Utility-Theory-Based Optimal Resource Consumption for Inference in IoT Systems

We study the problem of a sensor performing inference tasks based on the utility theory, where the objective is to derive the optimal resource usage amount that maximizes a profit-cost-based utility function. Furthermore, to enable the concept of sensing as a service in the context of IoT systems, we present a market-based paradigm, where there is a 'buyer' interested in buying the inference result from the sensor. We jointly optimize the resource usage policy and payment negotiation strategy for the sensor so as to maximize the expected profit. Optimal payment negotiation is analyzed in two situations, namely, when the sensor spends a fixed amount of resource, as well as when the sensor could vary the amount of resource consumption to maximize profit. It is shown that in the presence of the buyer, the optimal amount of resource consumption increases and, hence, the inference accuracy improves. Finally, we present some discussions on how energy efficiency affects the behavior of energy consumption in realistic environments. Simulation results are provided to illustrate the performance of our approach.