无线传感器网络节点定位和目标覆盖研究

发布时间：2018-06-15 20:49

本文选题：WSN + 节点定位　；参考：《哈尔滨工程大学》2014年硕士论文

【摘要】：无线传感器网络(Wireless Sensor Network,WSN)作为一种全新的信息交互方式将物理世界与传输网络联系起来。它具有低成本、容错性好、可长期执行监测任务等特点,被广泛用于军事、环境、医疗卫生以及商业等领域。作为无线传感器网络重要支撑技术和理论基础的节点定位和覆盖控制技术也引起了人们的广泛关注。大部分网络应用中起先都要求得位置信息,没有位置信息的数据对于大多数传感器网络应用来说是无意义的。WSN履行自身获取信息的职责,实现用户服务质量的前提,就是设法实现传感器节点完全地覆盖监控区域或目标,只有达到了覆盖控制要求,才能保证获得的信息全面、可靠、准确。本文围绕节点定位和目标覆盖这两方面进行深入的研究。本文总结了前人的研究成果,针对分布式迭代定位算法中传播误差的问题,提出迭代平均思想,采用收敛速度快的种群分类和动态学习因子的粒子群算法(Population Classification and Dynamic Learning Factor Particle Swarm Optimization, CDLPSO)对节点进行定位,通过实验给出粒子群算法的分界值,能更好地为粒子速度选择适合的进化模型,通过实验仿真验证,本文算法在定位误差和定位时间方面均优于标准的粒子群定位算法。根据无线传感器网络的实际应用环境,本文对三维空间异构传感器网络的目标覆盖问题进行研究,针对遗传算法容易陷入局部最优的缺点,提出改进二进制差分算法(Chaotic Local Binary Differential Evolution,CLBDE)设计目标覆盖模型,引入混沌映射思想产生初始种群,使产生的初始种群均匀分布在搜索空间,并对后期优化出的部分优秀解进行相似调度,通过多次实验仿真,在满足所有目标达到覆盖要求的情况下,从普通节点最大无影响半径,衰减因子,目标个数,覆盖阈值,检测区域大小等标准进行评价,本文算法较遗传算法和二进制差分算法寻优得出的工作传感器节点集最小。
[Abstract]:Wireless Sensor Network (WSN), as a new way of information interaction, connects the physical world with the transmission network. It has the characteristics of low cost, good fault tolerance, and can perform monitoring tasks for a long time. It is widely used in military, environment, medical health, business and other fields. It is important as a wireless sensor network. In most network applications, location information is required in most network applications. Data without location information is a meaningless.WSN for most sensor network applications to perform its own responsibility to obtain information, and to achieve the quality of user service. The premise is to realize the sensor nodes to fully cover the monitoring area or target, only to achieve the coverage control requirements, to ensure that the information obtained is comprehensive, reliable and accurate. This paper focuses on the two aspects of the node location and target coverage. This paper has always been the research results of the predecessors, aiming at the distributed iterative positioning. In the algorithm, the problem of propagation error is proposed, and the iterative mean idea is proposed. The particle swarm optimization (Population Classification and Dynamic Learning Factor Particle Swarm Optimization, CDLPSO) is used to locate the node, and the boundary value of the particle swarm optimization is given by experiment. According to the experimental simulation, the algorithm is superior to the standard particle swarm optimization algorithm in location error and location time. According to the actual application environment of wireless sensor network, this paper studies the target coverage problem of the three-dimensional spatial heterogeneous sensor network, aiming at genetic calculation. Chaotic Local Binary Differential Evolution (CLBDE) is proposed to design the target coverage model, and the initial population is generated by the idea of chaotic mapping, and the initial population is distributed uniformly in the search space, and a similar scheduling is carried out for some excellent solutions optimized in the later period. Through many experiments and simulation, in order to meet the requirements of all targets, the maximum unaffected radius, attenuation factor, target number, coverage threshold, detection area size and other standards are evaluated. The algorithm of this algorithm is minimal compared with genetic algorithm and binary difference algorithm.
【学位授予单位】：哈尔滨工程大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN929.5;TP212.9

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