自适应逃逸粒子群算法的网络节点覆盖优化仿真

研究无线传感器节点优化选择。传统LEACH分簇算法中,节点选择的随机性很大,没有很好地参考节点中的多个属性,通信的簇头分布也无规律,算法把能量消耗分摊到所有的节点上,一旦选择边沿节点作为簇头,一些节点必须经过长距离的路由转发才能到达簇头,造成通信效率较低。为了避免上述缺陷,提出了一种基于自适应逃逸粒子群算法的网络节点覆盖优化方法。建立自适应逃逸粒子群算法的数学模型,准确描述网络节点覆盖问题。利用自适应逃逸粒子群方法,计算无线传感网络节点最优位置,从而实现网络节点覆盖优化。实验结果表明,这种算法能够实现网络节点覆盖优化处理,从而提高无线传感网络数据传递的效率。     

基于能量收集技术的无线传感网络路由协议分析仿真

本文将无线传感网络的能量采集模型进行综述,并对无线传感网络的两种路由协议进行仿真与比较,LEACH路由协议和HEED路由协议进行分析比较,HEED协议是基于LEACH协议的一种改进协议。LEACH协议没有能量意识,HEED在簇头选举阶段引入能量因素,节点生存周期明显有所延长。     

基于混沌粒子群与蚁群算法的WSN路由协议

为了降低无线传感器网络（WSN）能量消耗,延长网络生存周期,提出了一种基于混沌粒子群（CPSO）和蚁群算法相结合的路由协议。该协议针对典型的分簇协议LEACH（Low-Energy Adaptive Clustering Hierarchy）协议的簇头选择进行了优化,考虑了节点剩余能量和簇内密集性等因素,采用新的混沌粒子群算法对簇头选择进行优化。然后,针对LEACH协议簇头到基站采用单跳通信,容易使簇头早亡的问题,采用蚁群算法优化簇头到基站的路由路径,减少通信消耗的能量。仿真结果表明,与传统的LEACH协议相比,新的协议能有效减少能量消耗,延长网络生命周期。     

基于LEACH的能量节省路由协议的研究

无线传感器网络由于节点能量的限制,在组网中必须要充分考虑到节点能量的情况,以避免由于节点的能量耗尽,造成网络分割。针对经典无线传感器网络协议LEACH没有考虑节点能量的情况,提出了一种基于LEACH的改进协议。该协议实现了对簇头选择的优化,以及对簇头和基站通信路径的优化。OPNET仿真实验结果表明,该协议能有效地延长网络的生命周期,推迟网络分割的出现。     

基于遗传算法和蚁群算法的LEACH改进协议

针对无线传感器网络低功耗自适应集簇分层(Low Energy Adaptive Clustering Hierarchy, LEACH)路由协议因能耗不均衡导致节点过早死亡的问题,提出了一种基于遗传算法和蚁群算法改进的LEACH路由协议。在分簇阶段,通过遗传算法选举合理的簇头节点并根据节点的分布划分簇群;在数据传输阶段,通过蚁群算法使簇头节点尽可能选择能量充足且距离较短的路径进行数据传输。仿真结果表明,与传统的分簇路由协议LEACH和LEACH-C相比,改进算法可以使网络的能量消耗更加均衡,并延长网络的生命周期。     

一种基于时间延迟机制的分布式成簇算法研究

无线传感器网络能够在恶劣的环境下获取大量详实而可靠的信息,可以广泛应用于国防军事、工业控制、环境监测、交通管理、医疗保健、智能家居等各个领域.但是,传感器节点一般只靠电池供电,有效的利用传感器节点的能量是传感网络通信协议设计的重要目标.提出了一种基于定时器策略的具有能量效率的分布式成簇算法,称为LEACH-T算法.该算法主要包括两部分内容:一是簇头选择时引入定时器策略,使得能量较大的节点具有更高的概率成为簇头;二是引入竞争机制,使得簇头均匀分布,进而能有效平衡簇头节点的能量使用情况;三是构造了基于能量和距离的花费函数以平衡节点的能量效率.理论和仿真结果均说明该算法优于LEACH,生命周期比LEACH延长达到40%.     

