@ARTICLE{2017_1J_winips,
  author={H. {Zou} and M. {Jin} and H. {Jiang} and L. {Xie} and C. J. {Spanos}},
  journal={IEEE Transactions on Wireless Communications}, 
  title={WinIPS: WiFi-Based Non-Intrusive Indoor Positioning System With Online Radio Map Construction and Adaptation}, 
  year={2017},
  volume={16},
  number={12},
  pages={8118-8130},
  doi={10.1109/TWC.2017.2757472},
  abstract={WiFi fingerprinting-based indoor positioning system (IPS) has become the most promising solution for indoor localization. However, there are two major drawbacks that hamper its large-scale implementation. First, an offline site survey process is required which is extremely time-consuming and labor-intensive. Second, the RSS fingerprint database built offline is vulnerable to environmental dynamics. To address these issues comprehensively, in this paper, we propose WinIPS, a WiFi-based non-intrusive IPS that enables automatic online radio map construction and adaptation, aiming for calibration-free indoor localization. WinIPS can capture data packets transmitted in existing WiFi traffic and extract the RSS and MAC addresses of both WiFi access points (APs) and mobile devices in a non-intrusive manner. APs can be used as online reference points for radio map construction. A novel Gaussian process regression model is proposed to approximate the non-uniform RSS distribution of an indoor environment. Extensive experiments were conducted, which demonstrated that WinIPS outperforms existing solutions in terms of both RSS estimation accuracy and localization accuracy.},
  url_pdf = {WinIPS.pdf},
  url_link = {https://ieeexplore.ieee.org/document/8057286/versions},
  keywords = "Smart city, Data mining, Machine learning"}

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J."],"bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["H."],"propositions":[],"lastnames":["Zou"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Jin"],"suffixes":[]},{"firstnames":["H."],"propositions":[],"lastnames":["Jiang"],"suffixes":[]},{"firstnames":["L."],"propositions":[],"lastnames":["Xie"],"suffixes":[]},{"firstnames":["C.","J."],"propositions":[],"lastnames":["Spanos"],"suffixes":[]}],"journal":"IEEE Transactions on Wireless Communications","title":"WinIPS: WiFi-Based Non-Intrusive Indoor Positioning System With Online Radio Map Construction and Adaptation","year":"2017","volume":"16","number":"12","pages":"8118-8130","doi":"10.1109/TWC.2017.2757472","abstract":"WiFi fingerprinting-based indoor positioning system (IPS) has become the most promising solution for indoor localization. However, there are two major drawbacks that hamper its large-scale implementation. First, an offline site survey process is required which is extremely time-consuming and labor-intensive. Second, the RSS fingerprint database built offline is vulnerable to environmental dynamics. To address these issues comprehensively, in this paper, we propose WinIPS, a WiFi-based non-intrusive IPS that enables automatic online radio map construction and adaptation, aiming for calibration-free indoor localization. WinIPS can capture data packets transmitted in existing WiFi traffic and extract the RSS and MAC addresses of both WiFi access points (APs) and mobile devices in a non-intrusive manner. APs can be used as online reference points for radio map construction. A novel Gaussian process regression model is proposed to approximate the non-uniform RSS distribution of an indoor environment. Extensive experiments were conducted, which demonstrated that WinIPS outperforms existing solutions in terms of both RSS estimation accuracy and localization accuracy.","url_pdf":"WinIPS.pdf","url_link":"https://ieeexplore.ieee.org/document/8057286/versions","keywords":"Smart city, Data mining, Machine learning","bibtex":"@ARTICLE{2017_1J_winips,\n author={H. {Zou} and M. {Jin} and H. {Jiang} and L. {Xie} and C. J. {Spanos}},\n journal={IEEE Transactions on Wireless Communications}, \n title={WinIPS: WiFi-Based Non-Intrusive Indoor Positioning System With Online Radio Map Construction and Adaptation}, \n year={2017},\n volume={16},\n number={12},\n pages={8118-8130},\n doi={10.1109/TWC.2017.2757472},\n abstract={WiFi fingerprinting-based indoor positioning system (IPS) has become the most promising solution for indoor localization. However, there are two major drawbacks that hamper its large-scale implementation. First, an offline site survey process is required which is extremely time-consuming and labor-intensive. Second, the RSS fingerprint database built offline is vulnerable to environmental dynamics. To address these issues comprehensively, in this paper, we propose WinIPS, a WiFi-based non-intrusive IPS that enables automatic online radio map construction and adaptation, aiming for calibration-free indoor localization. WinIPS can capture data packets transmitted in existing WiFi traffic and extract the RSS and MAC addresses of both WiFi access points (APs) and mobile devices in a non-intrusive manner. APs can be used as online reference points for radio map construction. A novel Gaussian process regression model is proposed to approximate the non-uniform RSS distribution of an indoor environment. Extensive experiments were conducted, which demonstrated that WinIPS outperforms existing solutions in terms of both RSS estimation accuracy and localization accuracy.},\n url_pdf = {WinIPS.pdf},\n url_link = {https://ieeexplore.ieee.org/document/8057286/versions},\n keywords = \"Smart city, Data mining, Machine learning\"}\n\n\n\n","author_short":["Zou, H.","Jin, M.","Jiang, H.","Xie, L.","Spanos, C. J."],"key":"2017_1J_winips","id":"2017_1J_winips","bibbaseid":"zou-jin-jiang-xie-spanos-winipswifibasednonintrusiveindoorpositioningsystemwithonlineradiomapconstructionandadaptation-2017","role":"author","urls":{" pdf":"http://www.jinming.tech/papers/WinIPS.pdf"," link":"https://ieeexplore.ieee.org/document/8057286/versions"},"keyword":["Smart city","Data mining","Machine learning"],"metadata":{"authorlinks":{}},"downloads":1},"bibtype":"article","biburl":"http://www.jinming.tech/papers/myref.bib","dataSources":["sTzDHHaipTZWjp8oe","Y64tp2HnDCfXgLdc5"],"keywords":["smart city","data mining","machine learning"],"search_terms":["winips","wifi","based","non","intrusive","indoor","positioning","system","online","radio","map","construction","adaptation","zou","jin","jiang","xie","spanos"],"title":"WinIPS: WiFi-Based Non-Intrusive Indoor Positioning System With Online Radio Map Construction and Adaptation","year":2017,"downloads":1}