Adaptive Distributed Hierarchical Sensing Algorithm for Reduction of Wireless Sensor Network Cluster-Heads Energy Consumption

TL;DR

This paper introduces the ADHS algorithm that adaptively adjusts cluster-head sensing rates in wireless sensor networks to enhance energy efficiency and prolong network lifetime without significantly compromising data accuracy.

Contribution

The paper presents a novel adaptive distributed hierarchical sensing algorithm that dynamically modifies cluster-head sensing rates based on data variance, improving energy efficiency.

Findings

Extends network lifetime by optimizing energy use.

Reduces energy consumption in cluster-heads.

Maintains sensing accuracy with adaptive rates.

Abstract

Energy efficiency is a crucial performance metric in sensor networks, directly determining the network lifetime. Consequently, a key factor in WSN is to improve overall energy efficiency to extend the network lifetime. Although many algorithms have been presented to optimize the energy factor, energy efficiency is still one of the major problems of WSNs, especially when there is a need to sample an area with different types of loads. Unlike other energy-efficient schemes for hierarchical sampling, our hypothesis is that it is achievable, in terms of prolonging the network lifetime, to adaptively re-modify CHs sensing rates (the processing and transmitting stages in particular) in some specific regions that are triggered significantly less than other regions. In order to do so we introduce the Adaptive Distributed Hierarchical Sensing (ADHS) algorithm. This algorithm employs a homogenous sensor network in a distributed fashion and changes the sampling rates of the CHs based on the variance of the sampled data without damaging significantly the accuracy of the sensed area.