Learning Algorithms for Complete Targets Coverage in RF-Energy Harvesting Networks

A key task in Internet of Things (IoTs) networks is determining the active time of a set of devices or set covers to monitor static targets. This task, however, requires the battery level of devices. However, in practice, it is impractical to obtain an accurate battery level or channel state information from all devices, especially in large-scale networks. To this end, we present a number of approaches to construct set covers. We first propose a Two-Phase Algorithm (TPA) that requires devices to first determine their probability of being active in each time slot. This probability is then used by a Hybrid Access Point (HAP) to construct set covers. We then introduce learning approaches based on Gibbs and Thompson sampling. The Gibbs sampling based algorithm, aka GB, allows a sink/gateway to learn the best set cover to use over time. Similarly, our Thompson sampling solutions, namely TS-Random and TS-CB, construct set covers iteratively based on the probability that a device successfully monitors static targets. The numerical results show that GB performs better than TS-CB initially but has similar performance to TS-CB in the long term.