n issn = {0950-7051},\n doi = {10.1016/j.knosys.2016.06.030},\n url = {https://arxiv.org/pdf/1606.08968.pdf},\n author = {Charith Perera and Athanasios V. Vasilakos},\n keywords={Internet of Things Architectures},\n keywords={Service Composition, Knowledge Representation, Search and Discovery},\n abstract = {In the sensing as a service paradigm, Internet of Things (IoT) Middleware platforms allow data consumers to retrieve the data they want without knowing the underlying technical details of IoT resources (i.e. sensors and data processing components). However, configuring an IoT middleware platform and retrieving data is a significant challenge for data consumers as it requires both technical knowledge and domain expertise. In this paper, we propose a knowledge driven approach called Context Aware Sensor Configuration Model (CASCOM) to simplify the process of configuring IoT middleware platforms, so the data consumers, specifically non-technical personnel, can easily retrieve the data they required. In this paper, we demonstrate how IoT resources can be described using semantics in such away that they can later be used to compose service work-flows. Such automated semantic-knowledge based IoT resource composition approach advances the current research. We demonstrate the feasibility and the usability of our approach through a prototype implementation based on an IoT middleware called Global Sensor Networks (GSN), though our model can be generalized to any other middleware platform.},\n}\n\n%=============J015============\n
\n
\n\n\n
\n In the sensing as a service paradigm, Internet of Things (IoT) Middleware platforms allow data consumers to retrieve the data they want without knowing the underlying technical details of IoT resources (i.e. sensors and data processing components). However, configuring an IoT middleware platform and retrieving data is a significant challenge for data consumers as it requires both technical knowledge and domain expertise. In this paper, we propose a knowledge driven approach called Context Aware Sensor Configuration Model (CASCOM) to simplify the process of configuring IoT middleware platforms, so the data consumers, specifically non-technical personnel, can easily retrieve the data they required. In this paper, we demonstrate how IoT resources can be described using semantics in such away that they can later be used to compose service work-flows. Such automated semantic-knowledge based IoT resource composition approach advances the current research. We demonstrate the feasibility and the usability of our approach through a prototype implementation based on an IoT middleware called Global Sensor Networks (GSN), though our model can be generalized to any other middleware platform.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Privacy-Knowledge Modeling for the Internet of Things: A Look Back.\n \n \n \n \n\n\n \n Perera, C.; Liu, C.; Ranjan, R.; Wang, L.; and Zomaya, A. Y.\n\n\n \n\n\n\n Computer, 49(12): 60-68. Dec 2016.\n \n\n\n\n
\n\n\n\n \n \n \"Privacy-KnowledgePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@ARTICLE{J015, \n author  = {Charith Perera and Chang Liu and Rajiv Ranjan and Lizhe Wang and Albert Y. Zomaya}, \n journal = {Computer}, \n title   = {Privacy-Knowledge Modeling for the Internet of Things: A Look Back}, \n year    = {2016}, \n volume  = {49}, \n number  = {12}, \n pages   = {60-68}, \n keywords={Internet of Things Architectures,  Privacy},\n keywords={Knowledge Representation},\n doi     = {10.1109/MC.2016.366}, \n ISSN    = {0018-9162}, \n month   = {Dec},\n abstract = {Internet of Things (IoT) and cloud computing together give us the ability to sense, collect, process, and analyse data so we can use them to better understand behaviours, habits, preferences and life patterns of users and lead them to consume resources more efficiently. In such knowledge discovery activities, privacy becomes a significant challenge due to the extremely personal nature of the knowledge that can be derived from the data and the potential risks involved. Therefore, understanding the privacy expectations and preferences of stakeholders is an important task in the IoT domain. In this paper, we review how privacy knowledge has been modelled and used in the past in different domains. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT. Finally, we discuss major research challenges and opportunities.},\n url = {https://arxiv.org/ftp/arxiv/papers/1606/1606.08480.pdf},\n}\n\n%=============J014============\n
\n
\n\n\n
\n Internet of Things (IoT) and cloud computing together give us the ability to sense, collect, process, and analyse data so we can use them to better understand behaviours, habits, preferences and life patterns of users and lead them to consume resources more efficiently. In such knowledge discovery activities, privacy becomes a significant challenge due to the extremely personal nature of the knowledge that can be derived from the data and the potential risks involved. Therefore, understanding the privacy expectations and preferences of stakeholders is an important task in the IoT domain. In this paper, we review how privacy knowledge has been modelled and used in the past in different domains. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT. Finally, we discuss major research challenges and opportunities.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n A Distributed Sensor Data Search Platform for Internet of Things Environments.\n \n \n \n \n\n\n \n Nunes, L.; Estrella, J.; Nakamura, L.; de Libardi, R.; Ferreira, C.; Jorge, L.; Perera, C.; and Reiff-Marganiec, S.\n\n\n \n\n\n\n International Journal of Services Computing, 4(1): 1-12. January-March 2016.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@Article{J013,\n  author   = {Luiz Nunes and Julio Estrella and Luis Nakamura and Rafael de Libardi and Carlos Ferreira and Liuri Jorge and Charith Perera and Stephan Reiff-Marganiec},\n  title    = {A Distributed Sensor Data Search Platform for Internet of Things Environments},\n  journal  = {International Journal of Services Computing},\n  year     = {2016},\n  volume   = {4},\n  number   = {1},\n  pages    = {1-12},\n  month    = {January-March},\n  abstract = {Abstract In the sensing as a service paradigm, Internet of Things (IoT) Middleware platforms allow data consumers to retrieve the data they want without knowing the underlying technical details of IoT resources (i.e. sensors and data processing components). However, configuring an IoT middleware platform and retrieving data is a significant challenge for data consumers as it requires both technical knowledge and domain expertise. In this paper, we propose a knowledge driven approach called Context Aware Sensor Configuration Model (CASCOM) to simplify the process of configuring IoT middleware platforms, so the data consumers, specifically non-technical personnel, can easily retrieve the data they required. In this paper, we demonstrate how IoT resources can be described using semantics in such away that they can later be used to compose service work-flows. Such automated semantic-knowledge-based IoT resource composition approach advances the current research. We demonstrate the feasibility and the usability of our approach through a prototype implementation based on an IoT middleware called Global Sensor Networks (GSN), though our model can be generalized to any other middleware platform. },\n  doi      = {10.1016/j.knosys.2016.06.030},\n  issn     = {0950-7051},\n  keywords={Internet of Things Architectures},\n  keywords={Search and Discovery},\n  url      = {http://www.sciencedirect.com/science/article/pii/S0950705116302015},\n}\n\n%=============J012============\n
\n
\n\n\n
\n Abstract In the sensing as a service paradigm, Internet of Things (IoT) Middleware platforms allow data consumers to retrieve the data they want without knowing the underlying technical details of IoT resources (i.e. sensors and data processing components). However, configuring an IoT middleware platform and retrieving data is a significant challenge for data consumers as it requires both technical knowledge and domain expertise. In this paper, we propose a knowledge driven approach called Context Aware Sensor Configuration Model (CASCOM) to simplify the process of configuring IoT middleware platforms, so the data consumers, specifically non-technical personnel, can easily retrieve the data they required. In this paper, we demonstrate how IoT resources can be described using semantics in such away that they can later be used to compose service work-flows. Such automated semantic-knowledge-based IoT resource composition approach advances the current research. We demonstrate the feasibility and the usability of our approach through a prototype implementation based on an IoT middleware called Global Sensor Networks (GSN), though our model can be generalized to any other middleware platform. \n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Applying Seamful Design in Location-Based Mobile Museum Applications.\n \n \n \n \n\n\n \n Nilsson, T.; Hogsden, C.; Perera, C.; Aghaee, S.; Scruton, D. J.; Lund, A.; and Blackwell, A. F.\n\n\n \n\n\n\n ACM Transactions on Multimedia Computing, Communications and Applications (TOMM), 12(4): 56:1–56:23. August 2016.\n \n\n\n\n
\n\n\n\n \n \n \"ApplyingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@article{J012,\n author = {Nilsson, Tommy and Hogsden, Carl and Perera, Charith and Aghaee, Saeed and Scruton, David J. and Lund, Andreas and Blackwell, Alan F.},\n title = {Applying Seamful Design in Location-Based Mobile Museum Applications},\n journal = {ACM Transactions on Multimedia Computing, Communications and Applications (TOMM)},\n issue_date = {August 2016},\n volume = {12},\n number = {4},\n month = aug,\n year = {2016},\n issn = {1551-6857},\n pages = {56:1--56:23},\n articleno = {56},\n numpages = {23},\n url = {https://arxiv.org/pdf/1605.05528.pdf},\n doi = {10.1145/2962720},\n acmid = {2962720},\n publisher = {ACM},\n address = {New York, NY, USA},\n keywords = {},\n abstract = {The application of mobile computing is currently altering patterns of our behavior to a greater degree than perhaps any other invention. In combination with the introduction of power-efficient wireless communication technologies, such as Bluetooth Low Energy (BLE), designers are today increasingly empowered to shape the way we interact with our physical surroundings and thus build entirely new experiences. However, our evaluations of BLE and its abilities to facilitate mobile location-based experiences in public environments revealed a number of potential problems. Most notably, the position and orientation of the user in combination with various environmental factors, such as crowds of people traversing the space, were found to cause major fluctuations of the received BLE signal strength. These issues are rendering a seamless functioning of any location-based application practically impossible. Instead of achieving seamlessness by eliminating these technical issues, we thus choose to advocate the use of a seamful approach, that is, to reveal and exploit these problems and turn them into a part of the actual experience. In order to demonstrate the viability of this approach, we designed, implemented, and evaluated the Ghost Detector—an educational location-based museum game for children. By presenting a qualitative evaluation of this game and by motivating our design decisions, this article provides insight into some of the challenges and possible solutions connected to the process of developing location-based BLE-enabled experiences for public cultural spaces.},\n keywords={Internet of Things Architectures, Other},\n keywords={Human Computer Interaction},\n} \n\n%=============J011============\n
\n
\n\n\n
