Sensor deployment is an important issue in wireless sensor network (WSN), which is a typical nonlinear system. Conditions of both area coverage and point coverage should be considered in research studies on sensor coverage. It is generally necessary to ensure high coverage ratio of area when controlling sensor locations, and covering specific point targets to ensure long lifetime is also important sometimes. In current studies, swarm intelligence algorithms such as particle swarm optimization (PSO) are widely used to solve the sensor deployment problem in WSN. In this paper, coverage rate and network life indicators are analyzed comprehensively with establishment of a more general K-coverage model. In related calculation examples with different coverage requirements including target coverage, area coverage, and boundary coverage, several improved algorithms based on PSO are applied to solve the problem in the paper. Simulation results show that the improved algorithms can achieve a good performance and deployment effect.

中文翻译：

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改进PSO算法在多约束下传感器覆盖优化

传感器部署是无线传感器网络（WSN）中的一个重要问题，该网络是典型的非线性系统。在传感器覆盖率的研究中应考虑区域覆盖率和点覆盖率的条件。通常，在控制传感器位置时必须确保较高的面积覆盖率，并且覆盖特定的点目标以确保较长的使用寿命有时也很重要。在当前的研究中，诸如粒子群优化（PSO）之类的群体智能算法被广泛用于解决WSN中的传感器部署问题。本文通过建立更通用的K覆盖率模型，对覆盖率和网络寿命指标进行了综合分析。在具有不同覆盖范围要求（包括目标覆盖范围，区域覆盖范围和边界覆盖范围）的相关计算示例中，本文提出了几种基于粒子群优化算法的改进算法。仿真结果表明，改进算法能取得良好的性能和部署效果。