西安电子科技大学学报 ›› 2019, Vol. 46 ›› Issue (2): 101-106.doi: 10.19665/j.issn1001-2400.2019.02.017

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一种无线传感器网络二维目标覆盖的改进方法

卢毅1,周杰1(),万连城2   

  1. 1. 石河子大学 信息科学与技术学院,新疆维吾尔自治区 石河子 832003
    2. 西安电子科技大学 期刊中心,陕西 西安 710071
  • 收稿日期:2018-09-10 出版日期:2019-04-20 发布日期:2019-04-20
  • 通讯作者: 周杰
  • 作者简介:卢毅(1981-),男,石河子大学博士研究生,E-mail:luyi@xidian.edu.cn.
  • 基金资助:
    国家自然科学基金(61662063);兵团重大科技项目(2017AA005-04);石河子大学高层次人才科研启动项目(RCZX201530)

Improved method for 2D target coverage in wireless sensor networks

LU Yi1,ZHOU Jie1(),WAN Liancheng2   

  1. 1. College of Information Science and Technology, Shihezi University, Shihezi 832003, China
    2. Center of Journal Publication, Xidian Univ., Xi’an 710071, China;
  • Received:2018-09-10 Online:2019-04-20 Published:2019-04-20
  • Contact: Jie ZHOU

摘要:

针对二维目标覆盖问题,提出了一种新的量子退火算法,设计了相应的系统模型,并给出了覆盖优化的目标函数。因为以往的启发式算法存在运行停滞等问题,所以为量子退火算法设计了全新的解集生成方式、量子旋转门、量子位测量方法和量子位状态更新方法,加快了算法的收敛速度。将基于量子退火算法的方法与粒子群算法、蚁群算法进行了仿真比较。仿真结果显示,相比粒子群算法与蚁群算法,该量子退火算法能够有效地提升解的质量,检出的目标数有较大幅度的提高。

关键词: 无线传感器网络, 量子退火算法, 目标覆盖, 粒子群算法, 蚁群算法

Abstract:

Two-dimensional target coverage is a key issue in wireless sensor networks. A good coverage algorithm can effectively improve the monitoring effect of wireless sensor networks. Aiming at the two-dimensional target coverage problem, a new quantum annealing algorithm is proposed, and the corresponding system model is designed. The objective function of coverage optimization is also given. Aiming at the problem of running stagnation in the past heuristic algorithms, a new solution set generation method, quantum revolving gate, qubit measurement method and qubit state update method are designed for the quantum annealing algorithm, which accelerates the convergence speed of the algorithm. The method based on the quantum annealing algorithm is compared with particle swarm optimization and ant colony optimization. Simulation results show that compared with the particle swarm optimization algorithm and the ant colony optimization, the proposed algorithm can effectively improve the quality of the solution, with the number of detected targets greatly improved.

Key words: wireless sensor networks, quantum simulated annealing algorithm, target coverage, particle swarm optimization, ant colony optimization

中图分类号: 

  • TP393