Target Detection and Localization Methods using Compartmental Model for Internet of Things
Abstract
This paper analyses the performance of target detection and localization methods in heterogeneous sensor networks using compartmental model, which is an attenuation model expressing the variation of received signal strength (RSS) with propagation distance. First, we compute the threshold for the proposed target detection scheme, based on the decision fusion of different sensors and without requiring a priori probability. We also derive the bound on the threshold and subsequently the lower and upper bounds on the detection and false-alarm probabilities. Next, the location of the detected target is estimated using iterative mini-batch Singular Value Decomposition (SVD) methods in the presence of sensor location uncertainty. We highlight that the method for localization has low computational complexity which is suitable for Internet of Things (IoT) networks. The effectiveness of the compartmental model is demonstrated using both simulation study and real experiments. The model parameters are estimated using WiFi signal strength received on the mobile phones from the access points in an indoor environment.
Recommended Citation
S. Kumar and S. K. Das, "Target Detection and Localization Methods using Compartmental Model for Internet of Things," IEEE Transactions on Mobile Computing, vol. 19, no. 9, pp. 2234 - 2249, Institute of Electrical and Electronics Engineers (IEEE), Sep 2020.
The definitive version is available at https://doi.org/10.1109/TMC.2019.2921537
Department(s)
Computer Science
Research Center/Lab(s)
Center for High Performance Computing Research
Second Research Center/Lab
Intelligent Systems Center
Keywords and Phrases
decision fusion; Internet of Things; localization; Target detection
International Standard Serial Number (ISSN)
1536-1233; 1558-0660
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Sep 2020
Comments
The work of S. K. Das is partially supported by NSF grants under award numbers CNS-1818942, CCF-1725755, CNS-1545037, and CNS-1545050.