\n The application of mobile computing is currently altering patterns of our behavior to a greater degree than perhaps any other invention. In combination with the introduction of power-efficient wireless communication technologies, such as Bluetooth Low Energy (BLE), designers are today increasingly empowered to shape the way we interact with our physical surroundings and thus build entirely new experiences. However, our evaluations of BLE and its abilities to facilitate mobile location-based experiences in public environments revealed a number of potential problems. Most notably, the position and orientation of the user in combination with various environmental factors, such as crowds of people traversing the space, were found to cause major fluctuations of the received BLE signal strength. These issues are rendering a seamless functioning of any location-based application practically impossible. Instead of achieving seamlessness by eliminating these technical issues, we thus choose to advocate the use of a seamful approach, that is, to reveal and exploit these problems and turn them into a part of the actual experience. In order to demonstrate the viability of this approach, we designed, implemented, and evaluated the Ghost Detector—an educational location-based museum game for children. By presenting a qualitative evaluation of this game and by motivating our design decisions, this article provides insight into some of the challenges and possible solutions connected to the process of developing location-based BLE-enabled experiences for public cultural spaces.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n City Data Fusion: Sensor Data Fusion in the Internet of Things.\n \n \n \n \n\n\n \n Wang, M.; Perera, C.; Jayaraman, P. P.; Zhang, M.; Strazdins, P.; Shyamsundar, R.; and Ranjan, R.\n\n\n \n\n\n\n International Journal of Distributed Systems and Technologies (IJDST), 7(1): 15–36. January 2016.\n \n\n\n\n
\n\n\n\n \n \n \"CityPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@article{J009,\n author = {Wang, Meisong and Perera, Charith and Jayaraman, Prem Prakash and Zhang, Miranda and Strazdins, Peter and Shyamsundar, R.K. and Ranjan, Rajiv},\n title = {City Data Fusion: Sensor Data Fusion in the Internet of Things},\n journal = {International Journal of Distributed Systems and Technologies (IJDST)},\n issue_date = {January 2016},\n volume = {7},\n number = {1},\n month = jan,\n year = {2016},\n issn = {1947-3532},\n pages = {15--36},\n numpages = {22},\n url = {https://arxiv.org/ftp/arxiv/papers/1506/1506.09118.pdf},\n doi = {10.4018/IJDST.2016010102},\n acmid = {2890929},\n publisher = {IGI Global},\n address = {Hershey, PA, USA},\n keywords={Internet of Things Architectures}, \n abstract = {Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. Our main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.},\n}\n\n%=============J008============\n
\n
\n\n\n
\n Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. Our main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n The Effects of Relative Importance of User Constraints in Cloud of Things Resource Discovery: A Case Study.\n \n \n \n \n\n\n \n Nunes, L. H.; Estrella, J. C.; Delbem, A. N.; Perera, C.; and Reiff-Marganiec, S.\n\n\n \n\n\n\n In Proceedings of the 9th International Conference on Utility and Cloud Computing, of UCC '16, pages 245–250, New York, NY, USA, 2016. ACM\n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\n  \n \n \n \"The slides\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@inproceedings{C016,\n author = {Nunes, Luiz H. and Estrella, Julio C. and Delbem, Alexandre N. and Perera, Charith and Reiff-Marganiec, Stephan},\n title = {The Effects of Relative Importance of User Constraints in Cloud of Things Resource Discovery: A Case Study},\n booktitle = {Proceedings of the 9th International Conference on Utility and Cloud Computing},\n series = {UCC '16},\n year = {2016},\n isbn = {978-1-4503-4616-0},\n location = {Shanghai, China},\n pages = {245--250},\n numpages = {6},\n url = {https://arxiv.org/pdf/1611.05170.pdf},\n doi = {10.1145/2996890.3007867},\n acmid = {3007867},\n publisher = {ACM},\n address = {New York, NY, USA},\n keywords={Internet of Things Architectures},\n keywords={Search and Discovery},\n abstract = {Over the last few years, the number of smart objects connected to the Internet has grown exponentially in comparison to the number of services and applications. The integration between Cloud Computing and Internet of Things, named as Cloud of Things, plays a key role in managing the connected things, their data and services. One of the main challenges in Cloud of Things is the resource discovery of the smart objects and their reuse in different contexts. Most of the existent work uses some kind of multi-criteria decision analysis algorithm to perform the resource discovery, but do not evaluate the impact that the user constraints has in the final solution. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision analyses algorithms and the impact of user constraints on them. We evaluated the quality of the proposed solutions using the Pareto-optimality concept.},\n  url_Slides =   {slides/C016.pdf},\n}\n\n%=============C015============\n
\n
\n\n\n
\n Over the last few years, the number of smart objects connected to the Internet has grown exponentially in comparison to the number of services and applications. The integration between Cloud Computing and Internet of Things, named as Cloud of Things, plays a key role in managing the connected things, their data and services. One of the main challenges in Cloud of Things is the resource discovery of the smart objects and their reuse in different contexts. Most of the existent work uses some kind of multi-criteria decision analysis algorithm to perform the resource discovery, but do not evaluate the impact that the user constraints has in the final solution. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision analyses algorithms and the impact of user constraints on them. We evaluated the quality of the proposed solutions using the Pareto-optimality concept.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Privacy-by-Design Framework for Assessing Internet of Things Applications and Platforms.\n \n \n \n \n\n\n \n Perera, C.; McCormick, C.; Bandara, A. K.; Price, B. A.; and Nuseibeh, B.\n\n\n \n\n\n\n In Proceedings of the 6th International Conference on the Internet of Things, of IoT'16, pages 83–92, New York, NY, USA, 2016. ACM\n \n\n\n\n
\n\n\n\n \n \n \"Privacy-by-DesignPaper\n  \n \n \n \"Privacy-by-Design slides\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@inproceedings{C015,\n author = {Perera, Charith and McCormick, Ciaran and Bandara, Arosha K. and Price, Blaine A. and Nuseibeh, Bashar},\n title = {Privacy-by-Design Framework for Assessing Internet of Things Applications and Platforms},\n booktitle = {Proceedings of the 6th International Conference on the Internet of Things},\n series = {IoT'16},\n year = {2016},\n isbn = {978-1-4503-4814-0},\n location = {Stuttgart, Germany},\n pages = {83--92},\n numpages = {10},\n url = {https://arxiv.org/pdf/1609.04060.pdf},\n doi = {10.1145/2991561.2991566},\n acmid = {2991566},\n publisher = {ACM},\n address = {New York, NY, USA},\n keywords={Internet of Things Architectures, Privacy},\n keywords={Software Engineering},\n abstract = {The Internet of Things (IoT) systems are designed and developed either as standalone applications from the ground-up or with the help of IoT middleware platforms. They are designed to support different kinds of scenarios, such as smart homes and smart cities. Thus far, privacy concerns have not been explicitly considered by IoT applications and middleware platforms. This is partly due to the lack of systematic methods for designing privacy that can guide the software development process in IoT. In this paper, we propose a set of guidelines, a privacy-by-design framework, that can be used to assess privacy capabilities and gaps of existing IoT applications as well as middleware platforms. We have evaluated two open source IoT middleware platforms, namely OpenIoT and Eclipse SmartHome, to demonstrate how our framework can be used in this way.},\n  url_Slides =   {slides/C015.pdf},\n}\n\n%=============C014============\n
\n
\n\n\n
\n The Internet of Things (IoT) systems are designed and developed either as standalone applications from the ground-up or with the help of IoT middleware platforms. They are designed to support different kinds of scenarios, such as smart homes and smart cities. Thus far, privacy concerns have not been explicitly considered by IoT applications and middleware platforms. This is partly due to the lack of systematic methods for designing privacy that can guide the software development process in IoT. In this paper, we propose a set of guidelines, a privacy-by-design framework, that can be used to assess privacy capabilities and gaps of existing IoT applications as well as middleware platforms. We have evaluated two open source IoT middleware platforms, namely OpenIoT and Eclipse SmartHome, to demonstrate how our framework can be used in this way.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Feed Me, Feed Me: An Exemplar for Engineering Adaptive Software.\n \n \n \n \n\n\n \n Bennaceur, A.; McCormick, C.; Galan, J. G.; Perera, C.; Smith, A.; Zisman, A.; and Nuseibeh, B.\n\n\n \n\n\n\n In Proceedings of the 11th International Workshop on Software Engineering for Adaptive and Self-Managing Systems, of SEAMS '16, pages 89–95, New York, NY, USA, 2016. ACM\n \n\n\n\n
\n\n\n\n \n \n \"FeedPaper\n  \n \n \n \"Feed slides\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n
@inproceedings{C014,\n author = {Bennaceur, Amel and McCormick, Ciaran and Galan, Jesus Garcia and Perera, Charith and Smith, Andrew and Zisman, Andrea and Nuseibeh, Bashar},\n title = {Feed Me, Feed Me: An Exemplar for Engineering Adaptive Software},\n booktitle = {Proceedings of the 11th International Workshop on Software Engineering for Adaptive and Self-Managing Systems},\n series = {SEAMS '16},\n year = {2016},\n isbn = {978-1-4503-4187-5},\n location = {Austin, Texas},\n pages = {89--95},\n numpages = {7},\n url = {http://oro.open.ac.uk/45597/1/seams16_57_cameraReady.pdf},\n doi = {10.1145/2897053.2897071},\n acmid = {2897071},\n publisher = {ACM},\n address = {New York, NY, USA},\n keywords={Internet of Things Architectures},\n keywords = {Software Engineering, Privacy},\n abstract = {The Internet of Things (IoT) promises to deliver improved quality of life for citizens, through pervasive connectivity and quantified monitoring of devices, people, and their environment. As such, the IoT presents a major new opportunity for research in adaptive software engineering. However, there are currently no shared exemplars that can support software engineering researchers to explore and potentially address the challenges of engineering adaptive software for the IoT, and to comparatively evaluate proposed solutions. In this paper, we present Feed me, Feed me, an exemplar that represents an IoT-based ecosystem to support food security at different levels of granularity: individuals, families, cities, and nations. We describe this exemplar using animated videos which highlight the requirements that have been informally observed to play a critical role in the success or failure of IoT-based software systems. These requirements are: security and privacy, interoperability, adaptation, and personalisation. To elicit a wide spectrum of user reactions, we created these animated videos based on the ContraVision empirical methodology, which specifically supports the elicitation of end-user requirements for controversial or futuristic technologies. Our deployment of ContraVision presented our pilot study subjects with an equal number of utopian and dystopian scenarios, derived from the food security domain, and described them at the different level of granularity. Our synthesis of the preliminary empirical findings suggests a number of key requirements and software engineering research challenges in this area. We offer these to the research community, together with a rich exemplar and associated scenarios available in both their textual form in the paper, and as a series of animated videos (http://sead1.open.ac.uk/fmfm/).},\n  url_Slides =   {slides/C014.pdf},\n}\n\n%=============C013============\n
