基于RSSI的无线传感器网络混合定位算法

发布时间：2018-01-10 07:07

本文关键词：基于RSSI的无线传感器网络混合定位算法　出处：《太原理工大学》2017年硕士论文　论文类型：学位论文

【摘要】：无线传感器网络已经广泛的运用于医疗、军事、智能家居、灾情监测等领域,定位是无线传感器网络中的一项关键技术。在基于RSSI(接收信号强度指示)的测距定位方法中,节点定位的准确性经常会受到传感器节点能量有限、环境干扰等因素的影响,因此需要采用更为优秀的算法来提高节点定位的精度。文中分别对静止节点的定位问题和移动信标节点定位的问题展开了研究。智能算法具有非常优秀的性能并且受到了广泛的关注,可以用来解决定位问题,来提高未知节点的定位精度。文中结合了鸡群算法求解精度高的特点和物质状态搜索算法后期收敛速度快的特点,提出了鸡群物质状态搜索算法,用于节点定位。本文首先研究了静止节点的定位问题,提出了二维及三维的鸡群物质状态混合定位算法,分为初步定位阶段和精确定位阶段。对于初步定位阶段,二维定位方法采用双边节点估计法和特殊节点估计法,三维定位算法采用三面节点估计法和质心算法,可以得到初步定位结果;对于精确定位阶段,采用鸡群物质状态搜索算法对初步定位结果进行优化,得到最终定位结果。经过仿真分析,该算法能以较少的迭代次数优化得到精度较高的定位结果。最后研究了移动信标节点定位的问题,提出了一种基于改进遗传算法的移动信标节点路径规划算法,分为虚拟信标节点的选取阶段和最优移动路径的选取阶段。对于虚拟信标节点选取阶段,可以分为构建节点的邻接矩阵、节点分组、寻找冗余虚拟信标节点组和确定虚拟信标节点等步骤;对于最优路径的选取阶段,采用改进的遗传TSP(旅行商问题)算法进行寻优,引入了物质搜索算法中碰撞检测的思想,增加了种群的多样性,降低了算法陷入局部最优的可能性。经过仿真分析,该算法可以在获得较高的定位覆盖率的情况下使得信标节点移动的距离较短。
[Abstract]:Wireless sensor networks have been widely used in medical, military, smart home, disaster monitoring and other fields. Location is a key technology in wireless sensor networks. In the RSSI (received signal intensity indication) based ranging and location method, the accuracy of node location is often limited by the sensor node energy. Environmental interference and other factors. Therefore, it is necessary to use more excellent algorithms to improve the accuracy of node localization. In this paper, the problem of static node location and mobile beacon node localization is studied respectively. The intelligent algorithm has excellent performance. And has received extensive attention. It can be used to solve the problem of location to improve the location accuracy of unknown nodes. This paper combines the characteristics of high accuracy of chicken swarm algorithm and fast convergence speed of material state search algorithm. In this paper, the problem of static node location is studied, and a two-dimensional and three-dimensional hybrid algorithm is proposed to locate the material state of chicken herd. For the initial positioning stage, the two-dimensional localization method and the special node estimation method are used, while the three-dimensional location algorithm uses the three-sided node estimation method and the centroid algorithm. Preliminary location results can be obtained; For the precise positioning stage, the initial location results are optimized by using the material state search algorithm, and the final location results are obtained. The algorithm can optimize the location results with less iteration times. Finally, the problem of mobile beacon node location is studied, and an improved genetic algorithm for mobile beacon node path planning is proposed. For the selection phase of virtual beacon nodes, it can be divided into the construction of node adjacency matrix, node grouping. Finding redundant virtual beacon node group and determining virtual beacon node etc. For the optimal path selection stage, the improved genetic TSP (traveling Salesman problem) algorithm is used to optimize, and the idea of collision detection in the material search algorithm is introduced, which increases the diversity of the population. The probability of the algorithm falling into local optimum is reduced. The simulation results show that the algorithm can make the beacon node move at a shorter distance under the condition of high localization coverage.
【学位授予单位】：太原理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP212.9;TN929.5

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