基于改进蚁群算法的无线传感器网络路由

为了降低节点能量消耗,延长网络生存的时间,提出了一种改进蚁群算法的无线传感器网络路由机制。首先将无线传感器网络服务质量分为3类,然后利用蚁群算法可以自适应网络状况动态性的优势,构建传感器节点转移函数和信息素更新规则,自适应构建数据路由,最后采用仿真模拟实验对算法性能进行检验。实验结果表明,相对于与现有无线传感器路由算法,本文通过引入蚁群优化机理挖掘传感器节点之间的关联性,数据传输延迟、可靠性和能量开销上具有更好的性能,使整个网络性能保持最优。     

蚁群-遗传算法的无线传感器网络路径优化

为了提高无线传感器网络路径优化效率,快速找到最优路径,提出基于蚁群-遗传算法的传感器路径优化方法.利用遗传算法的快速全局搜索能力和蚁群算法的正反馈机制,实现了两种算法的融合.仿真结果表明,蚁群-遗传算法在时间和性能上都优于单独的蚁群算法和遗传算法,能快速找到无线传感器网络最优路径,有效延长了网络的生命周期.     

基于改进蚁群优化算法的无线传感器网络路由研究

无线传感器网络能够进行传感器的数据收集和传输,为用户提供多元信息,在不同的领域都具有广阔的应用前景,但无线传感网络的路由研究没有动态的拓扑结构以及全地址机制,并且资源有限,因此需要新的无线传感器路由算法进行支持.本文提出了基于改进蚁群优化算法的无线传感器网络路由算法,将改进的蚁群算法的自组织、动态和多路径等特性结合到无线传感网络路由研究中,用仿真实验进行分析表明该算法在网路平均能耗方面的显著改善,并证明其基本满足无线传感器网络的设计目标.     

一种基于无交集节点分组的无线传感器网络覆盖算法

无线传感器网络是一种无线自组织网络,它由大量能量有限的传感器节点组成.能量消耗和网络覆盖是无线传感器网络的两个核心问题,网络覆盖决定了无线传感器网络对物理世界的监测能力,能量消耗则决定了无线传感器网络的生存时间.本文研究了一种改进的基于无交集节点分组算法,针对随机选取节点实现无交集节点分组方式获得的分组个数少且节点通信...     

具有能量和位置意识基于ACO的WSN路由算法

通过融合传感器节点的剩余能量和地理位置信息,设计一种具有传感器节点能量和地理位置意识的基于蚁群优化方法的无线传感器网络路由算法(ELACO);针对路由空洞现象,提出一种路由回退机制,提高了路由搜索成功率.仿真结果表明,ELACO算法具有很高的路由查寻成功率,能够更好地均衡传感器节点能量消耗,从而延长网络使用寿命.     

混合光无线传感网络节能路由算法优化

为解决混合光无线传感网络节点因自身能量耗尽而过早消亡的问题.结合改进的蚁群算法,对一般混合光无线传感网络节能路由算法进行优化.首先给出节能路由算法的假定条件,设置算法具体应用场景,然后构建混合光无线传感器网络通信能耗模型,最后从引入路径度量,改进前向蚂蚁和返回蚂蚁的信息素更新规则着手,提高传统蚁群算法收敛速度,以减少能...     

WSN中动态自适应蚁群路由算法

路由问题是无线传感器网络的核心问题,采用动态自适应调整信息素的蚁群算法,克服了基本蚁群算法的搜索时间过长、易于陷于局部最优解等缺点;同时对无线传感器网络存在的节点能量、传输延时、网络生命周期等问题有明显的改善与提高;最后通过实验仿真证实了该算法的有效性.     

基于BWAS的无线传感器网络静态分簇路由算法

为提高路径搜索效率,避免动态分簇较多的能量消耗,提出了基于最优-最差蚂蚁系统(BWAS)的无线传感器网络静态分簇路由算法.BWAS是对蚁群算法的改进,在路径搜寻过程中评价出最优最差蚂蚁,引入奖惩机制,加快了路径搜索速度.通过无线传感器网络静态分簇、簇内动态选举簇头,在簇头节点间运用BWAS算法搜寻从簇头节点到汇聚节点的多跳最优路径,能减少路径寻优能量消耗,实现均衡能量管理,延长网络寿命,且具有较强的鲁棒性.通过与基于BWAS的动态分簇和基于蚁群算法的动态分簇路由的仿真实验相比较,证实了本算法的有效性.     

