基于PSO的无线多媒体传感器网络覆盖增强算法

在无线多媒体传感器网络(Wireless Multimedia Sensor Networks,WMSNs)中,由于节点部署的不合理,往往存在较多的监控盲区,影响了网络的服务质量。为了提高网络的覆盖率,在有向感知模型基础的基础上,提出了一种基于粒子群算法的WMSNs覆盖增强算法PSOCE。PSOCE算法以网络覆盖率为优化目标,以粒子群算法为计算工具,同时对节点的位置与主感知方向进行调整。仿真试验表明,PSOCE算法能够有效地改进WMSNs的覆盖质量,网络的覆盖率能提高6%～12%。     

无线多媒体传感器网络部署控制算法

已有的无线多媒体传感器网络（WMSNs）研究针对传感器放置在目标区域内的情况进行,且没有考虑节点通过云台的转动获得的整个可能感知区域对覆盖率的影响。本文首先针对节点放置点高于目标区域的应用进行研究,综合考虑节点感知区域和可能感知区域,建立了延时和无延时感知模型,并针对不同的感知模型提出了传感器网络部署控制算法（IVPDCA）,算法中改进了虚拟势场算法,定义了节点质量的概念来表示节点间覆盖重叠的大小,建立受力模型,使得节点在合力作用下进行重新部署,同时关闭冗余节点,既延长了网络寿命,又提高了区域覆盖率。仿真结果验证了算法的有效性。     

基于Voronoi的无线传感器网络覆盖控制优化策略

针对无线传感器网络运行状态中存在覆盖空洞的问题,提出了一种基于Voronoi有效覆盖区域的空洞侦测修复策略。该策略以满足一定网络区域覆盖质量为前提,在空洞区域内合理增加工作节点以提高网络覆盖率为优化目标,采用几何图形向量方法对节点感知范围和Voronoi多边形的位置特性进行理论分析,力求较准确地计算出空洞面积,找寻最佳空洞修复位置,部署较少的工作节点保证整个网络的连通性。仿真结果表明,该策略能有效地减少网络总节点个数和感知重叠区域,控制网络中冗余节点的存在,同时其收敛速度较快,能够获得比现有算法更高的目标区域空洞修复率,实现网络覆盖控制优化.     

基于拟物力导向的量子粒子群优化算法的WSN节点部署研究

节点部署是无线传感器网络(Wireless Sensor Network,简称WSN)设计的一个重要方面,它将会影响网络的有效覆盖,连通性和能耗。粒子群算法(Particle Swarm Optimization,简称PSO)可以提高目标区域无线传感器网络的覆盖率。然而该算法在优化过程中易早熟收敛,影响覆盖的优化效果,并且算法复杂度较高。针对该问题文章在量子粒子群算法(Quantum Particle Swarm Optimization,简称QPSO)的基础上,结合拟物力导向的思想,提出了基于拟物力导向的量子粒子群优化算法。通过仿真实验得出,该算法加快了粒子的收敛速度,提高了WSN的覆盖率,同时算法的复杂度降低。     

一种新的异构无线传感网覆盖优化算法

针对异构传感器节点在网络初期部署中产生大量覆盖面积冗余的问题，结合相关几何图形知识，以提高网络覆盖率、改善节点分布均匀度为优化目标，提出一种基于节点定向移动来减少节点两两之间覆盖冗余面积的网络覆盖优化算法。算法预先设立判定门限，通过判定两两节点之间覆盖冗余面积与设定门限的大小关系，对节点实施有向性偏移，逐一减少节点之间的覆盖冗余面积。理论分析与实验仿真证明，该算法能够有效提高异构传感器网络部署中的覆盖率，优化节点分布均匀度将近8.7，尤其在大型传感器网络的节点部署中具有极强实用性。     

