【作者】 刘萍；

【导师】 杨小军；

【作者基本信息】 长安大学 ， 软件工程， 2015， 硕士

【摘要】 无线传感器网络(Wireless Sensor Networks,WSN)是由一组能够感知和监测环境的小型装置组合而成的且通过无线方式通信的传感器网络,它的应用已经从军事扩展到医疗、教育和家庭等众多领域,为社会的发展做出了巨大的贡献。但是由于WSN中的能量、通信带宽等资源的限制,选择最优且最少的传感器节点组合对目标进行跟踪已成为研究热点,因此无线传感器网络中传感器节点的管理对于目标的跟踪具有非常重要的意义。在目标的监测与跟踪过程中,若所有的节点都参与目标的监测跟踪,其跟踪精度会很高但是节点的能量消耗也非常多,所以需要对传感器节点进行选择从而节省能量。本文利用条件后验克拉美-罗下界作为传感器选择的判断标准,实现传感器节点的在线选择,该管理准则能够更准确的估计目标的运动,得出更加精确的跟踪位置。在WSN中,一般采用穷举算法对传感器量测节点进行管理。但是在节点的选择过程中,随着系统中被选择的节点个数的增加,其计算量也越大。针对这一问题,本文提出利用二进制粒子群算法对传感器节点进行管理,该算法简单易于理解,参数少,是通过迭代来寻找最优的节点组合,在很大程度上减少了计算量。在粒子群算法中每一个粒子表示一种潜在的传感器节点的组合,每个粒子的维数表示所有节点的个数,算法中的适应度函数是条件后验克拉美-罗下界,结果表明此算法具有很好的跟踪性能。在利用二进制粒子群算法对传感器节点进行管理时,目标函数只有一个。在实际的应用中通常需要解决的是多个目标同时优化的问题,即目标的跟踪过程中,选择的传感器节点需要在满足跟踪性能的同时其被选择的节点的个数最少。本文提出利用多目标粒子群优化算法来解决多目标优化问题,同时优化两个目标函数,一个是传感器的管理准则条件后验克拉美-罗下界,一个是传感器节点的个数。最后仿真表明该算法的有效性。更多还原

【Abstract】 Wireless Sensor network is composed of a set of small unit which can sense and monitoring environment, and it communicates with other sensor via wireless way. Its application has been expanded from the military to many fields, such as health care, education and family. It has made great contributions to the development of society. But in WSN, the energy and communications bandwidth are limited. It has become a hot research to choose the optimal and the least amount of sensor nodes for target tracking. So the management of the sensor nodes in wireless sensor network is very meaningful.In the process of target tracking, the tracking accuracy will be very high if all of the nodes involved in the work. But it also will consume much energy in this way. For this problem, a sensor management scheme is proposed based on conditional posterior Cramer-Rao lower bounds(CPCRLB). This online sensor selection is achieved by particle filtering. And the results demonstrate the efficiency and superiority of the CPCRLB-based sensor management.Exhaustive algorithm usually be used to select sensor in WSN. The computational complexity of find an optimal subset through exhaustive search can grow exponentially with the number of sensors. In this paper, we apply the binary particle swarm optimization to the problem of selecting k sensors from a set of m sensors for the purpose of minimizing the error in parameter estimation. In addition to applying the general binary particle swarm optimization(BPSO) to the sensor selection problem, we also present a specific improvement to this population heuristic algorithm. The proposed BPSO for the sensor selection problem is computationally efficient, and its performance is verified through simulation results.In the BPSO for sensors management there is only one objective function. Usually we need to solve multiple optimization problems. In this paper, we propose multiple objective particle swarm optimization algorithm for target tracking in wireless sensor network by formulating it as a multiobjective optimization. At each time of tracking, we obtain tradeoff solutions between two conflicting objectives: minimization of the number of selected sensors and minimization of CPCRLB. Simulation results show that the sensor strategy achieves good estimation performance by significantly decreasing the number of selected sensors.更多还原