research-article

Sensing by proxy in buildings with agglomerative clustering of indoor temperature movements

Authors:

Tegawendé F. Bissyandé,

Yves Le TraonAuthors Info & Claims

SAC '17: Proceedings of the Symposium on Applied Computing

Pages 477 - 484

https://doi.org/10.1145/3019612.3019699

Published: 03 April 2017 Publication History

Abstract

As the concept of Internet of Things (IoT) develops, buildings are equipped with increasingly heterogeneous sensors to track building status as well as occupant activities. As users become more and more concerned with their privacy in buildings, explicit sensing techniques can lead to uncomfortableness and resistance from occupants. In this paper, we adapt a sensing by proxy paradigm that monitors building status and coarse occupant activities through agglomerative clustering of indoor temperature movements. Through extensive experimentation on 86 classrooms, offices and labs in a five-story school building in western Europe, we prove that indoor temperature movements can be leveraged to infer latent information about indoor environments, especially about rooms' relative physical locations and rough type of occupant activities. Our results evidence a cost-effective approach to extending commercial building control systems and gaining extra relevant intelligence from such systems.

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Cited By

Bajaber F(2023)Enhancing Subcluster Identification in IoT Sensor Networks with Hierarchical Clustering Algorithms and Dendrograms2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS)10.1109/ICSECS58457.2023.10256413(459-464)Online publication date: 25-Aug-2023
https://doi.org/10.1109/ICSECS58457.2023.10256413
Yoshida MKleisarchaki SGtirgen LNishi H(2018)Indoor Occupancy Estimation via Location-Aware HMM: An IoT Approach2018 IEEE 19th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)10.1109/WoWMoM.2018.8449765(14-19)Online publication date: Jun-2018
https://doi.org/10.1109/WoWMoM.2018.8449765

Index Terms

Sensing by proxy in buildings with agglomerative clustering of indoor temperature movements
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensor networks
2. Information systems
  1. Information systems applications
    1. Data mining
      1. Clustering

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cover image ACM Conferences

SAC '17: Proceedings of the Symposium on Applied Computing

April 2017

2004 pages

ISBN:9781450344869

DOI:10.1145/3019612

Conference Chair:
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Maria Lencastre
University of Pernambuco, Brazil

Copyright © 2017 ACM.

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Published: 03 April 2017

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April 3 - 7, 2017

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Bajaber F(2023)Enhancing Subcluster Identification in IoT Sensor Networks with Hierarchical Clustering Algorithms and Dendrograms2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS)10.1109/ICSECS58457.2023.10256413(459-464)Online publication date: 25-Aug-2023
https://doi.org/10.1109/ICSECS58457.2023.10256413
Yoshida MKleisarchaki SGtirgen LNishi H(2018)Indoor Occupancy Estimation via Location-Aware HMM: An IoT Approach2018 IEEE 19th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)10.1109/WoWMoM.2018.8449765(14-19)Online publication date: Jun-2018
https://doi.org/10.1109/WoWMoM.2018.8449765

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