基于改进粒子群算法的红外WSN覆盖研究北大核心CSCD

为了提高红外无线传感器网络覆盖效果,采用改进粒子群算法。首先建立红外无线传感器网络覆盖模型,包括无线传感器的覆盖率、剩余能量率、利用率;接着对粒子进行方向感知,粒子的适应度函数值与自身当前最佳值比较,获得不同的更新速度与位置,为了避免进化过程中受方向感知粒子过度引导,自适应感知因子在算法运行初期设置较大值,而在算法运行后期设置较小值;最后构建粒子群适应度函数,给出了算法流程。实验显示本文算法增加了覆盖率,减少了功耗,同时也降低了传感器节点利用率,不同场景下本文算法评价指标较优。     

基于改进PSO的WMSNs覆盖增强算法

对无线多媒体传感器网络(WMSNs)的覆盖增强问题进行了研究.在WMSNs网络中,视频、图像节点的视角范围有限,只能监控周围的部分区域.由于节点数量众多、部署方式受限等原因,网络中往往存在大量的监测重叠与监控盲区,需要对各节点的感知方向进行优化,以提高网络的监控质量.文中基于有向感知模型,提出了一种覆盖增强算法MCE.MCE对各节点的感知方向进行调整,并使用了改进的PSO算法来计算求解.仿真实验表明,MCE算法能够有效地提高网络的覆盖率.     

一种面向三维感知的无线多媒体传感器网络覆盖增强算法

 覆盖作为无线传感器网络中的基础问题直接反映了网络感知服务质量.本文在分析现有无线多媒体传感器网络覆盖增强算法的基础上,构建节点三维感知模型,提出面向三维感知的多媒体传感器网络覆盖增强算法(Three-Dimensional Perception Based Coverage-Enhancing Algorithm,TDPCA).该算法将节点主感知方向划分为仰俯角和偏向角,并根据节点自身位置及监测区域计算并调整各节点最佳仰俯角,在此基础上基于粒子群优化调整节点偏向角,从而有效减少节点感知重叠区及感知盲区,最终实现监测场景的区域覆盖增强.仿真实验表明:对比已有的覆盖增强算法,TDPCA可有效降低除节点感知重叠区和盲区,最终实现网络的高效覆盖.     

面向目标跟踪的物联网感知层节点部署优化研究

针对目标跟踪物联网感知层节点动态部署的特点,在人工鱼群算法和虚拟力算法的基础上,设计了融入虚拟力影响的人工鱼群控制算法,给出了算法的参数自适应调整策略,该算法利用节点间的虚拟力来影响人工鱼的觅食行为和追尾行为,指导人工鱼群的进化过程,加快算法的收敛性。仿真实验结果显示,算法能快速有效地实现无线传感器网络节点的部署优化,与人工鱼群算法和虚拟力算法相比,该算法不仅全局寻优能力强,且收敛速度快,可有效提高网络覆盖率,优化网络性能。     

基于改进萤火虫算法的WSN覆盖优化

针对无线传感器网络节点部署不均所导致的网络覆盖率较低问题,以无线传感器网络覆盖率最大化为目标,提出一种基于改进萤火虫算法(IFA)的网络覆盖优化方法。该方法运用佳点集方法初始化种群,提高种群的多样性,奠定全局搜索基础;利用具有非线性指数递减的变形Sigmoid函数作为惯性权重,平衡算法的全局搜索和局部开发能力;采用高斯扰动策略对个体位置扰动更新,避免算法早熟。仿真结果表明,该算法与人工鱼群算法(AFSA)、种子杂交粒子群算法(HSPSO)和混沌萤火虫算法(CGSO)相比,能有效提高网络覆盖率,使节点部署分布更均匀。     

Performance evaluation of sensor deployment using optimization techniques and scheduling approach for K-coverage in WSNs

In the wireless sensor networks, sensor deployment and coverage are the vital parameter that impacts the network lifetime. Network lifetime can be increased by optimal placement of sensor nodes and optimizing the coverage with the scheduling approach. For sensor deployment, heuristic algorithm is proposed which automatically adjusts the sensing range with overlapping sensing area without affecting the high degree of coverage. In order to demonstrate the network lifetime, we propose a new heuristic algorithm for scheduling which increases the network lifetime in the wireless sensor network. Further, the proposed heuristic algorithm is compared with the existing algorithms such as ant colony optimization, artificial bee colony algorithm and particle swarm optimization. The result reveals that the proposed heuristic algorithm with adjustable sensing range for sensor deployment and scheduling algorithm significantly increases the network lifetime.     

