无线传感器网络覆盖与数据融合算法的研究

发布时间：2018-01-23 21:05

本文关键词： 无线传感器网络区域覆盖数据融合遗传算法模糊理论　出处：《太原理工大学》2014年硕士论文　论文类型：学位论文

【摘要】：无线传感器网络是近年来兴起的物联网的重要组成部分,作为一种全新的信息获取和处理技术,已被广泛应用于电子医疗、智能电网、智能家居等多个领域,并深刻地改变着人们的生活方式。目前,无线传感器网络的研究逐渐从软硬件设计、通信协议设计和开发分层架构的初级阶段,进入到面向应用研发的全局解决方案。无线传感器网络部署与覆盖、信息处理与信息融合正在成为研究的重点和热点。然而,现有的研究往往从一般无线自组网络设计的角度出发,没有关注无线传感器网络能量受限、计算和存储能力有限的特点。基于上述考虑,本文针对无线传感器网络应用中的区域覆盖问题和多传感器数据融合问题进行了研究,分别提出了相应的算法。本文的贡献主要有以下几方面：首先,在无线传感器网络布尔感知模型的基础上,提出节点冗余度和覆盖集冗余度的概念,用计算复杂度低、参数可配置的网络局部特征表征网络的全局特征,改进了无线传感器网络覆盖模型。这种覆盖模型实现覆盖集性能的快速计算,可为无线传感器网络通信协议设计、路由协议设计等研究提供参照。其次,在改进网络覆盖模型的基础上,提出了针对区域覆盖问题的RGA-D算法,其利用遗传算法计算覆盖集、同时考虑节点和覆盖集冗余度,对网络整个生存期进行全局优化。仿真实验表明,RGA-D算法能在活跃节点数和网络覆盖率之间达到平衡,解决了网络生存期后期容易出现覆盖盲区的问题。再次,针对现有的数据融合算法中支持度的计算较为复杂的问题,用最小二乘法拟合高斯型支持度函数,充分考虑了无线传感器网络能量受限的特点,减少了数据融合过程中的计算量,能在多项式级计算复杂度下,得到与指数级复杂度的高斯支持度函数非常接近的计算结果。最后,针对无线传感器网络应用最广泛的数值型数据,设计了多传感器数据融合算法TMMDF, TMMDF算法运用Dixon法剔除原始数据中的疏失误差,再运用OLSF支持度函数计算数据集合中各数据关于中值和均值的支持度,最后选取合适三角模融合算子进行融合计算,得到融合结果。TMMDF算法计算复杂度低,融合精度高,仿真实验结果表明TMMDF算法具有较高的精确性和稳健性。
[Abstract]:Wireless sensor network (WSN) is an important part of the Internet of things (IoT), which has been widely used in many fields, such as electronic medicine, smart grid, smart home and so on. As a new information acquisition and processing technology, wireless sensor network (WSN) has been widely used in many fields. And profoundly changing people's way of life. At present, the research of wireless sensor networks is gradually from the initial stage of hardware and software design, communication protocol design and development of hierarchical architecture. Wireless sensor network deployment and coverage, information processing and information fusion are becoming the focus and focus of research. The existing research often starts from the general wireless ad hoc network design angle, does not pay attention to the wireless sensor network energy limit, the computation and the storage ability limited characteristic. Based on the above consideration. In this paper, the area coverage problem and the multi-sensor data fusion problem in wireless sensor networks are studied, and the corresponding algorithms are proposed. The contributions of this paper are as follows: Firstly, based on the Boolean sensing model of wireless sensor networks, the concepts of node redundancy and overlay set redundancy are proposed with low computational complexity. The local feature of the network with configurable parameters represents the global feature of the network and improves the coverage model of the wireless sensor network. This coverage model can quickly calculate the performance of the coverage set. It can provide reference for wireless sensor network communication protocol design, routing protocol design and so on. Secondly, based on the improved network coverage model, a new RGA-D algorithm is proposed, which uses genetic algorithm to calculate the coverage set, and considers the redundancy of nodes and cover sets at the same time. The simulation results show that the RGA-D algorithm can balance the number of active nodes and the network coverage. It solves the problem of covering blind area easily in the later period of network life. Thirdly, aiming at the complicated problem of the support degree calculation in the existing data fusion algorithm, the Gao Si type support function is fitted by the least square method, which fully considers the characteristics of the limited energy in wireless sensor networks. The computational complexity of the data fusion process is reduced, and the result is very close to the Gao Si support function of exponential complexity under the polynomial complexity. Finally, a multi-sensor data fusion algorithm, TMMDF, is designed for the most widely used numerical data in wireless sensor networks. TMMDF algorithm uses the Dixon method to eliminate the errors in the original data, and then uses the OLSF support function to calculate the support of the median value and the mean value of the data set. Finally, the suitable triangle mode fusion operator is selected for fusion calculation, and the fusion results. TMMDF algorithm has low computational complexity and high fusion accuracy. Simulation results show that the TMMDF algorithm has high accuracy and robustness.
【学位授予单位】：太原理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN929.5;TP212.9

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