Energy-efficient Optimization of Reorganization-Enabled Wireless Sensor Networks

Abstract

This paper studies the target tracking problem in wireless sensor networks where sensor nodes are deployed randomly. To achieve tracking accuracy constrained by energy consumption, an energy-efficient optimization approach that enables reorganization of wireless sensor networks is proposed. The approach includes three phases which are related to prediction, localization and recovery, respectively. A particle filter algorithm is implemented on the sink node to forecast the future movement of the target in the first prediction phase. Upon the completion of this phase, the most energy efficient sensor nodes are awakened to collaboratively locate the target. Energy efficiency is evaluated by the ratio of mutual information to energy consumption. The recovery phase is needed to improve the robustness of the approach. It is performed when the target is missed because of the incorrect predicted target location. In order to recapture the target by awakening additional sensor nodes as few as possible, a genetic-algorithm-based mechanism is introduced to cover the recovery area. We show that the proposed approach has excellent tracking performance. Moreover, it can efficiently reduce energy consumption, prolong network lifetime and reduce network overheads.

Wireless Sensor Networks for Oceanographic Monitoring: A Systematic Review

“… The node’s power supply will require short-, medium- or long-term maintenance depending on the elements used. In normal conditions batteries limit operability to a matter of months, or even weeks [ 23 ]. It is therefore necessary to consider the use of renewable energies such as solar, eolic, wave or tidal power to significantly reduce system maintenance requirements.…”

“…It is therefore necessary to consider the use of renewable energies such as solar, eolic, wave or tidal power to significantly reduce system maintenance requirements. The most widely used is solar power because light energy is available practically constantly, and because of the accumulated experience in integrating solar panels as a supplementary energy source in A-WSN deployments [ 23 ]. One of the main challenges for A-WSNs as regards network topology and infrastructure is to achieve a network that functions without sacrificing originally-defined requirements in respect of performance, sensor coverage and connectivity [ 18 ].…”

An Energy Conservative Wireless Sensor Network Model for Object Tracking

“…In [4] and [17], the first node which senses the object wakes its one hop neighbours at first, if the object cannot be located then some more-hop neighbours are awaken, if this also fails, all the nodes are awaken at the worst case. An alternative way to choose the appropriate nodes to wake is selecting the nodes that have more energy for the recovery process [18].…”

“…The sink node awakens a sensor node according to the initial prediction results. This is similar to the Wang's model [18], however in their model, when the predicted nodes fail to sense the object, neighborhood nodes are awakened according to the results of the genetic algorithm they use and are awakened on a one by one basis, while we take an approach in which they are awakened based on one of the three approaches we implemented.…”

Comparative Analysis of Routing Protocols in Wireless Sensor–Actor Networks: A Review

“…WSN is widely used in various applications such as environmental monitoring, battlefield surveillance, industrial process control, and home applications [3][4][5]. These networks are distinguished from traditional wireless networks because of its unique characteristics namely, the dense node deployment, unreliability of sensor nodes, severe energy consumption, and storage constraints [6][7][8][9]. WSN is a passive network, where sensors gather and forward environmental information to the sink.…”