Likelihood ratio based communication for distributed detection

Abstract

This paper is concerned with a detection framework under scheduled communication for a binary hypothesis testing problem. A scheduler is designed to smartly select useful sensor measurements for transmission and leave non-useful ones, which results in that only a subset of measurements is sent to the testing agency. To this purpose, a likelihood ratio based scheduler is implemented to decide the transmission of measurements from sensor to the tester. For comparison, a random scheduler which randomly selects measurements for transmission is also included. The Neyman-Pearson tests under the above two schedulers is provided. Given a moderate communication cost constraint, it is shown that the likelihood ratio based scheduler achieves a comparable asymptotic testing performance to the optimal test using the full set of measurements, and is strictly better than the random scheduler. The theoretical results are verified by simulations.

Likelihood Ratio-Based Scheduler for Secure Detection in Cyber Physical Systems

“…Thus, we resort to the asymptotic approach to quantify the effect of the scheduler on the detection performance, and obtain optimal schedulers. Note that sensor data scheduling has been reported in [20]- [24] for detection problems and in [19], [25]- [27] for state estimation problems, while none of these works consider security issues. Besides, they focus on establishing an optimal transmission strategy under different scenarios while the effect of sensor scheduling mechanism on the detection performance remains unexploited.…”