无线传感网络布局的虚拟力导向微粒群优化策略

无线传感网络通常由固定传感节点和少量移动传感节点构成,动态无线传感网络布局优化有利于提高无线传感网络覆盖率和目标检测概率,是无线传感网络研究的关键问题之一.传统的虚拟力算法在优化过程中容易受固定传感节点的影响,无法实现全局优化.本文结合虚拟力算法和微粒群算法,提出一种面向无线传感网络布局的虚拟力导向微粒群优化策略.该策略通过无线传感节点间的虚拟力影响微粒群算法的速度更新过程,指导微粒进化,加快算法收敛.实验表明,虚拟力导向微粒群优化策略能快速有效地实现无线传感节点布局优化.与微粒群算法和虚拟力算法相比,虚拟力导向微粒群优化策略不仅网络覆盖率高,且收敛速度快,耗时少.     

无线传感器网络分簇路由协议研究

经典的分簇路由协议LEACH在无线传感器网络中有着非常广泛的应用,针对LEACH协议在成簇时没有考虑簇头节点的能量等因素的缺陷,为延长网络生存期在LEACH协议基础之上,在簇头选择公式中加入节点剩余能量的参数,使得选举能量较高的节点为簇头,提出改进的LEACH协议。并在网络模拟软件NS2平台下,对改进的LEACH协议与原LEACH协议进行仿真比较分析,结果表明改进后的协议中各个节点能耗均衡,避免个别节点过早衰亡,可以延长网络生存期。     

一种基于LEACH协议的改进算法

作为能量有效、基于层次结构的路由协议的典型代表,LEACH协议存在的不足表现为网络中会出现极大簇和极小簇并存;当节点能量不同时簇头选择不合理;在簇头死亡后簇内节点仍会消耗能量.本文基于LEACH提出了一种改进算法,使用引入簇成员数门限和合并极小簇的方法避免极大簇和极小簇同时存在;通过对簇头能量消耗的估计,在簇头能量耗尽...     

一种半集中式低能耗自适应无线传感器网络成簇算法

基于对LEACH等算法的研究,提出一种半集中式,综合利用节点位置信息与剩余能量的无线传感器网络分簇及簇头选举算法。利用节点位置信息,在簇头选举阶段和传感数据传输阶段使用不同的拓扑划分,在尽可能选取剩余能量较高的节点作为簇头的前提下,能够既保证簇均匀分布,又尽量做到簇头在簇内处于相对中心位置,并且避免了成簇阶段的碰撞。仿真结果表明,该算法有效延长了网络生存周期,收集了更多的传感数据,并且适合大范围覆盖的传感器网络。     

Sandpiper optimization algorithm with cosine similarity based cross-layer routing protocol for smart agriculture in wireless sensor network assisted internet of things systems

The Internet of Things (IoT) has recently attained a prominent role in enabling smooth and effective communication among various networks. Wireless sensor network (WSN) is utilized in IoT to collect peculiar data without interacting with humans in specific applications. Energy is a major problem in WSN-assisted IoT applications, even though better data communication is achieved through cross-layer models. This paper proposes a new cross-layer-based clustering and routing model to provide a scalable and energy-efficient long data communication in WSN-assisted IoT systems for smart agriculture. Initially, the fuzzy k-medoids clustering approach is used to split the network into various clusters since the formation of clusters plays an important role in energy consumption. Then, a new swarm optimization known as enhanced sparrow search algorithm (ESSA), which is the combination of SSA and chameleon swarm algorithm (CSA), has been introduced for optimal cluster head (CH) selection to solve the energy-hole problems in WSN. A cross-layer strategy has been preferred to provide efficient data transmission. Each sensor node parameter of the physical layer, network layer and medium access control (MAC) is considered for processing routing. Finally, a new bio-inspired algorithm is known as the sandpiper optimization algorithm (SOA), and cosine similarity (CS) has been employed to determine the optimal route for efficient data transmission and retransmission. The simulation of the proposed protocol is implemented by network simulator (NS2), and the simulation results are taken in terms of end-to-end delay, PDR, communication overhead, communication cost, average consumed energy, and network lifetime.     

