| A key task in Internet of Things (IoTs) networks is determining the active time of a set of devices or set covers to monitor \textcolor{red}{static} targets. This task, however, requires the battery level of devices. However, in practice, it is impractical to \textcolor{red}{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 \textcolor{red}{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. |