About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Research Article

Infrastructure-less Occupancy Detection and Semantic Localization in Smart Environments

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{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},
        proceedings={12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services},
        publisher={ACM},
        proceedings_a={MOBIQUITOUS},
        year={2015},
        month={8},
        keywords={crowdsourcing opportunistic sensing occupancy counting semantic localization},
        doi={10.4108/eai.22-7-2015.2260062}
    }
    
  • Md Abdullah Al Hafiz Khan
    H M Sajjad Hossain
    Nirmalya Roy
    Year: 2015
    Infrastructure-less Occupancy Detection and Semantic Localization in Smart Environments
    MOBIQUITOUS
    ICST
    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

Abstract

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.

Keywords
crowdsourcing opportunistic sensing occupancy counting semantic localization
Published
2015-08-11
Publisher
ACM
http://dx.doi.org/10.4108/eai.22-7-2015.2260062
Copyright © 2015–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL