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| 融合环境知识PSO算法的WSN覆盖优化 |
| Wireless Sensor Network Coverage Optimization Based on Particle Swarm Optimization with Environmental Knowledge |
| 投稿时间：2023-12-15 修订日期：2024-04-24 |
| DOI： |
| 中文关键词: 无线传感器网络 粒子群优化 多障碍物环境 环境知识 运动方程 |
| 英文关键词: wireless sensor networks particle swarm optimization multi-obstacle environment environmental knowledge motion equation |
| 基金项目:国家自然科学基金项目（12161043; 61662029）；江西省自然科学基金（20192BAB201007）；江西省教育厅科技项目（GJJ160623；GJJ170495）；江西理工大学青年英才支持计划项目（2018） |
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| 中文摘要: |
| 针对传统的粒子群优化算法在解决多障碍物环境内的无线传感器网络(Wireless sensor networks, WSNs)覆盖优化问题时存在盲目性的不足,提出一种融合环境知识的粒子群优化(Particle swarm optimization with environmental knowledge, PSO-EK)算法.在PSO-EK算法中,根据无线传感器节点在多障碍物环境下的状态,提出融合环境知识的扩散运动速度公式和碰撞运动速度公式,设计自适应的参数策略,提高多障碍物环境内的WSN覆盖率.实验结果表明,相比于传统算法,PSO-EK算法在解决多障碍物环境内的WSN覆盖优化问题时有着更优异的性能. |
| 英文摘要: |
| To reduce the blindness of particle swarm optimization in solving the coverage problem of wireless sensor networks (WSNs) within multi-obstacle environments, a novel particle swarm optimization with environmental knowledge (PSO-EK) is proposed. In the PSO-EK algorithm, based on the states of wireless sensor nodes within multi-obstacle environments, the diffusive and collisional motion velocity equations of the particles are proposed by fusing the environmental knowledge. In addition, an adaptive parameter strategy is also proposed to improve the coverage rate of WSN within multi-obstacle environments. Experimental results show that PSO-EK has obvious advantages in solving the coverage problem of WSN within multi-obstacle environments. |
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