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.     

基于自适应噪声抵消的无线传感网络信道优化

无线传感器网络通信技术广泛应用在物联网近场通信、水声通信等领域。无线传感网络通信信道受到多途干扰,导致信道失衡,需要进行信道均衡模型设计。提出一种基于自适应噪声抵消的无线传感器网络通信信道优化技术,首先构建了无线传感器网络通信的信道模型,对无线传感器网络信道传播过程中衰减损失和各条路径的信号进行重组,采用自适应噪声抵消算法进行信道的多途干扰滤波,结合最小二乘（ RLS）准则算法进行无线传感器网络通信信道均衡设计。仿真结果表明,采用该通信信道均衡技术能有效提高无线传感器网络通信的信道质量,降低通信传输失真和误比特率,实现信道自适应均衡,提高通信的抗干扰能力。     

Two-level Noise Robust and Block Featured PNN Model for Speaker Recognition in Real Environment

Wireless Personal Communications - Underwater Wireless sensor network posses several factors, which causes congestion, issues in the network, like mutual interference, many to one communication,...     

无线传感器网络的体系结构与关键技术

无线传感器网络（WSN）是通过无线通信方式形成的一个多跳自组织网络。它综合了微传感器、微机电系统（MEMS）、嵌入式、网络通信和分布式信息处理等技术,是集信息采集、信息传输、信息处理于一体的智能化信息系统。本文主要介绍了无线传感器网络的体系结构、传感器节点的组成、协议栈和关键技术。     

A Binary PSO Approach for Improving the Performance of Wireless Sensor Networks

Wireless sensor networks are used for low-cost unsupervised observation in a wide-range of environments and their application is largely constrained by the limited power sources of their constituent sensor nodes. Techniques such as routing and clustering are promising and can extend network lifetime significantly, however finding an optimal routing and clustering configuration is a NP-hard problem. In this paper, we present an energy efficient binary particle swarm optimization based routing and clustering algorithm using an intuitive matrix-like particle representation. We propose a novel particle update strategy and an efficient linear transfer function which outperform previously employed particle update strategies and some traditional transfer functions. Detailed experiments confirmed that our routing and clustering algorithm yields significantly higher network lifetime in comparison to existing algorithms. Furthermore, our results suggest that Binary PSO is better equipped to solve discrete problems of routing and clustering than its continuous counterpart, PSO.

     

Design and Analysis of High-Speed Bottleneck-Free One-Dimensional OCDMA Based Gigabit Symmetric PON

Wireless Personal Communications - Wireless sensor network (WSN) is an emerging research field in recent years. The advancement in sensory device and communication technologies has enabled the...     

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。     

Particle-Water Wave Optimization for Secure Routing in Wireless Sensor Network Using Cluster Head Selection

Wireless Personal Communications - Wireless sensor network (WSN) has some great advantages, such as flexible communication, low power consumption, and low cost. In view of the WSN clustering...     

无线传感器网络的新进展与应用

为完成实时监测、感知和采集各种监测环境或对象的信息,将智能传感器组成分布式信息测控网络。无线传感器网络是传感器智能化发展的趋势。它是以数据获取与数据管理为核心的全新网络,综合了传感器技术、嵌入式计算技术、分布式信息处理技术和无线通信技术。介绍了无线传感器网络节点、无线传感器网络、无线传感器网络的最新进展,并给出了无线传感器网络的应用。