Energy-Efficient Sensor Scheduling for State Estimation Over Homogeneous Multi-Hop Networks
Yao Li, Rui Song, Jianyong Zheng, Xinping Guan · IEEE Signal Processing Letters · 2024
In this letter, anestimation-oriented power-constrained sensor scheduling problem over multi-hop sensor networks is studied. Two different online scheduling schemes for multi-hop transmission, i.e., global-delay-based scheduling (GS) and covariance-based scheduling (CS) are proposed, respectively. We propose a stochastic triggering scheme to satisfy power constraints. A Markovian model is adopted to formulate the state transition relationship in the scheduling process. In order to calculate the switching threshold and selection probabilities, an algorithm for parameters determination is further properly designed to overcome the coupling property between hops. We have explicitly analyzed the performance of GS by figuring out the upper and lower bounds of cost. Moreover, the superiority and optimality of CS have been theoretically proved by using the optimal state distribution method. Numerical simulations and comparisons with existing methods have been conducted illustratively to verify the correctness and effectiveness of our proposed schemes, algorithms and results.