In this paper, we propose a locally optimum detection (LOD) scheme for detecting a weak radioactive source buried in background clutter. We develop a decentralized algorithm, based on alternating direction method of multipliers (ADMM), for implementing the proposed scheme in autonomous sensor networks. Results show that algorithm performance approaches the centralized clairvoyant detection algorithm in the low SNR regime, and exhibits excellent convergence rate and scaling behavior (w.r.t. number of nodes). We also devise a low-overhead, robust ADMM algorithm for Byzantine-resilient detection, and demonstrate its robustness to data falsification attacks.
@article{arxiv.1803.01221,
title = {Byzantine-Resilient Locally Optimum Detection Using Collaborative Autonomous Networks},
author = {Bhavya Kailkhura and Priyadip Ray and Deepak Rajan and Anton Yen and Peter Barnes and Ryan Goldhahn},
journal= {arXiv preprint arXiv:1803.01221},
year = {2018}
}
Comments
Proceedings of the 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2017), 10.-13. December 2017, Curacao, Dutch Antilles