Joint Group Testing of Time-varying Faulty Sensors and System State Estimation in Large Sensor Networks
For large sensor networks with sparse, time-varying faults (e.g., from attacks), this work offers a more efficient detection method that reduces testing overhead without sacrificing performance.
The paper proposes a joint group testing and Kalman filter method for detecting time-varying sparse sensor faults and estimating system state in large sensor networks, achieving efficient fault detection and improved state estimation while significantly reducing the number of tests compared to testing sensors individually.
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.