  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.