\n
\n\n\n
\n The Internet of Things (IoT) promises to deliver improved quality of life for citizens, through pervasive connectivity and quantified monitoring of devices, people, and their environment. As such, the IoT presents a major new opportunity for research in adaptive software engineering. However, there are currently no shared exemplars that can support software engineering researchers to explore and potentially address the challenges of engineering adaptive software for the IoT, and to comparatively evaluate proposed solutions. In this paper, we present Feed me, Feed me, an exemplar that represents an IoT-based ecosystem to support food security at different levels of granularity: individuals, families, cities, and nations. We describe this exemplar using animated videos which highlight the requirements that have been informally observed to play a critical role in the success or failure of IoT-based software systems. These requirements are: security and privacy, interoperability, adaptation, and personalisation. To elicit a wide spectrum of user reactions, we created these animated videos based on the ContraVision empirical methodology, which specifically supports the elicitation of end-user requirements for controversial or futuristic technologies. Our deployment of ContraVision presented our pilot study subjects with an equal number of utopian and dystopian scenarios, derived from the food security domain, and described them at the different level of granularity. Our synthesis of the preliminary empirical findings suggests a number of key requirements and software engineering research challenges in this area. We offer these to the research community, together with a rich exemplar and associated scenarios available in both their textual form in the paper, and as a series of animated videos (http://sead1.open.ac.uk/fmfm/).\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2015\n \n \n (8)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n .\n \n \n \n\n\n \n Perera, C.; Jayaraman, P. P; Jayawardena, S.; Zaslavsky, A.; Liu, C. H.; and Christen, P.\n\n\n \n\n\n\n Mobile Sensing Devices and Platforms for CPS, pages 11–40. Liu, C. H.; and Zhang, Y., editor(s). Cyber Physical Systems: Architectures, Protocols and Applications, CRC Press, 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n
@Inbook{B002,\n Title                   = {Mobile Sensing Devices and Platforms for CPS},\n Author                  = {Perera, Charith and Jayaraman, Prem P and Jayawardena, Srimal and Zaslavsky, Arkady and Liu, Chi Harold and Christen, Peter},\n Booktitle               = {Cyber Physical Systems: Architectures, Protocols and Applications},\n Publisher               = {Cyber Physical Systems: Architectures, Protocols and Applications, CRC Press},\n Year                    = {2015},\n Editor                  = {Liu, Chi Harold and Zhang, Yan},\n doi                     = {10.1201/b19003-5},\n keywords                = {Internet of Things Architectures, Fog Computing, Sensing as a Service},  \n Pages                   = {11--40},\n}\n\n%=============B003============\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n .\n \n \n \n\n\n \n Perera, C.; Liu, C. H.; and Christen, P.\n\n\n \n\n\n\n Device Search and Selection for CPS, pages 67–99. Liu, C. H.; and Zhang, Y., editor(s). Cyber Physical Systems: Architectures, Protocols and Applications, CRC Press, 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@Inbook{B003,\n Title                    = {Device Search and Selection for CPS},\n Author                   = {Perera, Charith and Liu, Chi Harold and Christen, Peter},\n Booktitle                = {Cyber Physical Systems: Architectures, Protocols and Applications},\n Publisher                = {Cyber Physical Systems: Architectures, Protocols and Applications, CRC Press},\n Year                     = {2015},\n Editor                   = {Liu, Chi Harold and Zhang, Yan},\n doi                      = {10.1201/b19003-7},\n keywords                 = {Internet of Things Architectures}, \n Pages                    = {67--99}\n}\n\n%=============B004============\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Energy-Efficient Location and Activity-Aware On-Demand Mobile Distributed Sensing Platform for Sensing as a Service in IoT Clouds.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n IEEE Transactions on Computational Social Systems, 2(4): 171-181. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"Energy-EfficientPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n
\n
\n\n\n
\n The Internet of Things (IoT) envisions billions of sensors deployed around us and connected to the Internet, where the mobile crowd sensing technologies are widely used to collect data in different contexts of the IoT paradigm. Due to the popularity of Big Data technologies, processing and storing large volumes of data have become easier than ever. However, large-scale data management tasks still require significant amounts of resources that can be expensive regardless of whether they are purchased or rented (e.g., pay-as-you-go infrastructure). Further, not everyone is interested in such large-scale data collection and analysis. More importantly, not everyone has the financial and computational resources to deal with such large volumes of data. Therefore, a timely need exists for a cloud-integrated mobile crowd sensing platform that is capable of capturing sensors data, on-demand, based on conditions enforced by the data consumers. In this paper, we propose a context-aware, specifically, location and activity-aware mobile sensing platform called context-aware mobile sensor data engine (C-MOSDEN) for the IoT domain. We evaluated the proposed platform using three real-world scenarios that highlight the importance of selective sensing. The computational effectiveness and efficiency of the proposed platform are investigated and are used to highlight the advantages of context-aware selective sensing.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n End-to-End Privacy for Open Big Data Markets.\n \n \n \n \n\n\n \n Perera, C.; Ranjan, R.; and Wang, L.\n\n\n \n\n\n\n IEEE Cloud Computing, 2(4): 44-53. July 2015.\n \n\n\n\n
\n\n\n\n \n \n \"End-to-EndPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n
@ARTICLE{J010, \n author={Charith Perera and Rajiv Ranjan and Lizhe Wang}, \n journal={IEEE Cloud Computing}, \n title={End-to-End Privacy for Open Big Data Markets}, \n year={2015}, \n volume={2}, \n number={4}, \n pages={44-53}, \n abstract={Establishing an open data market would require the creation of a data trading model to facilitate exchange of data between different parties in the Internet of Things (IoT) domain. The data collected by IoT products and solutions are expected to be traded in these markets. Data owners will collect data using IoT products and solutions. Data consumers who are interested will negotiate with the data owners to get access to such data. Data captured by IoT products will allow data consumers to further understand the preferences and behaviors of data owners and to generate additional business value using techniques ranging from waste reduction to personalized service offerings. In open data markets, data consumers will be able to give back part of the additional value generated to the data owners. However, privacy becomes a significant issue when data that can be used to derive extremely personal information is being traded. This article discusses why privacy matters in the IoT domain in general and especially in open data markets, and then surveys existing privacy-preserving strategies and design techniques that can be used to facilitate end-to-end privacy for open data markets. It also highlights some of the major research challenges that must be addressed to make the vision of open data markets a reality through ensuring the privacy of stakeholders.}, \n keywords={Internet of Things Architectures, Sensing as a Service, Privacy},\n doi={10.1109/MCC.2015.78}, \n ISSN={2325-6095}, \n month={July},\n url = {https://arxiv.org/ftp/arxiv/papers/1506/1506.08865.pdf},\n}\n\n%=============J009============\n
\n
\n\n\n
\n Establishing an open data market would require the creation of a data trading model to facilitate exchange of data between different parties in the Internet of Things (IoT) domain. The data collected by IoT products and solutions are expected to be traded in these markets. Data owners will collect data using IoT products and solutions. Data consumers who are interested will negotiate with the data owners to get access to such data. Data captured by IoT products will allow data consumers to further understand the preferences and behaviors of data owners and to generate additional business value using techniques ranging from waste reduction to personalized service offerings. In open data markets, data consumers will be able to give back part of the additional value generated to the data owners. However, privacy becomes a significant issue when data that can be used to derive extremely personal information is being traded. This article discusses why privacy matters in the IoT domain in general and especially in open data markets, and then surveys existing privacy-preserving strategies and design techniques that can be used to facilitate end-to-end privacy for open data markets. It also highlights some of the major research challenges that must be addressed to make the vision of open data markets a reality through ensuring the privacy of stakeholders.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey.\n \n \n \n \n\n\n \n Perera, C.; Liu, C. H.; and Jayawardena, S.\n\n\n \n\n\n\n IEEE Transactions on Emerging Topics in Computing, 3(4): 585-598. Dec 2015.\n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@ARTICLE{J008, \n author={Charith Perera and Chi Harold Liu and Srimal Jayawardena}, \n journal={IEEE Transactions on Emerging Topics in Computing}, \n title={The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey}, \n year={2015}, \n volume={3}, \n number={4}, \n pages={585-598}, \n abstract={The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as Radio frequency identifications, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organizations making their way into the market. In this paper, we survey over one hundred IoT smart solutions in the marketplace and examine them closely in order to identify the technologies used, functionalities, and applications. Based on the application domain, we classify and discuss these solutions under five different categories: 1) smart wearable; 2) smart home; 3) smart city; 4) smart environment; and 5) smart enterprise. This survey is intended to serve as a guideline and a conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions.}, \n keywords={Internet of Things Architectures}, \n doi={10.1109/TETC.2015.2390034}, \n ISSN={2168-6750}, \n url = {https://arxiv.org/pdf/1502.00134.pdf},\n month={Dec},\n}\n\n%=============J007============\n
\n
\n\n\n
\n The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as Radio frequency identifications, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organizations making their way into the market. In this paper, we survey over one hundred IoT smart solutions in the marketplace and examine them closely in order to identify the technologies used, functionalities, and applications. Based on the application domain, we classify and discuss these solutions under five different categories: 1) smart wearable; 2) smart home; 3) smart city; 4) smart environment; and 5) smart enterprise. This survey is intended to serve as a guideline and a conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Big Data Privacy in the Internet of Things Era.\n \n \n \n \n\n\n \n Perera, C.; Ranjan, R.; Wang, L.; Khan, S. U.; and Zomaya, A. Y.\n\n\n \n\n\n\n IT Professional, 17(3): 32-39. May 2015.\n \n\n\n\n
