EAI Endorsed Transactions on Context-aware Systems and Applications 15(5): e3

Research Article

Infrastructure-less Occupancy Detection and Semantic Localization in Smart Environments

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  • @ARTICLE{10.4108/eai.22-7-2015.2260062,
        author={Md Abdullah Al Hafiz Khan and H M Sajjad Hossain and Nirmalya Roy},
        title={Infrastructure-less Occupancy Detection and Semantic Localization in Smart Environments},
        journal={EAI Endorsed Transactions on Context-aware Systems and Applications},
        keywords={crowdsourcing, opportunistic sensing, occupancy counting, semantic localization},
  • Md Abdullah Al Hafiz Khan
    H M Sajjad Hossain
    Nirmalya Roy
    Year: 2015
    Infrastructure-less Occupancy Detection and Semantic Localization in Smart Environments
    DOI: 10.4108/eai.22-7-2015.2260062
Md Abdullah Al Hafiz Khan1,*, H M Sajjad Hossain1, Nirmalya Roy1
  • 1: UMBC
*Contact email: mdkhan1@umbc.edu


Accurate estimation of localized occupancy related informa- tion in real time enables a broad range of intelligent smart environment applications. A large number of studies using heterogeneous sensor arrays reflect the myriad requirements of various emerging pervasive, ubiquitous and participatory sensing applications. In this paper, we introduce a zero- configuration and infrastructure-less smartphone based lo- cation specific occupancy estimation model. We opportunis- tically exploit smartphone’s acoustic sensors in a conversing environment and motion sensors in absence of any conver- sational data. We demonstrate a novel speaker estimation algorithm based on unsupervised clustering of overlapped and non-overlapped conversational data and a change point detection algorithm for locomotive motion of the users to infer the occupancy. We augment our occupancy detection model with a fingerprinting based methodology using smart- phone’s magnetometer sensor to accurately assimilate loca- tion information of any gathering. We postulate a novel crowdsourcing-based approach to annotate the semantic lo- cation of the occupancy. We evaluate our algorithms in dif- ferent contexts; conversational, silence and mixed in pres- ence of 10 domestic users. Our experimental results on real-life data traces in natural settings show that using this hybrid approach, we can achieve approximately 0.76 error count distance for occupancy detection accuracy on aver- age.