基于改进蚁群算法的传感路由低负载方法

无线多媒体传感网络采用无线信号完成通信,会受到网络带宽的限制,实时传递能量较低,限制多媒体数据的传输。提出一种基于改进蚁群算法的无线多媒体传感网络的路由结构优化方法,根据改进蚁群算法,设计无线多媒体传感网络路由模块的硬件构造,改进无线多媒体传感网络路由优化过程,通过改进基本蚁群算法中的转移概率模型,对蚁群移动概率进行完善,确保负载的均衡,实现路由结构优化设计。实验结果说明,所提方法对无线多媒体网络中的路由结构进行改进后,均衡了网络能量消耗,使得无线传感网络节点的平均剩余能量更多,提高无线多媒体传感网络的运转周期。     

基于混合机制算法的WSN节能优化策略

在WSN(无线传感器网络)节能优化过程中,覆盖模型复杂、抽象难以求解.部分节点利用率与覆盖率存在差异性,从而制约无线传感器网络,使能耗降低.针对此问题,提出一种基于混合机制的算法,利用分治思想将节点分为活跃节点和非活跃节点;将部分覆盖算法和唤醒机制算法相结合,建立最优覆盖模型,然后进行求解,得到最优节能方案.该方法能够充分利用节点的不同特点降低算法复杂度,避免不必要的运算消耗.实验表明:混合机制算法分别降低了活跃节点与非活跃的节点能耗;与单一算法相比较效果显著,网络生命周期得到延长.     

基于传输距离和Sink移动的扩延网络寿命算法

在无线传感网络WSN(Wireless Sensor Network)中,传感节点通常以多跳方式向信宿Sink传输感测数据.由于邻近信宿Sink的传感节点需要承担数据转发的任务,比其他节点消耗更多的能量,缩短了网络寿命.为此,提出一种扩延网络寿命的新算法,记为NLTA(Network LifeTime Augmentation).NLTA算法采用了节点传输距离自适应调整和信宿Sink移动两个策略.节点依据能量情况,调整传输距离,减少能量消耗,然后根据路径容量值,调整Sink的位置,平衡网内的节点能量消耗,避免信宿Sink的周围节点能量过度消耗.仿真结果表明,提出的NLTA方案能够有效地提高网络寿命.     

Adaptive directed evolved NSGA2 based node placement optimization for wireless sensor networks

Wireless sensor network (WSN) is a wireless network composed of a large number of static or mobile sensors in a self-organizing and multi-hop manner. In WSN research, node placement is one of the basic problems. In view of the coverage, energy consumption and the distance of node movement, an improved multi-objective optimization algorithm based on NSGA2 is proposed in this paper. The proposed algorithm is used to optimize the node placement of WSN. The proposed algorithm can optimize both the node coverage and lifetime of WSN while also considering the moving distance of nodes, so as to optimize the node placement of WSN. The experiments show that the improved NSGA2 has improvements in both searching performance and convergence speed when solving the node placement problem.

     

Optimization Based Multi-Objective Weighted Clustering For Remote Monitoring System in WSN

Wireless Sensor Network (WSN) is generally considered as a standout amongst the most critical advancements for the twenty-first century, it normally comprises multifunctional wireless sensor nodes, with detecting, communications, and calculation capacities. Clustering the random nodes in WSN is a challenging task with high performance. This paper presents the new clustering model to monitor the eco-friendly mobile network by clustering the sensor nodes and to enhance the Quality of Service of that optimal network in WSN. The proposed Multi-Objective Weighted Clustering model groups the arbitrary nodes and afterward the optimal network is achieved by the optimization of network parameters. For optimizing the network parameters, a metaheuristic algorithm i.e. Improved Fruit Fly Optimization is introduced. With the goal of assessing the Coverage Efficiency (CE) and network user satisfaction of the accomplished optimal mobile network in WSN, the remote sensor monitoring process is applied. Sensor monitoring helps to know the network users and furthermore to improve the CE of WSN, contrasted with existing work.