\n\n\n\n \n \n \"BigPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n
@ARTICLE{J006, \n author={Charith Perera and Rajiv Ranjan and Lizhe Wang and Samee U. Khan and Albert Y. Zomaya}, \n journal={IT Professional}, \n title={Big Data Privacy in the Internet of Things Era}, \n year={2015}, \n volume={17}, \n number={3}, \n pages={32-39}, \n abstract={Over the last few years, we've seen a plethora of Internet of Things (IoT) solutions, products, and services make their way into the industry's marketplace. All such solutions will capture large amounts of data pertaining to the environment as well as their users. The IoT's objective is to learn more and better serve system users. Some IoT solutions might store data locally on devices ("things"), whereas others might store it in the cloud. The real value of collecting data comes through data processing and aggregation on a large scale, where new knowledge can be extracted. However, such procedures can lead to user privacy issues. This article discusses some of the main challenges of privacy in the IoT as well as opportunities for research and innovation. The authors also introduce some of the ongoing research efforts that address IoT privacy issues.}, \n keywords={Internet of Things Architectures, Privacy},\n doi={10.1109/MITP.2015.34}, \n ISSN={1520-9202}, \n month={May}, \n url = {https://arxiv.org/ftp/arxiv/papers/1412/1412.8339.pdf},\n}\n\n%=============J005============\n
\n
\n\n\n
\n Over the last few years, we've seen a plethora of Internet of Things (IoT) solutions, products, and services make their way into the industry's marketplace. All such solutions will capture large amounts of data pertaining to the environment as well as their users. The IoT's objective is to learn more and better serve system users. Some IoT solutions might store data locally on devices (\"things\"), whereas others might store it in the cloud. The real value of collecting data comes through data processing and aggregation on a large scale, where new knowledge can be extracted. However, such procedures can lead to user privacy issues. This article discusses some of the main challenges of privacy in the IoT as well as opportunities for research and innovation. The authors also introduce some of the ongoing research efforts that address IoT privacy issues.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Energy-Efficient and Context-Aware Smartphone Sensor Employment.\n \n \n \n\n\n \n Yurur, O.; Liu, C. H.; Perera, C.; Chen, M.; Liu, X.; and Moreno, W.\n\n\n \n\n\n\n IEEE Transactions on Vehicular Technology, 64(9): 4230-4244. Sept 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@Article{J005,\n author   = {Ozgur Yurur and Chi Harold Liu and Charith Perera and Min Chen and Xue Liu and Wilfrido Moreno},\n title    = {Energy-Efficient and Context-Aware Smartphone Sensor Employment},\n journal  = {IEEE Transactions on Vehicular Technology},\n year     = {2015},\n volume   = {64},\n number   = {9},\n pages    = {4230-4244},\n month    = {Sept},\n issn     = {0018-9545},\n abstract = {New-generation mobile devices will inevitably be employed within the realm of ubiquitous sensing. In particular, smartphones have been increasingly used for human activity recognition (HAR)-based studies. It is believed that recognizing human-centric activity patterns could accurately enough give a better understanding of human behaviors. Further, such an ability could have a chance to assist individuals to enhance the quality of their lives. However, the integration and realization of HAR-based mobile services stand as a significant challenge on resource-constrained mobile-embedded platforms. In this manner, this paper proposes a novel discrete-time inhomogeneous hidden semi-Markov model (DT-IHS-MM)-based generic framework to address a better realization of HAR-based mobile context awareness. In addition, we utilize power-efficient sensor management strategies by providing three intuitive methods and constrained Markov decision process (CMDP), as well as partially observable Markov decision process (POMDP)-based optimal methods. Moreover, a feedback control mechanism is integrated to balance the tradeoff between accuracy in context inference and power consumption. In conclusion, the proposed sensor management methods achieve a 40% overall enhancement in the power consumption caused by the physical sensor with respect to the overall 85-90% accuracy ratio due to the provided adaptive context inference framework.},\n doi      = {10.1109/TVT.2014.2364619},\n keywords={Fog Computing},\n}\n\n%=============J004============\n
\n
\n\n\n
\n New-generation mobile devices will inevitably be employed within the realm of ubiquitous sensing. In particular, smartphones have been increasingly used for human activity recognition (HAR)-based studies. It is believed that recognizing human-centric activity patterns could accurately enough give a better understanding of human behaviors. Further, such an ability could have a chance to assist individuals to enhance the quality of their lives. However, the integration and realization of HAR-based mobile services stand as a significant challenge on resource-constrained mobile-embedded platforms. In this manner, this paper proposes a novel discrete-time inhomogeneous hidden semi-Markov model (DT-IHS-MM)-based generic framework to address a better realization of HAR-based mobile context awareness. In addition, we utilize power-efficient sensor management strategies by providing three intuitive methods and constrained Markov decision process (CMDP), as well as partially observable Markov decision process (POMDP)-based optimal methods. Moreover, a feedback control mechanism is integrated to balance the tradeoff between accuracy in context inference and power consumption. In conclusion, the proposed sensor management methods achieve a 40% overall enhancement in the power consumption caused by the physical sensor with respect to the overall 85-90% accuracy ratio due to the provided adaptive context inference framework.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Natural Notation for the Domestic Internet of Things.\n \n \n \n \n\n\n \n Perera, C.; Aghaee, S.; and Blackwell, A.\n\n\n \n\n\n\n In End-User Development: 5th International Symposium, IS-EUD 2015, Madrid, Spain, May 26-29, 2015. Proceedings, pages 25–41, 2015. Springer International Publishing\n \n\n\n\n
\n\n\n\n \n \n \"NaturalPaper\n  \n \n \n \"Natural slides\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@inproceedings{C013,\n author={Perera, Charith and Aghaee, Saeed and Blackwell, Alan},\n %editor={Diaz, Paloma and Pipek, Volkmar and Ardito, Carmelo and Jensen, Carlos and Aedo, Ignacio and Boden, Alexander},\n title={Natural Notation for the Domestic Internet of Things},\n booktitle={End-User Development: 5th International Symposium, IS-EUD 2015, Madrid, Spain, May 26-29, 2015. Proceedings},\n year={2015},\n publisher={Springer International Publishing},\n pages={25--41},\n isbn={978-3-319-18425-8},\n doi={10.1007/978-3-319-18425-8_3},\n keywords={Internet of Things Architectures, Other},\n keywords={Human Computer Interaction},\n url={https://arxiv.org/pdf/1503.01895.pdf},\n abstract = {This study explores the use of natural language to give instructions that might be interpreted by Internet of Things (IoT) devices in a domestic 'smart home' environment. We start from the proposition that reminders can be considered as a type of end-user programming, in which the executed actions might be performed either by an automated agent or by the author of the reminder. We conducted an experiment in which people wrote sticky notes specifying future actions in their home. In different conditions, these notes were addressed to themselves, to others, or to a computer agent.We analyse the linguistic features and strategies that are used to achieve these tasks, including the use of graphical resources as an informal visual language. The findings provide a basis for design guidance related to end-user development for the Internet of Things.},\n  url_Slides =   {slides/C013.pdf},\n}\n\n\n%=============C012============\n
\n
\n\n\n
\n This study explores the use of natural language to give instructions that might be interpreted by Internet of Things (IoT) devices in a domestic 'smart home' environment. We start from the proposition that reminders can be considered as a type of end-user programming, in which the executed actions might be performed either by an automated agent or by the author of the reminder. We conducted an experiment in which people wrote sticky notes specifying future actions in their home. In different conditions, these notes were addressed to themselves, to others, or to a computer agent.We analyse the linguistic features and strategies that are used to achieve these tasks, including the use of graphical resources as an informal visual language. The findings provide a basis for design guidance related to end-user development for the Internet of Things.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2014\n \n \n (9)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n .\n \n \n \n \n\n\n \n Perera, C.; Jayaraman, P. P.; Zaslavsky, A.; Christen, P.; and Georgakopoulos, D.\n\n\n \n\n\n\n Context-Aware Dynamic Discovery and Configuration of `Things' in Smart Environments, pages 215–241. Bessis, N.; and Dobre, C., editor(s). Big Data and Internet of Things: A Roadmap for Smart Environments, Springer International Publishing, 2014.\n \n\n\n\n
\n\n\n\n \n \n \"Context-AwarePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@Inbook{B001,\n author\t=\t{Perera, Charith and Jayaraman, Prem Prakash and Zaslavsky, Arkady and Christen, Peter and Georgakopoulos, Dimitrios},\n editor\t=\t{Bessis, Nik and Dobre, Ciprian},\n Title=\t{Context-Aware Dynamic Discovery and Configuration of `Things' in Smart Environments},\n Booktitle = {Big Data and Internet of Things: A Roadmap for Smart Environments},\n year\t=\t {2014},\n publisher = {Big Data and Internet of Things: A Roadmap for Smart Environments, Springer International Publishing},\n pages\t=\t{215--241},\n isbn\t=\t{978-3-319-05029-4},\n keywords={Internet of Things Architectures, Fog Computing, Sensing as a Service},\n keywords={Search and Discovery}, \n doi\t=\t{10.1007/978-3-319-05029-4_9},\n url\t=\t{https://arxiv.org/pdf/1311.2134.pdf},\n abstract = {The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as RFIDs, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Currently, such Internet-connected objects or 'things' outnumber both people and computers connected to the Internet and their population is expected to grow to 50 billion in the next 5–10 years. To be able to develop IoT applications, such 'things' must become dynamically integrated into emerging information networks supported by architecturally scalable and economically feasible Internet service delivery models, such as cloud computing. Achieving such integration through discovery and configuration of 'things' is a challenging task. Towards this end, we propose a Context-Aware Dynamic Discovery of Things (CADDOT) model. We have developed a tool SmartLink, that is capable of discovering sensors deployed in a particular location despite their heterogeneity. SmartLink helps to establish the direct communication between sensor hardware and cloud-based IoT middleware platforms. We address the challenge of heterogeneity using a plug in architecture. Our prototype tool is developed on an Android platform. Further, we employ the Global Sensor Network (GSN) as the IoT middleware for the proof of concept validation. The significance of the proposed solution is validated using a test-bed that comprises 52 Arduino-based Libelium sensors.},\n \n}\n\n%=============B002============\n
\n
\n\n\n
