Optimal Sensor Grouping Transmission Strategy for Multiple Processes Over Packet-Dropping Channels

This article focuses on designing an optimal sensor grouping transmission strategy for multiple processes over packet-dropping channels. A necessary and sufficient condition for the convergence of the estimation error is presented when employing the random access protocol (RAP) for collision-free transmission within each group, and a continuous grouping transmission strategy (CGTS) is proposed to reduce the strategy space without losing optimality. Then, based on the condition and the CGTS, the optimal grouping transmission strategy is obtained by proposing an improved Q-learning algorithm. Compared with the existing works, the proposed optimal strategy reduces channel usage while ensuring estimation accuracy. Finally, a numerical simulation is provided to validate the main results.