An adaptive energy-efficient and low-latency MAC for data gathering in wireless sensor networks

TLDR

Many sensor network applications involve data collected from multiple source nodes to a sink via a unidirectional tree. The authors propose DMAC, an energy‑efficient, low‑latency MAC tailored for such data‑gathering trees in wireless sensor networks. DMAC addresses the interruption problem by assigning depth‑dependent sleep offsets, adapts duty cycles to traffic load, and incorporates data prediction and more‑to‑send packets to enable continuous forwarding and reduce contention. Simulations demonstrate that DMAC mitigates the interruption issue, achieving significant energy savings, latency reduction, and high data reliability. Only a summary form of the paper is provided.

Abstract

Summary form only given. In many sensor network applications the major traffic pattern consists of data collected from several source nodes to a sink through a unidirectional tree. We propose DMAC, an energy efficient and low latency MAC that is designed and optimized for such data gathering trees in wireless sensor networks. We first show that previously proposed MAC protocols for sensor networks that utilize activation/sleep duty cycles suffer from a data forwarding interruption problem, whereby not all nodes on a multihop path to the sink are notified of data delivery in progress, resulting in significant sleep delay. DMAC is designed to solve the interruption problem and allow continuous packet forwarding by giving the sleep schedule of a node an offset that depends upon its depth on the tree. DMAC also adjusts the duty cycles adaptively according to the traffic load in the network. We further propose a data prediction mechanism and the use of more-to-send (MTS) packets in order to alleviate problems pertaining to channel contention and collisions. Our simulation results show that by exploiting the application-specific structure of data gathering trees in sensor networks, DMAC provides significant energy savings and latency reduction while ensuring high data reliability.