\n The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as RFIDs, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Currently, such Internet-connected objects or 'things' outnumber both people and computers connected to the Internet and their population is expected to grow to 50 billion in the next 5–10 years. To be able to develop IoT applications, such 'things' must become dynamically integrated into emerging information networks supported by architecturally scalable and economically feasible Internet service delivery models, such as cloud computing. Achieving such integration through discovery and configuration of 'things' is a challenging task. Towards this end, we propose a Context-Aware Dynamic Discovery of Things (CADDOT) model. We have developed a tool SmartLink, that is capable of discovering sensors deployed in a particular location despite their heterogeneity. SmartLink helps to establish the direct communication between sensor hardware and cloud-based IoT middleware platforms. We address the challenge of heterogeneity using a plug in architecture. Our prototype tool is developed on an Android platform. Further, we employ the Global Sensor Network (GSN) as the IoT middleware for the proof of concept validation. The significance of the proposed solution is validated using a test-bed that comprises 52 Arduino-based Libelium sensors.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n A Survey on Internet of Things From Industrial Market Perspective.\n \n \n \n\n\n \n Perera, C.; Liu, C. H.; Jayawardena, S.; and Chen, M.\n\n\n \n\n\n\n IEEE Access, 2: 1660-1679. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@ARTICLE{J007, \n author={Charith Perera and Chi Harold Liu and Srimal Jayawardena and Min Chen}, \n journal={IEEE Access}, \n title={A Survey on Internet of Things From Industrial Market Perspective}, \n year={2014}, \n volume={2}, \n pages={1660-1679}, \n abstract={The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as radio frequency identifications, sensors, and actuators, as well as other instruments and smart appliances that are becoming an integral component of the Internet. Over the last few years, we have seen a plethora of IoT solutions making their way into the industry marketplace. Context-aware communications and computing have played a critical role throughout the last few years of ubiquitous computing and are expected to play a significant role in the IoT paradigm as well. In this paper, we examine a variety of popular and innovative IoT solutions in terms of context-aware technology perspectives. More importantly, we evaluate these IoT solutions using a framework that we built around well-known context-aware computing theories. This survey is intended to serve as a guideline and a conceptual framework for context-aware product development and research in the IoT paradigm. It also provides a systematic exploration of existing IoT products in the marketplace and highlights a number of potentially significant research directions and trends.}, \n keywords={Internet of Things Architectures}, \n doi={10.1109/ACCESS.2015.2389854}, \n ISSN={2169-3536}, \n month={},\n}\n\n%=============J006============\n
\n
\n\n\n
\n The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as radio frequency identifications, sensors, and actuators, as well as other instruments and smart appliances that are becoming an integral component of the Internet. Over the last few years, we have seen a plethora of IoT solutions making their way into the industry marketplace. Context-aware communications and computing have played a critical role throughout the last few years of ubiquitous computing and are expected to play a significant role in the IoT paradigm as well. In this paper, we examine a variety of popular and innovative IoT solutions in terms of context-aware technology perspectives. More importantly, we evaluate these IoT solutions using a framework that we built around well-known context-aware computing theories. This survey is intended to serve as a guideline and a conceptual framework for context-aware product development and research in the IoT paradigm. It also provides a systematic exploration of existing IoT products in the marketplace and highlights a number of potentially significant research directions and trends.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n EAI Endorsed Transactions on Collaborative Computing, 14(1). 5 2014.\n \n\n\n\n
\n\n\n\n \n \n \"MOSDEN:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things.\n \n \n \n\n\n \n Perera, C.; Zaslavsky, A.; Liu, C.; Compton, M.; Christen, P.; and Georgakopoulos, D.\n\n\n \n\n\n\n Sensors Journal, IEEE, 14(2): 406-420. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n
@ARTICLE{J003, \n author={Perera, Charith. and Zaslavsky, Arkady. and Liu, C.H. and Compton, M. and Christen, Peter and Georgakopoulos, Dimitrios}, \n journal={Sensors Journal, IEEE}, \n title={Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things}, \n year={2014}, \n volume={14}, \n number={2}, \n pages={406-420}, \n abstract={The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating 'things' or Internet Connected Objects (ICOs) that will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection, and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM considers user preferences and a broad range of sensor characteristics such as reliability, accuracy, location, battery life, and many more. This paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This paper also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.}, \n keywords={Internet of Things Architectures, Sensing as a Service},\n keywords={Knowledge Representation, Search and Discovery},\n doi={10.1109/JSEN.2013.2282292}, \n ISSN={1530-437X},\n}\n\n%=============J002============\n
\n
\n\n\n
\n The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating 'things' or Internet Connected Objects (ICOs) that will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection, and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM considers user preferences and a broad range of sensor characteristics such as reliability, accuracy, location, battery life, and many more. This paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This paper also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Sensing as a service model for smart cities supported by Internet of Things.\n \n \n \n \n\n\n \n Perera, C.; Zaslavsky, A.; Christen, P.; and Georgakopoulos, D.\n\n\n \n\n\n\n Transactions on Emerging Telecommunications Technologies, 2(1): 81-93. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"SensingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n
@Article{J002,\n  Title                    = {Sensing as a service model for smart cities supported by Internet of Things},\n  Author                   = {Perera, Charith and Zaslavsky, Arkady and Christen, Peter and Georgakopoulos, Dimitrios},\n  Journal                  = {Transactions on Emerging Telecommunications Technologies},\n  Year                     = {2014},\n  Number                   = {1},\n  Pages                    = {81-93},\n  Volume                   = {2},\n  Abstract                 = {The world population is growing at a rapid pace. Towns and cities are accommodating half of the world's population thereby creating tremendous pressure on every aspect of urban living. Cities are known to have large concentration of resources and facilities. Such environments attract people from rural areas. However, unprecedented attraction has now become an overwhelming issue for city governance and politics. The enormous pressure towards efficient city management has triggered various Smart City initiatives by both government and private sector businesses to invest in information and communication technologies to find sustainable solutions to the growing issues. The Internet of Things (IoT) has also gained significant attention over the past decade. IoT envisions to connect billions of sensors to the Internet and expects to use them for efficient and effective resource management in Smart Cities. Today, infrastructure, platforms and software applications are offered as services using cloud technologies. In this paper, we explore the concept of sensing as a service and how it fits with the IoT. Our objective is to investigate the concept of sensing as a service model in technological, economical and social perspectives and identify the major open challenges and issues.},\n  Doi                      = {10.1002/ett.2704},\n  ISSN                     = {2161-3915},\n  Publisher                = {John Wiley \\& Sons, Ltd},\n  Url                      = {https://arxiv.org/pdf/1307.8198.pdf},\n  keywords={Internet of Things Architectures, Sensing as a Service},\n\n}\n\n%=============J001============\n
\n
\n\n\n
\n The world population is growing at a rapid pace. Towns and cities are accommodating half of the world's population thereby creating tremendous pressure on every aspect of urban living. Cities are known to have large concentration of resources and facilities. Such environments attract people from rural areas. However, unprecedented attraction has now become an overwhelming issue for city governance and politics. The enormous pressure towards efficient city management has triggered various Smart City initiatives by both government and private sector businesses to invest in information and communication technologies to find sustainable solutions to the growing issues. The Internet of Things (IoT) has also gained significant attention over the past decade. IoT envisions to connect billions of sensors to the Internet and expects to use them for efficient and effective resource management in Smart Cities. Today, infrastructure, platforms and software applications are offered as services using cloud technologies. In this paper, we explore the concept of sensing as a service and how it fits with the IoT. Our objective is to investigate the concept of sensing as a service model in technological, economical and social perspectives and identify the major open challenges and issues.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Context Aware Computing for The Internet of Things: A Survey.\n \n \n \n \n\n\n \n Perera, C.; Zaslavsky, A.; Christen, P.; and Georgakopoulos, D.\n\n\n \n\n\n\n IEEE Communications Surveys Tutorials, 16(1): 414-454. First 2014.\n \n\n\n\n
\n\n\n\n \n \n \"ContextPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@ARTICLE{J001, \n author={Charith Perera and Arkady Zaslavsky and Peter Christen and Dimitrios Georgakopoulos}, \n journal={IEEE Communications Surveys Tutorials}, \n title={Context Aware Computing for The Internet of Things: A Survey}, \n year={2014}, \n volume={16}, \n number={1}, \n pages={414-454}, \n abstract={As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.}, \n keywords={Internet of Things Architectures},\n doi={10.1109/SURV.2013.042313.00197}, \n ISSN={1553-877X}, \n month={First},\n Url = {https://arxiv.org/pdf/1305.0982.pdf}, \n}\n\n\n%=============C020============\n
\n
\n\n\n
\n As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Improve the sustainability of Internet of Things through trading-based value creation.\n \n \n \n \n\n\n \n Perera, C.; and Zaslavsky, A.\n\n\n \n\n\n\n In Internet of Things (WF-IoT), 2014 IEEE World Forum on, pages 135-140, March 2014. \n \n\n\n\n
\n\n\n\n \n \n \"ImprovePaper\n  \n \n \n \"Improve slides\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@INPROCEEDINGS{C012, \n author={Charith Perera and Arkady Zaslavsky}, \n booktitle={Internet of Things (WF-IoT), 2014 IEEE World Forum on}, \n title={Improve the sustainability of Internet of Things through trading-based value creation}, \n year={2014}, \n pages={135-140}, \n abstract={Internet of Things (IoT) has been widely discussed over the past few years in technology point of view. However, the social aspects of IoT are seldom studied to date. In this paper, we discuss the IoT in social point of view. Specifically, we examine the strategies to increase the adoption of IoT in a sustainable manner. Such discussion is essential in today's context where adoption of IoT solutions by non-technical community is slow. Specially, large number of IoT solutions making their way into the market every day. We propose an trading-based value creation model based on sensing as a service paradigm in order to fuel the adoption of IoT. We discuss the value creation and its impact towards the society especially to households and their occupants. We also present results of two different surveys we conducted in order to examine the potential acceptance of the proposed model among the general public.}, \n keywords={Sensing as a Service},\n doi={10.1109/WF-IoT.2014.6803135}, \n month={March},\n url = {https://arxiv.org/pdf/1401.6720.pdf}, \n  url_Slides =   {slides/C012.pdf},\n}\n\n\n%=============C011============\n
