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"Learning Algorithms for Complete Targets Coverage in RF-Energy Harvest" by Chuyu Li, Kwan Wu Chin et al.

Internet of Things (IoTs) networks are responsible for monitoring an environment or targets such as vehicles. A key issue is determining the active time of a set of sensor nodes, so called set cover, that monitors all targets. This requires battery level knowledge at sensor nodes as an incorrect active time may cause energy outage, leading to uncovered target(s). However, in practice, it is impractical to obtain this information, 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 sensor nodes to first determine their probability of being active in each time slot. This information is then used by the HAP to construct set covers. We then introduce learning approaches based on Gibbs and Thompson sampling. The Gibbs sampling based algorithm or GB allows a sink/gateway to learn the best set cover to use over time. Similarly, our Thompson sampling solutions, namely TS-Random an ....

Two Phase Algorithm , Causal Energy Arrivals , Energy Harvesting , Markov Chain Monte Carlo , Onitoring Static Targets , Radio Frequency , F Charging , Temperature Measurement , Temperature Sensors ,

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