GTIACO: energy efficient clustering algorithm based on game theory and improved ant colony optimization

Recently, wireless sensor networks have been widely used for environmental and structural safety monitoring. However, node batteries cannot be replaced or easily recharged in harsh environments. Maximizing network lifetime remains a challenging issue in designing WSN routing. This paper introduces G...

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Vydáno v:	Telecommunication systems Ročník 86; číslo 3; s. 463 - 480
Hlavní autoři:	Wan, Hang, Qiu, Zhizhuo, Quan, Rui, David, Michael, Derigent, William
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.07.2024 Springer Nature B.V Springer Verlag (Germany)
Témata:	Algorithms Ant colony optimization Artificial Intelligence Business and Management Clustering Clusters Computer Communication Networks Computer Science Game theory Heuristic methods IT in Business Networking and Internet Architecture Probability Theory and Stochastic Processes Stability Structural safety Telecommunications systems Transmission efficiency Wireless sensor networks Ant colony optimization WSN Routing protocol Game theory Energy management Network lifecycle Game Theory Ant Colony Optimization Network Lifecycle Energy Management Routing Protocol
ISSN:	1018-4864, 1572-9451
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Abstract	Recently, wireless sensor networks have been widely used for environmental and structural safety monitoring. However, node batteries cannot be replaced or easily recharged in harsh environments. Maximizing network lifetime remains a challenging issue in designing WSN routing. This paper introduces GTIACO, a novel metaheuristic clustering protocol. It employs an optimal cluster head function to determine cluster number and utilizes game theory for selecting optimal cluster heads. To optimize inter-cluster routing, improved ant colony optimization (ACO) is introduced to construct gathering paths from clusters to the base station. Both blind pathways, pheromone concentration, and angle factors are considered to improve path exploration and transmission efficiency in ant colonies. To assess network performance, various scenarios involving different base station placements and network densities are examined. Experimental results demonstrate GTIACO's superiority over LEACH, ACO, SEP, and PRESPE protocols in network lifetime, stability, energy, and throughput. The proposed GTIACO shows an improvement of at least 4.3% in network lifetime and 32.8% in network throughout. It exhibits superior stability and transmission efficiency across diverse network densities.
AbstractList	Recently, wireless sensor networks have been widely used for environmental and structural safety monitoring. However, node batteries cannot be replaced or easily recharged in harsh environments. Maximizing network lifetime remains a challenging issue in designing WSN routing. This paper introduces GTIACO, a novel metaheuristic clustering protocol. It employs an optimal cluster head function to determine cluster number and utilizes game theory for selecting optimal cluster heads. To optimize inter-cluster routing, improved ant colony optimization (ACO) is introduced to construct gathering paths from clusters to the base station. Both blind pathways, pheromone concentration, and angle factors are considered to improve path exploration and transmission efficiency in ant colonies. To assess network performance, various scenarios involving different base station placements and network densities are examined. Experimental results demonstrate GTIACO's superiority over LEACH, ACO, SEP, and PRESPE protocols in network lifetime, stability, energy, and throughput. The proposed GTIACO shows an improvement of at least 4.3% in network lifetime and 32.8% in network throughout. It exhibits superior stability and transmission efficiency across diverse network densities. Recently, wireless sensor networks have been widely used for environmental and structural safety monitoring. However, node batteries cannot be replaced or easily recharged in harsh environments. Maximizing network lifetime remains a challenging issue in designing WSN routing. This paper introduces GTIACO, a novel metaheuristic clustering protocol. It employs an optimal cluster head function to determine cluster number and utilizes Game Theory (GT) for selecting optimal cluster heads. To optimize inter-cluster routing, improved ant colony optimization (ACO) is introduced toconstruct gathering paths from clusters to the base station. Both blind pathways, pheromone concentration, and angle factors are considered to improve path exploration and transmission efficiency in ant colonies. To assess network performance, various scenarios involving different base station placements and network densities are examined. Experimental results demonstrate GTIACO's superiority over LEACH, ACO, SEP, and PRESPE protocols in network lifetime, stability, energy, and throughput. The proposed GTIACO shows an improvement of at least 4.3% in network lifetime and 32.8% in network throughout. It exhibits superior stability and transmission efficiency across diverse network densities.
Author	David, Michael Wan, Hang Derigent, William Qiu, Zhizhuo Quan, Rui
Author_xml	– sequence: 1 givenname: Hang surname: Wan fullname: Wan, Hang organization: Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology – sequence: 2 givenname: Zhizhuo surname: Qiu fullname: Qiu, Zhizhuo organization: Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology – sequence: 3 givenname: Rui surname: Quan fullname: Quan, Rui email: 3331339275@qq.com organization: Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology – sequence: 4 givenname: Michael surname: David fullname: David, Michael organization: CRAN, CNRS UMR 7039, University of Lorraine – sequence: 5 givenname: William surname: Derigent fullname: Derigent, William organization: CRAN, CNRS UMR 7039, University of Lorraine
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Keywords	Ant colony optimization WSN Routing protocol Game theory Energy management Network lifecycle Game Theory Ant Colony Optimization Network Lifecycle Energy Management Routing Protocol
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SubjectTerms	Algorithms Ant colony optimization Artificial Intelligence Business and Management Clustering Clusters Computer Communication Networks Computer Science Game theory Heuristic methods IT in Business Networking and Internet Architecture Probability Theory and Stochastic Processes Stability Structural safety Telecommunications systems Transmission efficiency Wireless sensor networks
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