WSN粒子群覆盖优化算法研究

发布时间：2018-07-17 18:14

【摘要】：无线传感器网络(WSN)是目前在国内外备受关注,涉及多学科且知识高度交叉、集成的前沿热点研究领域。它实现了将传输网络与客观世界的信息有效地连接在一起,为下一代网络提供最直接、最有效、最真实的信息。在WSN的研究中,覆盖问题是大量传感器节点部署在监控区域内首先遇到的问题,而如何选取高效的覆盖算法及控制策略对WSN中节点进行优化部署,在很大程度上影响了节点能量的有效利用,网络的感知能力、服务质量以及被传感器节点所覆盖的区域范围也可以得到大幅度地提升,从而延长了网络的生存时间。本论文主要针对WSN部署中节点的覆盖问题展开研究,以粒子群和细菌觅食算法作为理论研究基础,建立了相关的WSN覆盖优化模型,提出了改进的细菌觅食算法与粒子群算法相结合的覆盖优化算法。论文主要内容和研究成果如下:(1)首先对WSN覆盖算法的收敛性能展开了研究。由于标准粒子群(PSO)算法在WSN覆盖部署中存在粒子收敛速度慢、粒子易陷入局部最优值而使算法出现“早熟”现象以及粒子种类单一等缺陷。针对上述问题,论文对标准的细菌觅食(BFO)算法进行改进:在趋向操作步骤中,通过自适应的改进细菌的游动步长,使细菌更加准确快速的搜索到目标;在复制操作中,加入分布估计算法,以增加细菌种类的多样性。进一步地,在改进的BFO算法基础上,提出了粒子群-细菌觅食(PSO-BFO)优化算法,即PSO算法完成对区域的全局搜索、记忆个体和群体的信息,而区域的局部搜索则由改进的BFO算法的趋向和聚集操作完成。仿真结果表明,所提优化算法在粒子的收敛速度以及寻优效率方面,明显优于标准粒子群和细菌觅食算法,从而保证了算法在WSN部署中的有效性。(2)针对随机高密度传感器节点的网络环境所导致的节点监测区域易出现覆盖重叠区及覆盖盲区等问题,论文以最大化WSN网络覆盖率和最少的节点部署个数为覆盖优化目标,对提出的粒子群-细菌觅食(PSO-BFO)覆盖优化算法与单一的PSO和BFO算法进行了覆盖率性能的比较。仿真结果显示,提出的覆盖优化算法能够以较少的部署节点获得较高的WSN网络覆盖率,较好地维持了网络的稳定性、有效延长了WSN网络生存时间。最后,对论文所做工作进行总结,提出研究发展方向。
[Abstract]:Wireless sensor network (WSN) is a hot research field in the field of multi-disciplinary, highly cross-knowledge and integration, which has attracted much attention at home and abroad. It can effectively connect the transmission network with the information of the objective world, and provide the most direct, effective and truest information for the next generation network. In the research of WSN, the coverage problem is the first problem that a large number of sensor nodes are deployed in the monitoring area, and how to select an efficient coverage algorithm and control strategy to optimize the deployment of WSN nodes. To a large extent, the effective utilization of node energy is affected, and the perception ability, quality of service and the area covered by sensor nodes can be greatly improved, thus prolonging the lifetime of the network. In this paper, the coverage of nodes in WSN deployment is studied. Based on particle swarm optimization and bacterial foraging algorithm, the related WSN coverage optimization model is established. An improved coverage optimization algorithm combining bacterial foraging algorithm and particle swarm optimization algorithm is proposed. The main contents and research results are as follows: (1) the convergence performance of WSN coverage algorithm is studied firstly. Due to the slow convergence rate of standard particle swarm optimization (PSO) algorithm in WSN coverage deployment, the particle is prone to fall into local optimal value, which makes the algorithm appear premature phenomenon and single particle category. In order to solve the above problems, the paper improves the standard bacterial foraging (BFO) algorithm: in the trend operation step, the bacteria can find the target more accurately and quickly through the adaptive improvement of the bacterial walk size; in the reproduction operation, the bacteria can find the target more accurately and quickly. Distribution estimation algorithm is added to increase the diversity of bacteria species. Furthermore, based on the improved BFO algorithm, a particle swarm optimization algorithm (PSO-BFO) is proposed, that is, the PSO algorithm completes the global search of the region and memorizes the information of individuals and populations. The local search of the region is accomplished by the orientation and aggregation operation of the improved BFO algorithm. Simulation results show that the proposed optimization algorithm is superior to standard particle swarm optimization and bacterial foraging algorithm in particle convergence speed and optimization efficiency. This ensures the effectiveness of the algorithm in WSN deployment. (2) because of the network environment of random high-density sensor nodes, the node monitoring area is prone to cover overlapped areas and blind areas, etc. The coverage performance of the proposed PSO-BFO coverage optimization algorithm is compared with that of a single PSO and BFO algorithm with the goal of maximizing the WSN coverage and the minimum number of nodes deployed. Simulation results show that the proposed coverage optimization algorithm can obtain higher WSN coverage with fewer deployment nodes, maintain the stability of the network, and effectively prolong the lifetime of WSN networks. Finally, the paper summarizes the work done, and puts forward the direction of research and development.
【学位授予单位】：兰州交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN929.5;TP212.9

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