\n
\n\n\n
\n Internet of Things (IoT) has been widely discussed over the past few years in technology point of view. However, the social aspects of IoT are seldom studied to date. In this paper, we discuss the IoT in social point of view. Specifically, we examine the strategies to increase the adoption of IoT in a sustainable manner. Such discussion is essential in today's context where adoption of IoT solutions by non-technical community is slow. Specially, large number of IoT solutions making their way into the market every day. We propose an trading-based value creation model based on sensing as a service paradigm in order to fuel the adoption of IoT. We discuss the value creation and its impact towards the society especially to households and their occupants. We also present results of two different surveys we conducted in order to examine the potential acceptance of the proposed model among the general public.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Sensor discovery and configuration framework for the Internet of Things paradigm.\n \n \n \n \n\n\n \n Perera, C.; Jayaraman, P. P.; Zaslavsky, A.; Georgakopoulos, D.; and Christen, P.\n\n\n \n\n\n\n In Internet of Things (WF-IoT), 2014 IEEE World Forum on, pages 94-99, March 2014. \n \n\n\n\n
\n\n\n\n \n \n \"SensorPaper\n  \n \n \n \"Sensor slides\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@INPROCEEDINGS{C011, \n author={Charith Perera and  Prem Prakash Jayaraman and Arkady Zaslavsky and Dimitrios Georgakopoulos and Peter Christen}, \n booktitle={Internet of Things (WF-IoT), 2014 IEEE World Forum on}, \n title={Sensor discovery and configuration framework for the Internet of Things paradigm}, \n year={2014}, \n pages={94-99}, \n abstract={Internet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. The data generated by the Internet of Things are valuable and have the potential to drive innovative and novel applications. The data streams coming from these devices will challenge the traditional approaches to data management and contribute to the emerging paradigm of big data. One of the most challenging tasks before collecting and processing data from these devices (e.g. sensors) is discovering and configuring the sensors and the associated data streams. In this paper, we propose a tool called SmartLink that can be used to discover and configure sensors. Specifically, SmartLink, is capable of discovering sensors deployed in a particular location despite their heterogeneity (e.g. different communication protocols, communication sequences, capabilities). SmartLink establishes the direct communication between the sensor hardware and cloud-based IoT middleware. We address the challenge of heterogeneity using a plugin architecture. Our prototype tool is developed on the Android platform. We evaluate the significance of our approach by discovering and configuring 52 different types of Libelium sensors.}, \n keywords={Internet of Things Architectures, Fog Computing, Sensing as a Service},\n keywords={Search and Discovery}, \n doi = {10.1109/WF-IoT.2014.6803127}, \n month = {March},\n url = {https://arxiv.org/pdf/1312.6721.pdf},\n url_Slides =   {slides/C011.pdf},\n }\n\n\n%=============C010============\n
\n
\n\n\n
\n Internet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. The data generated by the Internet of Things are valuable and have the potential to drive innovative and novel applications. The data streams coming from these devices will challenge the traditional approaches to data management and contribute to the emerging paradigm of big data. One of the most challenging tasks before collecting and processing data from these devices (e.g. sensors) is discovering and configuring the sensors and the associated data streams. In this paper, we propose a tool called SmartLink that can be used to discover and configure sensors. Specifically, SmartLink, is capable of discovering sensors deployed in a particular location despite their heterogeneity (e.g. different communication protocols, communication sequences, capabilities). SmartLink establishes the direct communication between the sensor hardware and cloud-based IoT middleware. We address the challenge of heterogeneity using a plugin architecture. Our prototype tool is developed on the Android platform. We evaluate the significance of our approach by discovering and configuring 52 different types of Libelium sensors.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n MOSDEN: An Internet of Things Middleware for Resource Constrained Mobile Devices.\n \n \n \n \n\n\n \n Perera, C.; Jayaraman, P. P.; Zaslavsky, A.; Georgakopoulos, D.; and Christen, P.\n\n\n \n\n\n\n In System Sciences (HICSS), 2014 47th Hawaii International Conference on, pages 1053-1062, Jan 2014. \n \n\n\n\n
\n\n\n\n \n \n \"MOSDEN:Paper\n  \n \n \n \"MOSDEN: slides\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n
@INPROCEEDINGS{C008, \n author = {Perera, Charith and Jayaraman, Prem Prakash and Zaslavsky, Arkady and Georgakopoulos, Dimitrios and Christen, Peter}, \n booktitle = {System Sciences (HICSS), 2014 47th Hawaii International Conference on}, \n title = {MOSDEN: An Internet of Things Middleware for Resource Constrained Mobile Devices}, \n year={2014}, \n month={Jan}, \n pages={1053-1062}, \n abstract={The Internet of Things (IoT) is part of Future Internet and will comprise many billions of Internet Connected Objects (ICO) or `things' where things can sense, communicate, compute and potentially actuate as well as have intelligence, multi-modal interfaces, physical/ virtual identities and attributes. Collecting data from these objects is an important task as it allows software systems to understand the environment better. Many different hardware devices may involve in the process of collecting and uploading sensor data to the cloud where complex processing can occur. Further, we cannot expect all these objects to be connected to the computers due to technical and economical reasons. Therefore, we should be able to utilize resource constrained devices to collect data from these ICOs. On the other hand, it is critical to process the collected sensor data before sending them to the cloud to make sure the sustainability of the infrastructure due to energy constraints. This requires to move the sensor data processing tasks towards the resource constrained computational devices (e.g. mobile phones). In this paper, we propose Mobile Sensor Data Processing Engine (MOSDEN), an plug-in-based IoT middleware for mobile devices, that allows to collect and process sensor data without programming efforts. Our architecture also supports sensing as a service model. We present the results of the evaluations that demonstrate its suitability towards real world deployments. Our proposed middleware is built on Android platform.}, \n keywords={Internet of Things Architectures, Fog Computing, Sensing as a Service},\n doi={10.1109/HICSS.2014.137},\n url = {https://arxiv.org/pdf/1310.4038.pdf},\n url_Slides =   {slides/C008.pdf},\n }\n\n%=============C007============\n
\n
\n\n\n
\n The Internet of Things (IoT) is part of Future Internet and will comprise many billions of Internet Connected Objects (ICO) or `things' where things can sense, communicate, compute and potentially actuate as well as have intelligence, multi-modal interfaces, physical/ virtual identities and attributes. Collecting data from these objects is an important task as it allows software systems to understand the environment better. Many different hardware devices may involve in the process of collecting and uploading sensor data to the cloud where complex processing can occur. Further, we cannot expect all these objects to be connected to the computers due to technical and economical reasons. Therefore, we should be able to utilize resource constrained devices to collect data from these ICOs. On the other hand, it is critical to process the collected sensor data before sending them to the cloud to make sure the sustainability of the infrastructure due to energy constraints. This requires to move the sensor data processing tasks towards the resource constrained computational devices (e.g. mobile phones). In this paper, we propose Mobile Sensor Data Processing Engine (MOSDEN), an plug-in-based IoT middleware for mobile devices, that allows to collect and process sensor data without programming efforts. Our architecture also supports sensing as a service model. We present the results of the evaluations that demonstrate its suitability towards real world deployments. Our proposed middleware is built on Android platform.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2013\n \n \n (5)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Efficient opportunistic sensing using mobile collaborative platform MOSDEN.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n In Collaborative Computing: Networking, Applications and Worksharing (Collaboratecom), 2013 9th International Conference Conference on, pages 77-86, Oct 2013. \n \n\n\n\n
\n\n\n\n \n \n \"EfficientPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n
\n
\n\n\n
\n Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of these applications take advantage of the plethora of sensors installed on the mobile device to deliver enhanced user experience. The sensors on the smartphone provide the opportunity to develop innovative mobile opportunistic sensing applications in many sectors including healthcare, environmental monitoring and transportation. In this paper, we present a collaborative mobile sensing framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on smartphones capturing and sharing sensed data between multiple distributed applications and users. MOSDEN follows a component-based design philosophy promoting reuse for easy and quick opportunistic sensing application deployments. MOSDEN separates the application-specific processing from the sensing, storing and sharing. MOSDEN is scalable and requires minimal development effort from the application developer. We have implemented our framework on Android-based mobile platforms and evaluate its performance to validate the feasibility and efficiency of MOSDEN to operate collaboratively in mobile opportunistic sensing applications. Experimental outcomes and lessons learnt conclude the paper.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Semantic-Driven Configuration of Internet of Things Middleware.\n \n \n \n \n\n\n \n Perera, C.; Zaslavsky, A.; Compton, M.; Christen, P.; and Georgakopoulos, D.\n\n\n \n\n\n\n In Semantics, Knowledge and Grids (SKG), 2013 Ninth International Conference on, pages 66-73, Oct 2013. \n \n\n\n\n
\n\n\n\n \n \n \"Semantic-DrivenPaper\n  \n \n \n \"Semantic-Driven slides\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n
