A new clustering routing algorithm for wsn based on brief artificial fish-school optimization and ant colony optimization

Haitao Xiao, Xue Zhao, Harutoshi Ogai

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

On the basis of analyzing the Low Energy Adaptive Clustering Hierarchy (LEACH) and Ant Colony Optimization based routing algorithm, a novel clustering routing algorithm for Wireless Sensor Network (WSN) based on Brief Artificial Fish-School Optimization and Ant Colony is proposed in this paper to prolong life cycle of the whole network and reduce energy consumption of WSN. The algorithm contains two routing levels. In the first level (intra-cluster), we propose a cluster routing algorithm based on Brief Artificial Fish-School Optimization (BAFSO) for one hop WSNs. The cluster members send data to their cluster head, and the cluster head aggregates these data and transmit them to base station. The selection method of the cluster head considers the location of the cluster head node and the optimal number of cluster head nodes. It can achieve the balance of the network energy consumption, improve the energy efficiency and prolong life cycle of the whole network. The second level (inter-cluster) is used for multi-hop WSNs. In the second level, the first level is employed to create cluster and select cluster head at first. Then the cluster heads use ant colony optimization (ACO) algorithm to find a route to the base station. As only cluster heads participate in the inter-cluster routing operation, the method can provide a smooth operation more effectively. The delay of the algorithm is minimized by using the ant colony optimization algorithm along with clustering. The proposed algorithm is applied in the complex wireless sensor system of bridge health diagnosis system. To assess the efficiency of the proposed algorithm, we compare the method with some other routing algorithms. The results show lower power consumption and more load balancing.

Original languageEnglish
JournalIEEJ Transactions on Electronics, Information and Systems
Volume133
Issue number7
DOIs
Publication statusPublished - 2013

Fingerprint

Ant colony optimization
Routing algorithms
Clustering algorithms
Fish
Base stations
Life cycle
Wireless sensor networks
Energy utilization
Resource allocation
Energy efficiency
Electric power utilization
Health
Sensors

Keywords

  • ACO
  • BAFSO
  • Bridge diagnosis
  • Clustering routing
  • System design
  • Wireless Sensor Networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

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abstract = "On the basis of analyzing the Low Energy Adaptive Clustering Hierarchy (LEACH) and Ant Colony Optimization based routing algorithm, a novel clustering routing algorithm for Wireless Sensor Network (WSN) based on Brief Artificial Fish-School Optimization and Ant Colony is proposed in this paper to prolong life cycle of the whole network and reduce energy consumption of WSN. The algorithm contains two routing levels. In the first level (intra-cluster), we propose a cluster routing algorithm based on Brief Artificial Fish-School Optimization (BAFSO) for one hop WSNs. The cluster members send data to their cluster head, and the cluster head aggregates these data and transmit them to base station. The selection method of the cluster head considers the location of the cluster head node and the optimal number of cluster head nodes. It can achieve the balance of the network energy consumption, improve the energy efficiency and prolong life cycle of the whole network. The second level (inter-cluster) is used for multi-hop WSNs. In the second level, the first level is employed to create cluster and select cluster head at first. Then the cluster heads use ant colony optimization (ACO) algorithm to find a route to the base station. As only cluster heads participate in the inter-cluster routing operation, the method can provide a smooth operation more effectively. The delay of the algorithm is minimized by using the ant colony optimization algorithm along with clustering. The proposed algorithm is applied in the complex wireless sensor system of bridge health diagnosis system. To assess the efficiency of the proposed algorithm, we compare the method with some other routing algorithms. The results show lower power consumption and more load balancing.",
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AU - Zhao, Xue

AU - Ogai, Harutoshi

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N2 - On the basis of analyzing the Low Energy Adaptive Clustering Hierarchy (LEACH) and Ant Colony Optimization based routing algorithm, a novel clustering routing algorithm for Wireless Sensor Network (WSN) based on Brief Artificial Fish-School Optimization and Ant Colony is proposed in this paper to prolong life cycle of the whole network and reduce energy consumption of WSN. The algorithm contains two routing levels. In the first level (intra-cluster), we propose a cluster routing algorithm based on Brief Artificial Fish-School Optimization (BAFSO) for one hop WSNs. The cluster members send data to their cluster head, and the cluster head aggregates these data and transmit them to base station. The selection method of the cluster head considers the location of the cluster head node and the optimal number of cluster head nodes. It can achieve the balance of the network energy consumption, improve the energy efficiency and prolong life cycle of the whole network. The second level (inter-cluster) is used for multi-hop WSNs. In the second level, the first level is employed to create cluster and select cluster head at first. Then the cluster heads use ant colony optimization (ACO) algorithm to find a route to the base station. As only cluster heads participate in the inter-cluster routing operation, the method can provide a smooth operation more effectively. The delay of the algorithm is minimized by using the ant colony optimization algorithm along with clustering. The proposed algorithm is applied in the complex wireless sensor system of bridge health diagnosis system. To assess the efficiency of the proposed algorithm, we compare the method with some other routing algorithms. The results show lower power consumption and more load balancing.

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