A Two-Phase Lifetime-Enhancing Method for Hybrid Energy-Harvesting Wireless Sensor Network

A major concern in wireless sensor network (WSN) is how to maximize the network lifetime while maintaining the coverage requirement. Since the energy of sensor node is limited, the network lifetime is extremely restricted. Recent studies have demonstrated that energy harvesting technology can potent...

Full description

Saved in:
Bibliographic Details
Published in:IEEE sensors journal Vol. 20; no. 4; pp. 1934 - 1946
Main Authors: Xiong, Yonghua, Chen, Gong, Lu, Manjie, Wan, Xiongbo, Wu, Min, She, Jinhua
Format: Journal Article
Language:English
Published: New York IEEE 15.02.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Classification
Computer simulation
Energy consumption
Energy harvesting
Energy limitation
Evolutionary algorithms
Intelligent sensors
Monitoring
multi-objective optimization
Multiple objective analysis
network lifetime
Nodes
Particle swarm optimization
Remote sensors
Schedules
Sensors
Sorting algorithms
target coverage
Wireless networks
wireless sensor network
Wireless sensor networks
ISSN:1530-437X, 1558-1748
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A major concern in wireless sensor network (WSN) is how to maximize the network lifetime while maintaining the coverage requirement. Since the energy of sensor node is limited, the network lifetime is extremely restricted. Recent studies have demonstrated that energy harvesting technology can potentially alleviate the energy limitation. This paper focuses on how to increase the network lifetime while satisfying the full target coverage in a novel Hybrid Energy-harvesting Wireless Sensor Network (HEWSN) which comprises both static non-rechargeable sensor nodes and mobile rechargeable ones, and then proposes a Two-phase Lifetime-enhancing Method (TLM). In the first phase, we employ a Multi-Objective Particle Swarm Optimization(MOPSO) algorithm based on adaptive grid for healing coverage hole, which relocates useful mobile rechargeable sensor nodes at optimal locations to meet the full target coverage. In the second phase, we present a Modified Binary multi-objective evolutionary algorithm based on Non-dominated Sorting and Bidirectional Local Search (MBNSBLS) to schedule sensor nodes into non-disjoint subsets working in turn, which helps to extend the network lifetime. Simulations are conducted and the results show that TLM has better performance than other approaches.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2019.2948620