3rd International ICST Conference on Quality of Service in Heterogeneous Wired/Wireless Networks

Research Article

Stability-based multi-objective clustering in mobile ad hoc networks

  • @INPROCEEDINGS{10.1145/1185373.1185408,
        author={Jiannong  Cao and Hui Cheng and Xingwei Wang and Sajal K.  Das},
        title={Stability-based multi-objective clustering in mobile ad hoc networks},
        proceedings={3rd International ICST Conference on Quality of Service in Heterogeneous Wired/Wireless Networks},
        publisher={ACM},
        proceedings_a={QSHINE},
        year={2006},
        month={8},
        keywords={Ad Hoc Networks Group Mobility Clustering Multi-Objective Evolutionary Optimization},
        doi={10.1145/1185373.1185408}
    }
    
  • Jiannong Cao
    Hui Cheng
    Xingwei Wang
    Sajal K. Das
    Year: 2006
    Stability-based multi-objective clustering in mobile ad hoc networks
    QSHINE
    ACM
    DOI: 10.1145/1185373.1185408
Jiannong Cao1,*, Hui Cheng1, Xingwei Wang2, Sajal K. Das3,4
  • 1: Dept. of Computing, Hong Kong, Polytechnic University, Hong Hum, Kowloon, Hong Kong
  • 2: School of Information Science & Engineering, Northeastern University, Shenyang, 110004, China
  • 3: Dept. of Computer Science and Engineering, University of Texas at Arlington
  • 4: Arlington, TX 76019, USA
*Contact email: csjcao@comp.polyu.edu.hk

Abstract

In this paper, we propose a stability-based multi-objective clustering algorithm, which can achieve stable cluster structure by exploiting the node movement proximity, and meanwhile optimize multiple clustering metrics simultaneously by a reputable multi-objective evolutionary algorithm (MOEA). The performance of the proposed algorithm has been evaluated through extensive simulations with network topologies of various sizes. The results demonstrated that the clustered topologies generated by our algorithm have good performance in terms of stability. Our algorithm can achieve optimal cluster structure with respect to each clustering metric on small-scale network topology. For large-scale network topology, it also outperforms WCA, a well known multi-objective clustering algorithm using a weighted sum of multiple metrics.