Multiple Source Localization in IoT: A Conditional GAN and Image-Processing-Based Framework

This article addresses the problem of multiple source localization (MSL) using the Internet of Things (IoT) sensors. MSL entails determining the locations of multiple unknown sources by fusing sensory data within a designated Area of Interest (AoI). Existing solutions suffer from limitations, such as increased algorithmic complexity, as the number of sources increases and degraded performance in sparse sensor placement scenarios. This article proposes a novel source-independent approach resilient to sparse sensor placements based on conditional generative adversarial networks (cGANs) and image processing-based peak finding with subpixel peak refinement to address the MSL problem. The proposed approach formulates the MSL problem into two subproblems: 1) image-to-image translation and 2) 2-D peak finding. The cGAN translates the raw measurement data to an intensity field through image-to-image translation. Then, a peak-finding algorithm based on persistent homology with subpixel peak refinement is applied to localize the unknown sources accurately. The proposed approach is tested through radioactive source localization experiments, benchmark comparisons, and adaptability evaluation in unseen environments.