@INPROCEEDINGS{C009, \n author={Perera, Charith and Zaslavsky, Arkady and Compton, Michael and Christen, Peter and Georgakopoulos, Dimitrios}, \n booktitle={Semantics, Knowledge and Grids (SKG), 2013 Ninth International Conference on}, \n title={Semantic-Driven Configuration of Internet of Things Middleware}, \n year={2013}, \n month={Oct}, \n pages={66-73}, \n abstract={We are currently observing emerging solutions to enable the Internet of Things (IoT). Efficient and feature rich IoT middeware platforms are key enablers for IoT. However, due to complexity, most of these middleware platforms are designed to be used by IT experts. In this paper, we propose a semantics-driven model that allows non-IT experts (e.g. plant scientist, city planner) to configure IoT middleware components easier and faster. Such tools allow them to retrieve the data they want without knowing the underlying technical details of the sensors and the data processing components. We propose a Context Aware Sensor Configuration Model (CASCoM) to address the challenge of automated context-aware configuration of filtering, fusion, and reasoning mechanisms in IoT middleware according to the problems at hand. We incorporate semantic technologies in solving the above challenges. We demonstrate the feasibility and the scalability of our approach through a prototype implementation based on an IoT middleware called Global Sensor Networks (GSN), though our model can be generalized into any other middleware platform. We evaluate CASCoM in agriculture domain and measure both performance in terms of usability and computational complexity.}, \n keywords={Internet of Things Architectures},\n keywords={Service Composition, Knowledge Representation, Search and Discovery},\n doi = {10.1109/SKG.2013.9},\n url = {https://arxiv.org/pdf/1309.1515.pdf},\n url_Slides =   {slides/C009.pdf},\n }\n\n%=============C008============\n
\n
\n\n\n
\n We are currently observing emerging solutions to enable the Internet of Things (IoT). Efficient and feature rich IoT middeware platforms are key enablers for IoT. However, due to complexity, most of these middleware platforms are designed to be used by IT experts. In this paper, we propose a semantics-driven model that allows non-IT experts (e.g. plant scientist, city planner) to configure IoT middleware components easier and faster. Such tools allow them to retrieve the data they want without knowing the underlying technical details of the sensors and the data processing components. We propose a Context Aware Sensor Configuration Model (CASCoM) to address the challenge of automated context-aware configuration of filtering, fusion, and reasoning mechanisms in IoT middleware according to the problems at hand. We incorporate semantic technologies in solving the above challenges. We demonstrate the feasibility and the scalability of our approach through a prototype implementation based on an IoT middleware called Global Sensor Networks (GSN), though our model can be generalized into any other middleware platform. We evaluate CASCoM in agriculture domain and measure both performance in terms of usability and computational complexity.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Context Aware Sensor Configuration Model for Internet of Things.\n \n \n \n \n\n\n \n Perera, C.; Zaslavsky, A.; Compton, M.; Christen, P.; and Georgakopoulos, D.\n\n\n \n\n\n\n In International Semantic Web Conference (Posters & Demos), pages 253-256, 2013. \n \n\n\n\n
\n\n\n\n \n \n \"ContextPaper\n  \n \n \n \"Context slides\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n
@inproceedings{C007,\n author    = {Charith Perera and Arkady Zaslavsky and Michael Compton and Peter Christen and Dimitrios Georgakopoulos},\n title     = {Context Aware Sensor Configuration Model for Internet of Things},\n booktitle = {International Semantic Web Conference (Posters {\\&} Demos)},\n year      = {2013},\n pages     = {253-256},\n url       = {http://ceur-ws.org/Vol-1035/iswc2013_poster_19.pdf},\n abstract  = {We propose a Context Aware Sensor Configuration Model (CASCoM) to address the challenge of automated context-aware configuration of filtering, fusion, and reasoning mechanisms in IoT middleware according to the problems at hand. We incorporate semantic technologies in solving the above challenges.},\n keywords={Internet of Things Architectures},\n keywords={Service Composition, Knowledge Representation, Search and Discovery},\n url_Slides =   {slides/C007.pdf},\n }\n\n%=============C006============\n
\n
\n\n\n
\n We propose a Context Aware Sensor Configuration Model (CASCoM) to address the challenge of automated context-aware configuration of filtering, fusion, and reasoning mechanisms in IoT middleware according to the problems at hand. We incorporate semantic technologies in solving the above challenges.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Context-Aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware.\n \n \n \n \n\n\n \n Perera, C.; Zaslavsky, A.; Christen, P.; Compton, M.; and Georgakopoulos, D.\n\n\n \n\n\n\n In Mobile Data Management (MDM), 2013 IEEE 14th International Conference on, volume 1, pages 314-322, 2013. \n \n\n\n\n
\n\n\n\n \n \n \"Context-AwarePaper\n  \n \n \n \"Context-Aware slides\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n
@INPROCEEDINGS{C006, \n author={Perera, Charith and Zaslavsky, Arkady and Christen, Peter and Compton, Michael and Georgakopoulos, Dimitrios}, \n booktitle={Mobile Data Management (MDM), 2013 IEEE 14th International Conference on}, \n title={Context-Aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware}, \n year={2013}, \n volume={1}, \n pages={314-322}, \n abstract={As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a substantial acceleration of the growth rate in the future. It is also evident that the increasing number of IoT middleware solutions are developed in both research and commercial environments. However, sensor search and selection remain a critical requirement and a challenge. In this paper, we present CASSARAM, a context-aware sensor search, selection, and ranking model for Internet of Things to address the research challenges of selecting sensors when large numbers of sensors with overlapping and sometimes redundant functionality are available. CASSARAM proposes the search and selection of sensors based on user priorities. CASSARAM considers a broad range of characteristics of sensors for search such as reliability, accuracy, battery life just to name a few. Our approach utilises both semantic querying and quantitative reasoning techniques. User priority based weighted Euclidean distance comparison in multidimensional space technique is used to index and rank sensors. Our objectives are to highlight the importance of sensor search in IoT paradigm, identify important characteristics of both sensors and data acquisition processes which help to select sensors, understand how semantic and statistical reasoning can be combined together to address this problem in an efficient manner. We developed a tool called CASSARA to evaluate the proposed model in terms of resource consumption and response time.}, \n keywords={Internet of Things Architectures, Sensing as a Service},\n keywords={Knowledge Representation, Search and Discovery},\n url = {https://arxiv.org/pdf/1303.2447.pdf},\n doi = {10.1109/MDM.2013.46},\n  url_Slides =   {slides/C006.pdf},\n }\n\n%=============C005============\n
\n
\n\n\n
\n As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a substantial acceleration of the growth rate in the future. It is also evident that the increasing number of IoT middleware solutions are developed in both research and commercial environments. However, sensor search and selection remain a critical requirement and a challenge. In this paper, we present CASSARAM, a context-aware sensor search, selection, and ranking model for Internet of Things to address the research challenges of selecting sensors when large numbers of sensors with overlapping and sometimes redundant functionality are available. CASSARAM proposes the search and selection of sensors based on user priorities. CASSARAM considers a broad range of characteristics of sensors for search such as reliability, accuracy, battery life just to name a few. Our approach utilises both semantic querying and quantitative reasoning techniques. User priority based weighted Euclidean distance comparison in multidimensional space technique is used to index and rank sensors. Our objectives are to highlight the importance of sensor search in IoT paradigm, identify important characteristics of both sensors and data acquisition processes which help to select sensors, understand how semantic and statistical reasoning can be combined together to address this problem in an efficient manner. We developed a tool called CASSARA to evaluate the proposed model in terms of resource consumption and response time.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Dynamic configuration of sensors using mobile sensor hub in internet of things paradigm.\n \n \n \n \n\n\n \n Perera, C.; Jayaraman, P.; Zaslavsky, A.; Christen, P.; and Georgakopoulos, D.\n\n\n \n\n\n\n In Intelligent Sensors, Sensor Networks and Information Processing, 2013 IEEE Eighth International Conference on, pages 473-478, 2013. \n \n\n\n\n
\n\n\n\n \n \n \"DynamicPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n
@INPROCEEDINGS{C005, \n author={Perera, Charith and Jayaraman, Peter and Zaslavsky, Arkady and Christen, Peter and Georgakopoulos, Dimitrios}, \n booktitle={Intelligent Sensors, Sensor Networks and Information Processing, 2013 IEEE Eighth International Conference on}, \n title={Dynamic configuration of sensors using mobile sensor hub in internet of things paradigm}, \n year={2013}, \n pages={473-478}, \n abstract={Internet of Things (IoT) envisions billions of sensors to be connected to the Internet. By deploying intelligent low-level computational devices such as mobile phones in-between sensors and cloud servers, we can reduce data communication with the use of intelligent processing such as fusing and filtering sensor data, which saves significant amount of energy. This is also ideal for real world sensor deployments where connecting sensors directly to a computer or to the Internet is not practical. Most of the leading IoT middleware solutions require manual and labour intensive tasks to be completed in order to connect a mobile phone to them. In this paper we present a mobile application called Mobile Sensor Hub (MoSHub). It allows variety of different sensors to be connected to a mobile phone and send the data to the cloud intelligently reducing network communication. Specifically, we explore techniques that allow MoSHub to be connected to cloud based IoT middleware solutions autonomously. For our experiments, we employed Global Sensor Network (GSN) middleware to implement and evaluate our approach. Such automated configuration reduces significant amount of manual labour that need to be performed by technical experts otherwise. We also evaluated different methods that can be used to automate the configuration process.}, \n keywords={Internet of Things Architectures, Fog Computing},\n doi = {10.1109/ISSNIP.2013.6529836},\n url = {https://arxiv.org/pdf/1302.1131.pdf}\n}\n\n%=============C004============\n
\n
\n\n\n
\n Internet of Things (IoT) envisions billions of sensors to be connected to the Internet. By deploying intelligent low-level computational devices such as mobile phones in-between sensors and cloud servers, we can reduce data communication with the use of intelligent processing such as fusing and filtering sensor data, which saves significant amount of energy. This is also ideal for real world sensor deployments where connecting sensors directly to a computer or to the Internet is not practical. Most of the leading IoT middleware solutions require manual and labour intensive tasks to be completed in order to connect a mobile phone to them. In this paper we present a mobile application called Mobile Sensor Hub (MoSHub). It allows variety of different sensors to be connected to a mobile phone and send the data to the cloud intelligently reducing network communication. Specifically, we explore techniques that allow MoSHub to be connected to cloud based IoT middleware solutions autonomously. For our experiments, we employed Global Sensor Network (GSN) middleware to implement and evaluate our approach. Such automated configuration reduces significant amount of manual labour that need to be performed by technical experts otherwise. We also evaluated different methods that can be used to automate the configuration process.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2012\n \n \n (4)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n CA4IOT: Context Awareness for Internet of Things.\n \n \n \n \n\n\n \n Perera, C.; Zaslavsky, A.; Christen, P.; and Georgakopoulos, D.\n\n\n \n\n\n\n In Green Computing and Communications (GreenCom), 2012 IEEE International Conference on, pages 775-782, 2012. \n \n\n\n\n
