Adaptive learning-enforced broadcast policy for solar energy harvesting wireless sensor networks

Abstract

The problem of message broadcast from the base station (BS) to sensor nodes (SNs) in solar energy harvesting enabled wireless sensor networks is considered in this paper. The aim is to ensure fast and reliable broadcast without disturbing upstream communications (from SNs to BS), while taking into account constraints related to the energy harvesting (EH) environment. A new policy is proposed where from the one hand, the BS first selects the broadcast time-slots adaptively with the SNs schedules (to meet active periods that are constrained by EH conditions), and from the other hand, SNs adapt their schedules to enable optimal selection of the broadcast time-slots that minimizes the number of broadcasts per message and the latency. Compared to the existing solutions, this enables fast broadcast and eliminates the need of adding message overhead to the broadcast message. For this purpose, an analytical energy model, a Hidden Markov Model(HMM), Baum–Welch learning algorithm, and a heuristic algorithm of the minimum covering set problem (MCS) are proposed and combined in a unique solution. The proposed solution is analyzed and compared with a state-of-the-art approach. The results confirm that the former has the advantage of performing the broadcast operation more reliably and in lower delay.