Distributed Energy Optimization for Target Tracking in Wireless Sensor Networks

Energy constraint is an important issue in wireless sensor networks. This paper proposes a distributed energy optimization method for target tracking applications. Sensor nodes are clustered by maximum entropy clustering. Then, the sensing field is divided for parallel sensor deployment optimization. For each cluster, the coverage and energy metrics are calculated by grid exclusion algorithm and Dijkstra's algorithm, respectively. Cluster heads perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric. Particle filter is improved by combining the radial basis function network, which constructs the process model. Thus, the target position is predicted by the improved particle filter. Dynamic awakening and optimal sensing scheme are then discussed in dynamic energy management mechanism. A group of sensor nodes which are located in the vicinity of the target will be awakened up and have the opportunity to report their data. The selection of sensor node is optimized considering sensing accuracy and energy consumption. Experimental results verify that energy efficiency of wireless sensor network is enhanced by parallel particle swarm optimization, dynamic awakening approach, and sensor node selection.     

Connectivity constrained wireless sensor deployment using multiobjective evolutionary algorithms and fuzzy decision making

Deployment of sensor nodes is an important issue in designing sensor networks. The sensor nodes communicate with each other to transmit their data to a high energy communication node which acts as an interface between data processing unit and sensor nodes. Optimization of sensor node locations is essential to provide communication for a longer duration. An energy efficient sensor deployment based on multiobjective particle swarm optimization algorithm is proposed here and compared with that of non-dominated sorting genetic algorithm. During the process of optimization, sensor nodes move to form a fully connected network. The two objectives i.e. coverage and lifetime are taken into consideration. The optimization process results in a set of network layouts. A comparative study of the performance of the two algorithms is carried out using three performance metrics. The sensitivity analysis of different parameters is also carried out which shows that the multiobjective particle swarm optimization algorithm is a better candidate for solving the multiobjective problem of deploying the sensors. A fuzzy logic based strategy is also used to select the best compromised solution on the Pareto front.     

Static wireless sensor networks deployment using an improved binary PSO

A major issue in designing wireless sensor networks is the deployment problem. Indeed, many performances of the sensor network, such as coverage, are determined by the number and locations of deployed sensors. This paper reviews existing deterministic deployment strategies and devises a modified binary particle swarm optimization, which adopts a new position updating procedure for a faster convergence and exploits the abandonment concept to avoid some drawbacks such as premature convergence. The devised approach combines, in a meaningful way, the characteristics of the binary particle swarm optimization with the wireless sensor networks deployment requirements in order to devise a lightweight and efficient sensor placement algorithm. The effectiveness and efficiency of the proposed approach are evaluated through extensive simulations. The obtained results show that the proposed algorithm outperforms the state‐of‐the‐art approaches, especially in the case of preferential coverage. Copyright © 2015 John Wiley & Sons, Ltd.     

Maximal coverage hybrid search algorithm for deployment in wireless sensor networks

A vital design aspect in the setting up of a wireless sensor network is the deployment of sensors. One of the key metrics of the quality of deployment is the coverage as it reflects the monitoring capability of the network. Random deployment is a sub-optimal method as it causes unbalanced deployment and requires sensors in excess of the planned deployment to achieve the same level of coverage. To achieve maximum coverage with a limited number of sensors, planned deployment is a preferred choice. Maximizing the coverage of the region of interest with a given number and type of sensors is an optimization problem. A novel maximal coverage hybrid search algorithm (MCHSA) is proposed in this paper to solve this problem. The MCHSA is a hybrid search algorithm that achieves the balance between exploration and exploitation by applying the particle swarm optimization as a global search technique and using the Hooke–Jeeves pattern search method to improve the local search. The algorithm starts with a good initial population. The proposed MCHSA has low computational complexity and fast convergence. The performance of the MCHSA is analyzed by performing a comparison with the existing algorithms in the literature, in terms of coverage achieved and number of fitness function evaluations. The paper also discusses the tuning of parameters of the proposed algorithm.