\n\n\n\n \n \n \"CA4IOT:Paper\n  \n \n \n \"CA4IOT: slides\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@INPROCEEDINGS{C004, \n author={Perera, Charith and Zaslavsky, Arkady and Christen, Peter and Georgakopoulos, Dimitrios}, \n booktitle={Green Computing and Communications (GreenCom), 2012 IEEE International Conference on}, \n title={CA4IOT: Context Awareness for Internet of Things}, \n year={2012}, \n pages={775-782}, \n abstract={Internet of Things (IoT) will connect billions of sensors deployed around the world together. This will create an ideal opportunity to build a sensing-as-a-service platform. Due to large number of sensor deployments, there would be number of sensors that can be used to sense and collect similar information. Further, due to advances in sensor hardware technology, new methods and measurements will be introduced continuously. In the IoT paradigm, selecting the most appropriate sensors which can provide relevant sensor data to address the problems at hand among billions of possibilities would be a challenge for both technical and non-technical users. In this paper, we propose the Context Awareness for Internet of Things (CA4IOT) architecture to help users by automating the task of selecting the sensors according to the problems/tasks at hand. We focus on automated configuration of filtering, fusion and reasoning mechanisms that can be applied to the collected sensor data streams using selected sensors. Our objective is to allow the users to submit their problems, so our proposed architecture understands them and produces more comprehensive and meaningful information than the raw sensor data streams generated by individual sensors.}, \n keywords={Internet of Things Architectures},\n doi = {10.1109/GreenCom.2012.128},\n url = {https://arxiv.org/pdf/1301.1084.pdf},\n  url_Slides =   {slides/C004.pdf},\n }\n\n%=============C003============\n
\n
\n\n\n
\n Internet of Things (IoT) will connect billions of sensors deployed around the world together. This will create an ideal opportunity to build a sensing-as-a-service platform. Due to large number of sensor deployments, there would be number of sensors that can be used to sense and collect similar information. Further, due to advances in sensor hardware technology, new methods and measurements will be introduced continuously. In the IoT paradigm, selecting the most appropriate sensors which can provide relevant sensor data to address the problems at hand among billions of possibilities would be a challenge for both technical and non-technical users. In this paper, we propose the Context Awareness for Internet of Things (CA4IOT) architecture to help users by automating the task of selecting the sensors according to the problems/tasks at hand. We focus on automated configuration of filtering, fusion and reasoning mechanisms that can be applied to the collected sensor data streams using selected sensors. Our objective is to allow the users to submit their problems, so our proposed architecture understands them and produces more comprehensive and meaningful information than the raw sensor data streams generated by individual sensors.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Sensing as a Service and Big Data.\n \n \n \n \n\n\n \n Zaslavsky, A.; Perera, C.; and Georgakopoulos, D.\n\n\n \n\n\n\n July 2012.\n \n\n\n\n
\n\n\n\n \n \n \"SensingPaper\n  \n \n \n \"Sensing slides\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n
@CONFERENCE{C003,\n author = {Arkady Zaslavsky and Charith Perera and Dimitrios Georgakopoulos},\n title = {Sensing as a Service and Big Data},\n booktitle = {International Conference on Advances in Cloud Computing (ACC-2012)},\n year = {2012},\n pages = {21-29},\n address = {Bangalore, India},\n keywords={Internet of Things Architectures, Sensing as a Service},\n month = {July},\n url = {https://arxiv.org/ftp/arxiv/papers/1301/1301.0159.pdf}, \n abstract = {Internet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. Data streams coming from these devices will challenge the traditional approaches to data management and contribute to the emerging paradigm of big data. This paper discusses emerging Internet of Things (IoT) architecture, large scale sensor network applications, federating sensor networks, sensor data and related context capturing techniques, challenges in cloud-based management, storing, archiving and processing of sensor data.},\n url_Slides =   {slides/C003.pdf},\n }\n\n%=============C002============\n
\n
\n\n\n
\n Internet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. Data streams coming from these devices will challenge the traditional approaches to data management and contribute to the emerging paradigm of big data. This paper discusses emerging Internet of Things (IoT) architecture, large scale sensor network applications, federating sensor networks, sensor data and related context capturing techniques, challenges in cloud-based management, storing, archiving and processing of sensor data.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Connecting Mobile Things to Global Sensor Network Middleware Using System-generated Wrappers.\n \n \n \n \n\n\n \n Perera, C.; Zaslavsky, A.; Christen, P.; Salehi, A.; and Georgakopoulos, D.\n\n\n \n\n\n\n In Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access, of MobiDE '12, pages 23–30, New York, NY, USA, 2012. ACM\n \n\n\n\n
\n\n\n\n \n \n \"ConnectingPaper\n  \n \n \n \"Connecting slides\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n
@inproceedings{C002,\n author = {Perera, Charith and Zaslavsky, Arkady and Christen, Peter and Salehi, Ali and Georgakopoulos, Dimitrios},\n title = {Connecting Mobile Things to Global Sensor Network Middleware Using System-generated Wrappers},\n booktitle = {Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access},\n series    = {MobiDE '12},\n year      = {2012},\n isbn      = {978-1-4503-1442-8},\n location  = {Scottsdale, Arizona},\n pages     = {23--30},\n numpages  = {8},\n url       = {https://arxiv.org/pdf/1301.1085.pdf},\n doi       = {10.1145/2258056.2258062},\n acmid     = {2258062},\n publisher = {ACM},\n address   = {New York, NY, USA},\n keywords  = {Internet of Things Architectures, Fog Computing},\n abstract  = {Internet of Things (IoT) will create a cyberphysical world where all the things around us are connected to the Inter net, sense and produce 'big data' that has to be stored, processed and communicated with minimum human intervention. With the ever increasing emergence of new sensors, interfaces and mobile devices, the grand challenge is to keep up with this race in developing software drivers and wrappers for IoT things. In this paper, we examine the approaches that automate the process of developing middleware drivers/wrappers for the IoT things. We propose ASCM4GSN architecture to address this challenge efficiently and effectively. We demonstrate the proposed approach using Global Sensor Network (GSN) middleware which exemplifies a cluster of data streaming engines. The ASCM4GSN architecture significantly speeds up the wrapper development and sensor configuration process as demonstrated for Android mobile phone based sensors as well as for Sun SPOT sensors.},\n url_Slides =   {slides/C002.pdf},\n }\n\n%=============C001============\n
\n
\n\n\n
\n Internet of Things (IoT) will create a cyberphysical world where all the things around us are connected to the Inter net, sense and produce 'big data' that has to be stored, processed and communicated with minimum human intervention. With the ever increasing emergence of new sensors, interfaces and mobile devices, the grand challenge is to keep up with this race in developing software drivers and wrappers for IoT things. In this paper, we examine the approaches that automate the process of developing middleware drivers/wrappers for the IoT things. We propose ASCM4GSN architecture to address this challenge efficiently and effectively. We demonstrate the proposed approach using Global Sensor Network (GSN) middleware which exemplifies a cluster of data streaming engines. The ASCM4GSN architecture significantly speeds up the wrapper development and sensor configuration process as demonstrated for Android mobile phone based sensors as well as for Sun SPOT sensors.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Capturing sensor data from mobile phones using Global Sensor Network middleware.\n \n \n \n \n\n\n \n Perera, C.; Zaslavsky, A.; Christen, P.; Salehi, A.; and Georgakopoulos, D.\n\n\n \n\n\n\n In Personal Indoor and Mobile Radio Communications (PIMRC), IEEE 23rd International Symposium on, pages 24-29, 2012. \n \n\n\n\n
\n\n\n\n \n \n \"CapturingPaper\n  \n \n \n \"Capturing slides\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n
@INPROCEEDINGS{C001, \n author={Perera, Charith and Zaslavsky, Arkady and Christen, Peter and Salehi, Ali and Georgakopoulos, Dimitrios}, \n booktitle={Personal Indoor and Mobile Radio Communications (PIMRC), IEEE 23rd International Symposium on}, \n title={Capturing sensor data from mobile phones using Global Sensor Network middleware}, \n year={2012}, \n pages={24-29}, \n abstract={Mobile phones play increasingly bigger role in our everyday lives. Today, most smart phones comprise a wide variety of sensors which can sense the physical environment. The Internet of Things vision encompasses participatory sensing which is enabled using mobile phones based sensing and reasoning. In this research, we propose and demonstrate our DAM4GSN architecture to capture sensor data using sensors built into the mobile phones. Specifically, we combine an open source sensor data stream processing engine called `Global Sensor Network (GSN)' with the Android platform to capture sensor data. To achieve this goal, we proposed and developed a prototype application that can be installed on Android devices as well as a AndroidWrapper as a GSN middleware component. The process and the difficulty of manually connecting sensor devices to sensor data processing middleware systems are examined. We evaluated the performance of the system based on power consumption of the mobile client.}, \n keywords={Internet of Things Architectures, Fog Computing},\n ISSN = {2166-9570},\n url  = {https://arxiv.org/pdf/1301.0157.pdf},\n doi  = {10.1109/PIMRC.2012.6362778},\n url_Slides =   {slides/C001.pdf},\n}
\n
\n\n\n
\n Mobile phones play increasingly bigger role in our everyday lives. Today, most smart phones comprise a wide variety of sensors which can sense the physical environment. The Internet of Things vision encompasses participatory sensing which is enabled using mobile phones based sensing and reasoning. In this research, we propose and demonstrate our DAM4GSN architecture to capture sensor data using sensors built into the mobile phones. Specifically, we combine an open source sensor data stream processing engine called `Global Sensor Network (GSN)' with the Android platform to capture sensor data. To achieve this goal, we proposed and developed a prototype application that can be installed on Android devices as well as a AndroidWrapper as a GSN middleware component. The process and the difficulty of manually connecting sensor devices to sensor data processing middleware systems are examined. We evaluated the performance of the system based on power consumption of the mobile client.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n\n\n \n\n \n \n \n \n\n
\n"}; document.write(bibbase_data.data);