Target Detection and Localization Methods using Compartmental Model for Internet of Things


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.


Computer Science

Research Center/Lab(s)

Center for High Performance Computing Research

Second Research Center/Lab

Intelligent Systems Center


The work of S. K. Das is partially supported by NSF grants under award numbers CNS-1818942, CCF-1725755, CNS-1545037, and CNS-1545050.

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


File Type





© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Sep 2020