一种通过剩余能量过滤进行簇头选举的低能耗无线路由算法

基于LEACH协议提出一种改进的无线传感器网络的自组织路由算法。该算法在原LEACH协议的簇头产生环节做了较大改进,在簇头产生过程中,将当前节点剩余能量与全无线传感器网络节点平均剩余能量进行比较,防止剩余能量小于全网平均剩余能量的节点当选簇头,进一步优化了全网络节点能量消耗的均衡性,有效推迟了节点的死亡时间。通过在簇头选举阶段使用有目的性的筛选取代LEACH的随机选取,实现降低无线传感器网络能耗、延长网络生命周期的目的。通过MATLAB仿真软件进行试验测试,结果表明,改进的算法可以提高无线网络的生命周期,均衡无线网络能量消耗,增加网络吞吐量,有效延迟无线网络节点的死亡时间。     

基于模糊逻辑的无线传感网络分簇路由协议

能量消耗是无线传感器网络中首要考虑的问题. 为了均衡每个节点的能耗, 延长网络节点的生存寿命, 本文提出了一种基于模糊逻辑方法的FLCHE分簇路由协议. 该协议充分考虑了节点的剩余能量, 能量消耗速率以及节点的分布密度. 通过MATLAB实验仿真表明, 相比于经典的LEACH分簇路由协议, FLCHE协议更加均衡了网络节点的能耗, 有效地延长了网络的生命周期, 总体性能优于LEACH算法.     

一种改进的LEACH模型及其仿真分析

为了最大限度地延长无线传感器网络生命周期,对无线传感器网络传统路由算法低功耗自适应聚类LEACH进行改进,改进后的算法命名为LEACH-EC.在广播阶段选取簇头节点时引入高概率选取机制,根据节点的剩余能量和节点的集中度选取簇头节点,选取的簇头节点兼顾了节点剩余能量和节点分布状况.实验结果表明,LEACH-EC算法选取的簇头节点性能较优,能有效地减少簇内节点传输能量消耗.因此,LEACH-EC算法能够均衡无线传感器网络能耗负载,延长无线传感器网络生命周期     

无线传感网络中基于综合因素的分布式路由算法

近年来由于在多方面的广泛应用,无线传感器网络受到了越来越多的关注.然而限于无线传感器网络自身的限制,如何更好地节省能量,仍为无线传感器路由协议设计中面临的主要问题之一.LEACH等基于分簇的路由协议通过成簇来减少能量消耗,但是成簇过程却带来额外消耗以及冗余.对LEACH协议中簇头生成算法进行了研究并提出了改进,提出了基于节点能量、节点距离以及节点度的分布式优化算法,并对优化算法进行分析与仿真.仿真试验表明,基于综合因素的分布式簇头选举算法优化了簇头选举方式和簇头的分布,从而节省了能量消耗,延长了网络生存周期.     

基于能量分布的异构传感器网络分簇算法

针对能量异构的无线传感器网络,提出一种分簇算法。该算法采用基于节点剩余能量分布状况的簇头竞争参数,降低成簇过程中的通信能耗,实现簇头的均匀分布。在簇间综合考虑簇头剩余能量及其与基站的通信能耗,以选择合适的下一跳路由节点。仿真结果表明,该算法可以均衡网络能量消耗,提高节点能量利用效率,延长网络寿命。     

分簇拓扑结构的水情无线传感器网络

针对水情无线传感器的立体空间中的网络分布问题,建立了无线网络数学模型;基于数学模型中节点与节点间的能量扩散模式,利用该模型研究网络中节点数据传输的能量消耗形式,计算出基于本节点能量消耗模型的LEACH算法所要推举最佳簇头个数;通过分析LEACH算法,提出在水情监测环境立体空间中分布节点且节点所储存能量不同情况下LEACH改进算法,采用轮循机制分别在不同能量储备节点中推举簇头,稳定阶段各个簇头采用星型拓扑与汇聚节点进行数据通信,簇成员与簇头进行数据传输;最后,利用MATLAB对改进算法进行了仿真,结果说明改进后的算法使网络所消耗的能量均匀地分布到各个网络节点上,可以可靠地应用到实际水情监测环境中。     

基于LEACH协议节能路由算法的研究与优化

针对LEACH(Low-Energy Adaptive Clustering Hierarchy)协议在分簇算法中存在的未考虑节点的剩余能量,簇头节点分布位置不合理等缺陷,提出基于LEACH协议的节能路由改进算法。在原有协议的基础上,优化了成簇的条件,充分考虑了剩余能量与相对位置,使得无线传感器网络的寿命得到进一步延长。仿真实验结果表明改进后的算法有效降低了网络能耗,提高了无线传感器网络的性能。     

传感器网络中基于节点密度的分布式成簇算法

在分簇路由协议中,延长传感器网络的寿命,很大程度上依赖于簇头节点选择的合理性。提出一种基于传感器节点分布密度的分布式成簇算法,该算法是对LEACH算法的改进,在选取簇头的时候除了考虑节点轮流成为簇头的问题,同时还考虑各节点的分布密度。仿真实验证明,新算法能比LEACH算法更有效地降低网络的能量消耗,均衡网络能耗水平,从而使得传感器网络的生命周期在LEACH算法的基础上有较大提高。     

