Cloud-Assisted On-Sensor Observation Classification in Latency-Impeded IoT Systems

摘要

The combination of computation and communication constraints within the Internet of Things systems require intelligent allocation of decision making and learning processes across a network of sensing and computing devices. In this paper, we present the problem of observation selection for reactive on-sensor decision-making, where the most accurate decision rule cannot be used unaided neither at the sensor (due to limited computing power), nor in the cloud (due to high communication latency). To make time-sensitive adaptation possible in these conditions, we consider learning a decision rule that is computationally viable for on-sensor use and is continuously adjusted by the cloud using the optimal decision rule for supervision. We pose a constrained stochastic optimization problem for online learning of such instrumental on-sensor classifier, propose an algorithm for updating its parameters, and establish the conditions under which convergence to a local extremum is guaranteed, at least for samples of independent observations.