WSNs中基于粒子群优化的信宿移动路径规划算法
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河南省科技攻关资助项目(182102210208);河南省高等学校青年骨干教师培养计划资助项目(2018GGJS196)

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Particle Swarm Optimization-based Rendezvous Point Selection in Wireless Sensor Networks
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    摘要:

    感测数据,再将数据传输至信宿是无线传感网络(WSNs)中节点的首要任务。传感节点由电池供电,它们的多数能量用于传输数据,越靠近信宿的节点,传输的数据量越大。因此,这些节点的能耗速度快,容易形成能量-空洞问题。而通过移动信宿收集数据能够缓解能量-空洞问题。为此,提出基于粒子群优化的信宿移动路径规划(PSO-RPS)算法。PSO-RPS算法结合数据传递时延和信息速率两项信息选择驻留点,并利用粒子群优化算法选择最优的驻留点,进而构建时延有效的信宿收集数据的路径。仿真结果表明,提出的PSO-RPS算法有效地控制路径长度,缩短了收集数据的时延。

    Abstract:

    Wireless Sensor Networks(WSNs) consist a set of sensor nodes whose primary task is to sense and relay the data to sink. Sensor nodes are powered by a battery, and most of the energy is consumed for the relay of the data. The closer the nodes are from the sink, the more the data will be relayed, and the faster the energy will be consumed, which results in energy-hole problem. The introduction of a mobile sink for collecting the data from the nodes can avoid the energy-hole problem. Particle Swarm Optimization-based Rendezvous Point Selection(PSO-RPS) is proposed, which considers the data delivery delay and traffic rate constraints of sensor nodes for rendezvous point selection, and finds an optimal number of rendezvous points by particle swarm optimization. On this basis, the delay-ef?cient trajectory of mobile sink for data collection is built. Simulation results show that the proposed PSO-RPS algorithm can effectively control the path length and reduce the time delay of data collection.

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张红军,刘 辉. WSNs中基于粒子群优化的信宿移动路径规划算法[J].太赫兹科学与电子信息学报,2021,19(6):1103~1107

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  • 收稿日期:2020-03-20
  • 最后修改日期:2020-05-21
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  • 在线发布日期: 2021-12-31
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