一种新的基于LEACH的WSN路由算法

研究无线传感器网络路由算法,无线传感器网络由能量有限的节点组成,因此高效节能的路由算法是无线传感器网络组网的基础.针对低功耗自适应分簇(LEACH)路由算法存在簇首节点选择不合理以及簇首节点与基站在远距离通信过程中能量消耗大的不足,提出了一种改进的LEACH路由算法.改进的算法在簇建立阶段的簇首选举过程中,引入节点剩余能量因素,且进行均匀分簇,有效地降低剩余能量较小和位置不佳节点成为簇首的可能性,均衡了网络的能量消耗,在簇稳定工作阶段,节点间的数据传输采用单跳和多跳相结合的通信方式,从而降低网络能耗.仿真结果表明,与传统的LEACH算法相比,改进的LEACH算法能量均衡性更好,并显著地延长了网络的存活时间.     

一种改进的无线传感器网络分簇路由算法

在无线传感器节能优化的研究中,能量约束问题是无线传感器网络网络协议设计最重要的问题.网络设计目标是要高效地使用传感器节点的能量,延长网络的存活时间.针对LEACH协议能耗大,为解决网络存活时间短等缺点,提高可靠性,提出了一种基于能耗均衡的分簇路由算法(IWA).IAW首先对LEACH协议的簇头选举过程进行了改进,把节点剩余能量作为簇头选举的依据,然后簇的形成根据簇所在层次和距离基站的距离实现,从而达到了能量均衡.仿真结果表明,IWA算法有效地节省了簇首的能量消耗,平衡了簇内节点能耗,延长了无线传感器网络的存活时间,使得到的监测结果准确可靠.     

DUCF: Distributed load balancing Unequal Clustering in wireless sensor networks using Fuzzy approach

Data gathering in wireless sensor networks (WSN) consumes more energy due to large amount of data transmitted. In direct transmission (DT) method, each node has to transmit its generated data to the base station (BS) which leads to higher energy consumption and affects the lifetime of the network. Clustering is one of the efficient ways of data gathering in WSN. There are various kinds of clustering techniques, which reduce the overall energy consumption in sensor networks. Cluster head (CH) plays a vital role in data gathering in clustered WSN. Energy consumption in CH node is comparatively higher than other non CH nodes because of its activities like data aggregation and transmission to BS node. The present day clustering algorithms in WSN use multi-hopping mechanism which cost higher energy for the CH nodes near to BS since it routes the data from other CHs to BS. Some CH nodes may die earlier than its intended lifetime due to its overloaded work which affects the performance of the WSN. This paper contributes a new clustering algorithm, Distributed Unequal Clustering using Fuzzy logic (DUCF) which elects CHs using fuzzy approach. DUCF forms unequal clusters to balance the energy consumption among the CHs. Fuzzy inference system (FIS) in DUCF uses the residual energy, node degree and distance to BS as input variables for CH election. Chance and size are the output fuzzy parameters in DUCF. DUCF assigns the maximum limit (size) of a number of member nodes for a CH by considering its input fuzzy parameters. The smaller cluster size is assigned for CHs which are nearer to BS since it acts as a router for other distant CHs. DUCF ensures load balancing among the clusters by varying the cluster size of its CH nodes. DUCF uses Mamdani method for fuzzy inference and Centroid method for defuzzification. DUCF performance was compared with well known algorithms such as LEACH, CHEF and EAUCF in various network scenarios. The experimental results indicated that DUCF forms unequal clusters which ensure load balancing among clusters, which again improves the network lifetime compared with its counterparts.     

一种能量高效的无线传感器网络拓扑控制算法

通过对现有拓扑控制算法的研究,针对无线传感器网络中节点能耗分布不均匀的问题,提出了一种能量高效的拓扑控制算法(EETCA)。该算法以均衡全局能耗为目标,综合考虑了节点的剩余能量、簇的规模、数据最优传输跳数等因素,避免了部分节点能量消耗过快,从而有效地均衡网络负载。仿真结果表明:EETCA在能耗均衡方面均优于原来的算法,延长了无线传感器网络的生命周期。     

A novel differential evolution based clustering algorithm for wireless sensor networks

Clustering is an efficient topology control method which balances the traffic load of the sensor nodes and improves the overall scalability and the life time of the wireless sensor networks (WSNs). However, in a cluster based WSN, the cluster heads (CHs) consume more energy due to extra work load of receiving the sensed data, data aggregation and transmission of aggregated data to the base station. Moreover, improper formation of clusters can make some CHs overloaded with high number of sensor nodes. This overload may lead to quick death of the CHs and thus partitions the network and thereby degrade the overall performance of the WSN. It is worthwhile to note that the computational complexity of finding optimum cluster for a large scale WSN is very high by a brute force approach. In this paper, we propose a novel differential evolution (DE) based clustering algorithm for WSNs to prolong lifetime of the network by preventing faster death of the highly loaded CHs. We incorporate a local improvement phase to the traditional DE for faster convergence and better performance of our proposed algorithm. We perform extensive simulation of the proposed algorithm. The experimental results demonstrate the efficiency of the proposed algorithm.     

