Energy efficient prediction clustering algorithm for multilevel heterogeneous wireless sensor networks

TL;DR

This paper introduces an adaptive energy-efficient clustering algorithm for heterogeneous wireless sensor networks that prolongs network lifetime by optimizing cluster head selection based on residual energy and communication costs.

Contribution

The paper proposes a novel prediction-based clustering algorithm tailored for heterogeneous sensor networks, improving energy efficiency and network longevity.

Findings

Longer network lifetime compared to existing algorithms

Higher energy efficiency in data transmission

Improved network monitoring quality

Abstract

In designing wireless sensor networks, it is important to reduce energy dissipation and prolong network lifetime. In this paper, a new model with energy and monitored objects heterogeneity is proposed for heterogeneous wireless sensor networks. We put forward an energy-efficient prediction clustering algorithm, which is adaptive to the heterogeneous model. This algorithm enables the nodes to select the cluster head according to factors such as energy and communication cost, thus the nodes with higher residual energy have higher probability to become a cluster head than those with lower residual energy, so that the network energy can be dissipated uniformly. In order to reduce energy consumption when broadcasting in clustering phase and prolong network lifetime, an energy consumption prediction model is established for regular data acquisition nodes. Simulation results show that compared with current clustering algorithms, this algorithm can achieve longer sensor network lifetime, higher energy efficiency and superior network monitoring quality.