     

A Cognitive Energy Efficient and Trusted Routing Model for the Security of Wireless Sensor Networks: CEMT

There are many smart applications evolved in the area of the wireless sensor networks. The applications of WSNs are exponentially increasing every year which creates a lot of security challenges that need to be addressed to safeguard the devices in WSN. Due to the dynamic characteristics of these resource constrained devices in WSN, there must be high level security requirements to be considered to create a high secure environments. This paper presents an efficient multi attribute based routing algorithm to provide secure routing of information for WSNs. The work proposed in this paper can decrease the energy and enhances the performance of the network than the currently available routing algorithm such as multi-attribute pheromone ant secure routing algorithm based on reputation value and ant-colony optimization algorithm. The proposed work secures the network environment with the improved detection techniques based on nodes’ higher coincidence rates to find the malicious behavior using trust calculation algorithm. This algorithm uses some QoS parameters such as reliability rate, elapsed time to detect impersonation attacks, and stability rate for trust related attacks, to perform an efficient trust calculation of the nodes in communication. The outcome of the simulation show that the proposed method enhances the performance of the network with the improved detection rate and secure routing service.

     

Weighted relay node placement for wireless sensor network connectivity

In wireless sensor networks (WSN), which are composed of unreliable sensor nodes, preserving the connectivity is a serious problem and one of the most effective solutions of this problem is to deploy powerful relay nodes (RN). The location of the RN is an important parameter for the network performance. In this paper, we investigate relay node placement (RNP) problem on a weighted terrain structure to satisfy WSN connectivity. Contrary to the existing studies, instead of minimizing the number of RN, the main objective of weighted RNP is to minimize the total weight of the points on which RN are deployed. In order to solve the weighted RNP problem, a mathematical formulation is proposed to find the optimal solution. However, because of the NP-complete nature of the problem, a polynomial time heuristic algorithm is also developed. Performance results show that the proposed heuristic algorithm can find near-optimal solutions in a reasonable time bound.     

基于边框定界的WSN分布式全搜索定位算法

提出一种基于边框定界的WSN分布式全搜索定位算法。该算法通过节点测距得到邻居节点的坐标和距离信息,然后通过边框定界方法确定节点存在的位置区域,最后将位置区域网格化,并用全搜索方法在该区域搜索最佳估计点,最佳估计点的坐标即为节点的定位坐标。该算法应用到网络时需运行多轮,通过逐步求精得到节点的定位坐标。仿真实验表明该算法达到当前其他复杂定位算法的性能。     

An energy efficient hole repair node scheduling algorithm for WSN

A sensor node in the wireless sensor network has limited energy and it normally cannot be replaced due to the random deployment, so how to prolong the network life time with limited energy while satisfying the coverage quality simultaneously becomes a crucial problem to solve for wireless sensor networks (WSN). In this work, we propose an energy efficient algorithm based on the sentinel scheme to reduce the sleeping node detection density by defining a new deep sleeping state for each sensor node. The average energy consumed by probing neighboring nodes is introduced as a factor to calculate the detection rate. In addition, after some theoretical analysis of the existence of coverage holes in WSN, a triangle coverage repair procedure is defined to repair coverage holes. Simulation results show that our proposed algorithm obtained better performance in terms of the coverage quality and network life time compared with some existing algorithms in the literature.     

Sling-shot spider optimization algorithm based packet length control in wireless sensor network and Internet of Things-based networks

Wireless sensor network (WSN) is a well-developed domain suitable in the optimal collection and processing of information needed for the present world. For processing and transmitting the correct data by the network, it is affected by many disrupting factors like interference, battery life, distance between the nodes, and redundant data. There have been many methods proposed in the past. Among all the disrupting factors, routing has been the most studied problem in the WSN literature, whereas packet length sizing has been the most untouched topic compared to routing. Sizing the packet lengths to transmit it through the network is important to get error-free data and run an energy-efficient network. In this paper, we propose a sling-shot spider optimization (S²SO) algorithm for packet length optimization in WSN and Internet of Things (IoT)-based networks. The S²SO algorithm is developed based on the spider's unique behavior of catching prey. The proposed algorithm is implemented in three types of networks: 1-hop, 2-hop, and multi-hop networks. A mathematical model for the communication channel, energy efficiency, and energy consumption is developed for all three types of networks. The proposed packet length optimization model is simulated in MATLAB software and compared with conventional bio-inspired algorithms. The results show that the proposed algorithm is very fast in finding the optimal results and transmits optimal packet size with low error rate of 0.2 p.u. and a high efficiency of 98%.