Node distribution optimization in mobile sensor networks based on differential evolution algorithm

被引：0
|
作者：
Jin, Li-Zhong [1 ]
Chang, Gui-Ran [2 ]
Jia, Jie [1 ]
机构：
[1] College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
[2] Computing Center, Northeastern University, Shenyang 110819, China
来源：
Kongzhi yu Juece/Control and Decision | 2010年 / 25卷 / 12期
关键词：
Sensor nodes - Global optimization - Evolutionary algorithms;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Aiming at the optimization of node distribution, this paper investigates the problem of maximizing the effective coverage area of mobile sensor networks under the premise of guaranteeing the network connectivity. A node distribution optimization scheme based on differential evolution algorithm is proposed. Simulation results show that the proposed algorithm can quickly achieve node distribution optimization of a mobile sensor network, increase the effective coverage rate, and achieve global optimization of the deployment of the mobile sensor network at a relatively low cost.
引用
收藏
页码：1857 / 1860
相关论文
共 50 条
  • [31] Distributed deployment optimization algorithm for mobile sensor networks
    Zhu Y.
    Jiang A.
    Zhu Z.
    He K.
    2018, Huazhong University of Science and Technology (46): : 89 - 92and103
  • [32] A hybrid optimization algorithm based on chaotic differential evolution and estimation of distribution
    Zhao, Fuqing
    Shao, Zhongshi
    Wang, Junbiao
    Zhang, Chuck
    COMPUTATIONAL & APPLIED MATHEMATICS, 2017, 36 (01): : 433 - 458
  • [33] An Optimization Based Distributed Algorithm for Mobile Data Gathering in Wireless Sensor Networks
    Zhao, Miao
    Yang, Yuanyuan
    2010 PROCEEDINGS IEEE INFOCOM, 2010,
  • [34] A Coverage Optimization Strategy for Mobile Wireless Sensor Networks Based on Genetic Algorithm
    Liang, Chiu-Kuo
    Lin, Yu-Hsiung
    PROCEEDINGS OF 4TH IEEE INTERNATIONAL CONFERENCE ON APPLIED SYSTEM INNOVATION 2018 ( IEEE ICASI 2018 ), 2018, : 1272 - 1275
  • [35] A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks
    Qin, Miao
    Zhu, Rongbo
    EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2018,
  • [36] A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks
    Miao Qin
    Rongbo Zhu
    EURASIP Journal on Wireless Communications and Networking, 2018
  • [37] Optimization of Wireless Sensor Node Parameters by Differential Evolution and Particle Swarm Optimization
    Kroemer, Pavel
    Prauzek, Michal
    Musilek, Petr
    Barton, Tomas
    PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS (IBICA 2014), 2014, 303 : 13 - 22
  • [38] Seagull optimization algorithm for node localization in wireless sensor networks
    Mohan, Yogendra
    Yadav, Rajesh Kumar
    Manjul, Manisha
    MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83 (28) : 70793 - 70814
  • [39] RSSI-based optimization of static and mobile node combinations for dynamic node localization in wireless sensor networks
    Kumar, Kottu Santosh
    Rout, Saroja Kumar
    Panda, Sujit Kumar
    Mohapatra, Pradyumna Kumar
    Mohanty, Sachi Nandan
    Khan, M. Ijaz
    TELECOMMUNICATION SYSTEMS, 2024, 87 (01) : 137 - 149
  • [40] A Node Positioning Algorithm in Wireless Sensor Networks Based on Improved Particle Swarm Optimization
    Sun Shunyuan
    Yu Quan
    Xu Baoguo
    INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING, 2016, 9 (04): : 179 - 189
12345