Minimal Sleep Delay Driven Aggregation Tree Construction in IoT Sensor Networks

TL;DR

This paper addresses minimizing data aggregation delay in multi-channel duty-cycled IoT sensor networks by proposing a scheduling scheme that constructs an efficient aggregation tree, significantly reducing latency.

Contribution

It introduces a novel aggregation tree construction method based on sensor sleep delay and a scheduling scheme to improve parallel data transmissions in IoT sensor networks.

Findings

Reduces aggregation delay by up to 61%

Enhances parallel transmissions in sensor networks

Improves energy efficiency through optimized scheduling

Abstract

Data aggregation is a fundamental technique in wireless sensor networks (WSNs) in which sensory data collected by intermediate nodes is merged by in-network computation using maximum, average, or sum functions. Because sensors run on batteries, energy conservation is a critical issue. Duty cycle is a well-known energy-saving mechanism in WSNs, but it causes data aggregation latency to increase. As a result, the use of multichannel technology allows more sensor nodes to send data simultaneously, reducing data aggregation latency. We investigate the minimum latency aggregation scheduling problem in multi-channel duty-cycled IoT sensor networks in this paper. We propose a scheduling scheme that first constructs an aggregation tree based on sensor node sleep delay, then improves parallel transmissions by scheduling all eligible nodes in the constructed aggregation tree to enhance data aggregation. Based on extensive simulation experiments, our proposed approach lowers the aggregation delay by at most 61% compared to a novel approach.