基于改进粒子群优化算法的传感器部署机制  

Sensor Deployment Scheme Based on Improved Particle Swarm Optimization

作　　者：丁晨阳[1] 彭军[2] DING Chen-yang PENG Jun(Department of Electrical Engineering, Yangzhou Polytechnic College, Yangzhou 225009, China School of Information Science and Engineering, Central South University, Changsha 410075. China)

机构地区：[1]扬州市职业大学电子工程学院,江苏扬州225009 [2]中南大学信息科学与工程学院,湖南长沙410075

出　　处：《仪表技术与传感器》2016年第12期176-180,共5页Instrument Technique and Sensor

基　　金：扬州市基础研究计划(自然科学基金)-面上项目(YZ2016124)

摘　　要：为提高无线传感器网络性能,通过恰当的传感器部署机制获得优化的覆盖是很重要的问题。改进的粒子群优化算法通过重新部署初始随机分布的移动传感器,改善了覆盖效果。针对粒子群优化算法可能出现局部最优而导致覆盖优化效果降低问题,引入模拟退火算法的接受规则更新微粒的速度和位置。为减少算法执行时间,采用Voronoi图覆盖空缺和传感器间距离的标准偏差设计了适应度函数。最后根据移动距离调整各个传感器的移动目标,减少了能量消耗。仿真结果表明:和原始粒子群算法相比,改进的算法能够获得更高的覆盖率、更快的收敛以及更低的能量消耗。For the efficiency of sensor network, the appropriate sensor deployment scheme to achieve the optimal coverage is an important issue.Mobile sensors were redeployed to accomplish a better arrangement after the initial random deployment by improved particle swarm optimization.To prevent particle swarm optimization algorithm falling into a local optimum solution, simulated annealing was imported and its acceptance rule was used to determine the new velocity and position of particles. Voronoi vacancy and standard deviation of the distances between sensors were used to design the fitness function, so that computing time was reduced.Moreover, the energy consumption was reduced by adjusting the destination for mobile sensors, which was based on the distance sensors.Simulation results show the improved algorithm achieves higher coverage,faster convergence speed and lower energy consumption.

关 键 词：传感器部署 覆盖 粒子群优化 模拟退火 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]