Randomized Edge-Assisted On-Sensor Information Selection for Bandwidth-Constrained Systems

摘要

The problem of intelligent information selection in the Internet-of-Things systems with limited computational and communication resources is studied. One distinctive property of such systems is the clash of the computational complexity of the desired selection procedure and the low throughput of the wireless links between the devices acquiring information (sensors) and processing it (edge and cloud computing servers). To adaptively resolve that conflict, we propose a stochastic optimization algorithm for edge-assisted online learning of the optimal on-sensor observation classification and transmission decision rules. Using the stochastic Lyapunov function method, we prove that the resulting adaptive procedure can be used to adjust the parameters of the two local decision rules to asymptotically satisfy the constraint on channel access probability and to minimize the expected classification error.