Joint Group Testing of Time-varying Faulty Sensors and System State Estimation in Large Sensor Networks

TL;DR

This paper proposes a joint group testing and Kalman filtering approach for real-time detection of sparse, time-varying faulty sensors in large networks, improving detection efficiency and system state estimation.

Contribution

It introduces a novel combined method for fault detection and state estimation that reduces testing efforts while maintaining high accuracy in large sensor networks.

Findings

Faulty sensors are efficiently detected and removed.

System state estimation performance is significantly improved.

The method reduces the number of tests compared to individual testing.

Abstract

The problem of faulty sensor detection is investigated in large sensor networks where the sensor faults are sparse and time-varying, such as those caused by attacks launched by an adversary. Group testing and the Kalman filter are designed jointly to perform real time system state estimation and time-varying faulty sensor detection with a small number of tests. Numerical results show that the faulty sensors are efficiently detected and removed, and the system state estimation performance is significantly improved via the proposed method. Compared with an approach that tests sensors one by one, the proposed approach reduces the number of tests significantly while maintaining a similar fault detection performance.