Optimal sensor scheduling for state estimation under limited channel resources

In this paper, we investigate the estimation-oriented sensor scheduling problem over industrial cyberphysical systems. Different from the existing works, we consider a more innovative and practical scenario where channel resources are limited. A novel transmission scheduling framework for sensors monitoring multiple sub-processes is proposed, where the most important sensory data will transmitted with higher priority and reliability. For two-system case, we first present an explicit optimal scheduling strategy under the off-line scenario. In order to further improve the performance, we propose an on-line scheduling strategy based on the arrival information feedback. Moreover, we have theoretically proved the feasibility and superiority of our proposed on-line schedule to the optimal off-line one. Besides, in order to reduce the computation complexity, we present sub-optimal algorithms for more complicated multisystem scenario, and derive out the conditions under which the optimal or sub-optimal schedules can be designed explicitly and separately, which conspicuously reduces the computation complexity. Moreover, the theoretical gap between our proposed sub-optimal schedules and optimal schedule can be determined by solving two relaxed problems. Simulations are conducted to demonstrate and verify the correctness and advantages of proposed schedules.(c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.