Computer Science ›› 2018, Vol. 45 ›› Issue (11): 103-107.doi: 10.11896/j.issn.1002-137X.2018.11.015

• Network & Communication • Previous Articles     Next Articles

Multi-sink Deployment in Wireless Sensor Networks for Underground Coal MineBased onAdaptive Particle Swarm Optimization Clustering Algorithm

HU Chang-jun1,2, YUAN Shu-jie1,3   

  1. (Key Laboratory of Safety and High-efficiency Coal Mining,Ministry of Education,Anhui University of Science and Technology,Huainan,Anhui 232001,China)1
    (School of Electrical and Information Engineering,Anhui University of Science and Technology,Huainan,Anhui 232001,China)2
    (School of Energy and Safety,Anhui University of Science and Technology,Huainan,Anhui 232001,China)3
  • Received:2017-10-22 Published:2019-02-25

Abstract: Multi-sink deployment is an important research topic in underground sensor networks,which has a great influence on network performance.In view of the defect of complex calculation process,slow convergence rate,and trapping into local optimization existing in current deployment methods,on the basis of standard particle swarm optimization algorithm,a multi-sink deployment algorithm (A-PSOCA) based on adaptive particle swarm optimization clustering algorithm was proposed.In the A-PSOCA algorithm,the status of particle evolution and aggregation is introduced in the inertia weight coefficient to make the proposed algorithm more adaptive,and a preventive strategy from position overlapping in the iterative process of the algorithm is introduced to prevent particle swarm search from local optimization.Simulation results show that the A-PSOCA algorithm obtains reasonable locations for sink nodes,and its convergence rate is twice as faster as the standard particle swarm clustering algorithm.Compared with the other algorithms based on particle swarm optimization,the A-PSOCA approach has obvious advantages in terms of average energy consumption,proportionality and the lifetime of corresponding network.It is more suitable for underground communication environment.

Key words: Adaptive algorithm, Clustering algorithm, Multi-sink deployment, Particle swarm optimization algorithm, Underground mine monitoring

CLC Number: 

  • TP393
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