Unsupervised Topology Assessment in Smart Homes
Authors: 
Avirup Mukherjee, Madhumita Mallick, Sandip Chakraborty, Niloy GangulyAuthors Info & Claims
Pages 193 - 197
Abstract
Nowadays, a wide range of IOT devices are deployed in a variety of environments and settings to enhance the quality of human life. With a huge amount of data being generated from them, privacy is becoming a very big concern. To determine the level of privacy breach that can be achieved, we introduce in this paper, an unsupervised approach to visualize the sensor network, which in turn divulges the indoor topology of a smart home. The results are obtained from a smart environment by conducting a series of deductions and analysis on sensor datasets generated by a smart home. The experimental results demonstrate that our approach is able to deduce room-level sensor topology for a smart home even without the knowledge of any activity label or any prior information about the environment.
References
[1]
Alaa E Abdel Hakim and Wael Deabes. 2019. Can People Really Do Nothing? Handling Annotation Gaps in ADL Sensor Data. Algorithms 12, 10 (2019), 217.
[2]
Mohammad Arif Ul Alam and Nirmalya Roy. 2017. Unseen activity recognitions: A hierarchical active transfer learning approach. In IEEE ICDCS. IEEE, 436–446.
[3]
Moustafa Alzantot and Moustafa Youssef. 2012. Crowdinside: automatic construction of indoor floorplans. In ACM SIGSPATIAL. 99–108.
[4]
Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment 2008, 10(2008), P10008.
[5]
Diane J Cook. 2010. Learning setting-generalized activity models for smart spaces. IEEE intelligent systems 2010, 99 (2010), 1.
[6]
Diane J Cook and Maureen Schmitter-Edgecombe. 2009. Assessing the quality of activities in a smart environment. Methods of information in medicine 48, 05 (2009), 480–485.
[7]
Basma M Mohammad El-Basioni, Sherine Mohamed Abd El-Kader, and Hussein S Eissa. 2014. Independent living for persons with disabilities and elderly people using smart home technology. International Journal of Application or Innovation in Engineering and Management 3, 4 (2014), 11–28.
[8]
Michelle Girvan and Mark EJ Newman. 2002. Community structure in social and biological networks. Proceedings of the national academy of sciences 99, 12 (2002), 7821–7826.
[9]
Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In ACM SIGKDD. 855–864.
[10]
John Hopcroft and Robert Tarjan. 1973. Algorithm 447: efficient algorithms for graph manipulation. Commun. ACM 16, 6 (1973), 372–378.
[11]
HM Sajjad Hossain and Nirmalya Roy. 2018. SocialAnnotator: Annotator Selection Using Activity and Social Context. In Proceedings of the AAAI Fall Symposium on Reasoning and Learning in Real-World Systems for Long-Term Autonomy. 30–37.
[12]
Danai Koutra, Ankur Parikh, Aaditya Ramdas, and Jing Xiang. 2011. Algorithms for graph similarity and subgraph matching. In Proc. Ecol. Inference Conf, Vol. 17.
[13]
Suk Lee, Kyoung Nam Ha, and Kyung Chang Lee. 2006. A pyroelectric infrared sensor-based indoor location-aware system for the smart home. IEEE Trans. on Consumer Elec. 52, 4 (2006), 1311–1317.
[14]
Jiakang Lu, Yamina Taskin Shams, and Kamin Whitehouse. 2014. Smart blueprints: how simple sensors can collaboratively map out their own locations in the home. ACM Tran. on Sensor Netw. 11, 1 (2014), 1–23.
[15]
Joseph Rafferty, Chris D Nugent, Jun Liu, and Liming Chen. 2017. From activity recognition to intention recognition for assisted living within smart homes. IEEE Trans. on Human-Machine Sys. 47, 3 (2017), 368–379.
[16]
Vijay Srinivasan, John Stankovic, and Kamin Whitehouse. 2008. Protecting your daily in-home activity information from a wireless snooping attack. In ACM UbiComp. 202–211.
[17]
Niall Twomey, Tom Diethe, Ian Craddock, and Peter Flach. 2017. Unsupervised learning of sensor topologies for improving activity recognition in smart environments. Neurocomputing 234(2017), 93–106.
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Published In
January 2021
453 pages
ISBN:9781450388177
DOI:10.1145/3430984
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Published: 02 January 2021
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CODS COMAD 2021
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