Event Driven Duty Cycling with Reinforcement Learning and Monte Carlo Technique for Wireless Network

Abstract

Reducing transmission delay and maximizing the network lifetime are important issues for wireless sensor networks (WSN). The existing approaches commonly let the nodes periodically sleep to minimize energy consumption, which adversely increases packet forwarding latency. In this study, a novel scheme is proposed, which effectively determines the duty cycle of the nodes and packet forwarding path according to the network condition by employing the event-based mechanism and reinforcement learning technique. This allows low-latency energy-efficient scheduling and reduces the transmission collision between the nodes on the path. The Monte Carlo evaluation method is also adopted to minimize the overhead of the computation of each node in making the decision. Computer simulation reveals that the proposed scheme significantly improves end-to-end latency, waiting time, packet delivery ratio, and energy efficiency compared to the existing schemes including S-MAC and event-driven adaptive duty cycling scheme.