@Article{J005,
 author   = {Ozgur Yurur and Chi Harold Liu and Charith Perera and Min Chen and Xue Liu and Wilfrido Moreno},
 title    = {Energy-Efficient and Context-Aware Smartphone Sensor Employment},
 journal  = {IEEE Transactions on Vehicular Technology},
 year     = {2015},
 volume   = {64},
 number   = {9},
 pages    = {4230-4244},
 month    = {Sept},
 issn     = {0018-9545},
 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.},
 doi      = {10.1109/TVT.2014.2364619},
 keywords={Fog Computing},
}

%=============J004============

{"_id":"GbobEyam9Wz3nL4y9","bibbaseid":"yurur-liu-perera-chen-liu-moreno-energyefficientandcontextawaresmartphonesensoremployment-2015","downloads":0,"creationDate":"2017-08-06T23:58:04.310Z","title":"Energy-Efficient and Context-Aware Smartphone Sensor Employment","author_short":["Yurur, O.","Liu, C. H.","Perera, C.","Chen, M.","Liu, X.","Moreno, W."],"year":2015,"bibtype":"article","biburl":"https://ngcharithperera.github.io/bibbase.github.io/finalize.bib","bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["Ozgur"],"propositions":[],"lastnames":["Yurur"],"suffixes":[]},{"firstnames":["Chi","Harold"],"propositions":[],"lastnames":["Liu"],"suffixes":[]},{"firstnames":["Charith"],"propositions":[],"lastnames":["Perera"],"suffixes":[]},{"firstnames":["Min"],"propositions":[],"lastnames":["Chen"],"suffixes":[]},{"firstnames":["Xue"],"propositions":[],"lastnames":["Liu"],"suffixes":[]},{"firstnames":["Wilfrido"],"propositions":[],"lastnames":["Moreno"],"suffixes":[]}],"title":"Energy-Efficient and Context-Aware Smartphone Sensor Employment","journal":"IEEE Transactions on Vehicular Technology","year":"2015","volume":"64","number":"9","pages":"4230-4244","month":"Sept","issn":"0018-9545","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.","doi":"10.1109/TVT.2014.2364619","keywords":"Fog Computing","bibtex":"@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","author_short":["Yurur, O.","Liu, C. H.","Perera, C.","Chen, M.","Liu, X.","Moreno, W."],"key":"J005","id":"J005","bibbaseid":"yurur-liu-perera-chen-liu-moreno-energyefficientandcontextawaresmartphonesensoremployment-2015","role":"author","urls":{},"keyword":["Fog Computing"],"metadata":{"authorlinks":{"perera, c":"https://ngcharithperera.github.io/"}}},"search_terms":["energy","efficient","context","aware","smartphone","sensor","employment","yurur","liu","perera","chen","liu","moreno"],"keywords":["fog computing"],"authorIDs":["kCgNaG76MMeuNhePE"],"dataSources":["XdPhS7pyvbbn7G67t"]}