Arranging cluster sizes and transmission ranges for wireless sensor networks

Hierarchical routing is an efficient way to lower energy consumption within a cluster. Due to the characteristics of wireless channels, multi-hop communications between a data source and a data sink are usually more energy efficient than direct transmission. However, because the cluster heads (CHs) closer to the data sink are burdened with heavy relay traffic, they drain much faster than other CHs.This paper presents a cluster-based routing protocol called “arranging cluster sizes and transmission ranges for wireless sensor networks (ACT).” The aim is to reduce the size of clusters near the base station (BS), as CHs closer to the BS need to relay more data. The proposed method allows every CH to consume approximately the same amount of energy so that the CHs near the BS do not exhaust their power so quickly. Furthermore, we separate the network topology into multiple hierarchical levels to prolong network lifetime. Simulation results show that our clustering mechanism effectively improves the network lifetime over LEACH (Low Energy Adaptive Clustering Hierarchy), BCDCP (Base Station Controlled Dynamic Clustering Protocol) and MR-LEACH (multi-hop routing with low energy adaptive clustering hierarchy).     

Distributed clustering algorithms for data-gathering in wireless mobile sensor networks

One critical issue in wireless sensor networks is how to gather sensed information in an energy-efficient way since the energy is a scarce resource in a sensor node. Cluster-based architecture is an effective architecture for data-gathering in wireless sensor networks. However, in a mobile environment, the dynamic topology poses the challenge to design an energy-efficient data-gathering protocol. In this paper, we consider the cluster-based architecture and provide distributed clustering algorithms for mobile sensor nodes which minimize the energy dissipation for data-gathering in a wireless mobile sensor network. There are two steps in the clustering algorithm: cluster-head election step and cluster formation step. We first propose two distributed algorithms for cluster-head election. Then, by considering the impact of node mobility, we provide a mechanism to have a sensor node select a proper cluster-head to join for cluster formation. Our clustering algorithms will achieve the following three objectives: (1) there is at least one cluster-head elected, (2) the number of cluster-heads generated is uniform, and (3) all the generated clusters have the same cluster size. Last, we validate our algorithms through an extensive experimental analysis with Random Walk Mobility (RWM) model, Random Direction Mobility (RDM) model, and a Simple Mobility (SM) model as well as present our findings.     

响应式簇结构的无线传感器网络路由算法

在无线传感器网络路由协议中利用分簇技术可以提高网络的存活时间。提出了一种基于响应式的簇结构路由算法（RCSA）。该算法的思想是应用节点间局部信息快速选举簇头,簇头之间以多跳的通信方式传输数据到汇聚节点,且不需要预先得知节点自身及其他节点的位置信息,大大节约了节点的能量消耗。仿真结果表明该路由算法有效地平衡了节点间的能量消耗,延长了网络的生存周期。     

一种能量均衡的无线传感器网络分簇算法*

为了延长网络的生存时间,提出了一种能量均衡的无线传感器网络分簇算法（EBCA）,该算法优先选择剩余能量较多的节点作为簇首,以平衡节点的能量消耗。仿真实验结果表明：无论同构网还是异构网,该算法都能显著地推迟网络第一个节点的死亡时间,其性能明显优于LEACH算法。     

一种新的基于能量消耗速率模型的分簇路由协议

无线传感器网络中通常采用分簇路由协议来减少能耗,但仍然存在节点能量消耗快且不均匀的问题。鉴于经典的低功耗自适应集簇分层型协议LEACH的簇头选举过程中,没有考虑节点能量消耗速率和普通节点到sink节点距离的局限性,提出了一种新的分簇路由协议。仿真实验表明,新协议能够使节点能量均匀分布,降低节点能量消耗,延长传感器网络的生存周期。     

基于事件的移动认知无线传感器网分簇算法

移动认知无线传感网中, 节点的移动特性会导致网络拓扑结构不断变化, 节点的能耗不均衡等问题, 本文提出一种基于事件的移动认知无线传感器网的分簇算法, 来重点解决上述问题. 算法根据通信区域内的预估计停留时间确定了合格节点和备用节点, 通过节点的移动方向、速度、节点在簇中的预估计连接时间等特性, 采用直接分簇的方法来建簇, 提高簇的稳定性, 保证了路由跳数最少. 同mESAC, EACRP和MNB 3个算法进行了仿真实验比较, 验证了本算法有更低的分簇能耗和更好的连通性.     

无线传感器网络能量异构非均匀分簇路由协议

基于分簇的无线传感器网络路由协议,采用多跳路由方式传输数据至基站,容易造成靠近基站的节点转发大量数据而过早失效。另外,分簇协议通常假定网络节点是能量同构的,不能有效解决节点能量异构的问题。因此,从非均匀分簇的角度出发,结合局部竞争簇首机制,提出了一种基于能量异构的分簇协议（EHUC）。仿真结果表明该协议能够有效应用于能量异构的无线传感器网络,并延长网络的生命周期。