2021/05/22 by Mohammadreza Doostmohammadian, Themistoklis Charalambous, Doostmohammadian, Mohammadreza +7
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Distributed Sensor Networks and Detection Algorithms #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Multiagent Systems (cs.MA) #Opinion Dynamics and Social Influence #Random Matrices and Applications #Smart Grid Security and Resilience #Social and Information Networks (cs.SI) #Systems and Control (eess.SY) #electronic engineering #information engineering #stochastic dynamics and bifurcation
paper · pdf · doi:10.48550/arxiv.2105.10639
openalex publication_date 2021/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper considers distributed estimation of linear systems when the state\nobservations are corrupted with Gaussian noise of unbounded support and under\npossible random adversarial attacks. We consider sensors equipped with single\ntime-scale estimators and local chi-square (\χ2) detectors to\nsimultaneously opserve the states, share information, fuse the\nnoise/attack-corrupted data locally, and detect possible anomalies in their own\nobservations. While this scheme is applicable to a wide variety of systems\nassociated with full-rank (invertible) matrices, we discuss it within the\ncontext of distributed inference in social networks. The proposed technique\noutperforms existing results in the sense that: (i) we consider Gaussian noise\nwith no simplifying upper-bound assumption on the support; (ii) all existing\n\χ2-based techniques are centralized while our proposed technique is\ndistributed, where the sensors \locally detect attacks, with no central\ncoordinator, using specific probabilistic thresholds; and (iii) no\nlocal-observability assumption at a sensor is made, which makes our method\nfeasible for large-scale social networks. Moreover, we consider a Linear Matrix\nInequalities (LMI) approach to design block-diagonal gain (estimator) matrices\nunder appropriate constraints for isolating the attacks.\n