一种响应型无线传感器网络路由算法

提出一种节能型无线传感器网络路由算法——TEENNEW.该算法利用能量模型确定了最优簇头数,在簇头选取阶段考虑了节点剩余能量;在数据传输阶段,该算法根据距离和能量建立簇头与基站之间的多跳通信路径.与传统的TEEN协议相比,TEENNEW算法延长了网络的生命周期,有效均衡了节点能耗.     

无线传感网络中能耗均衡的混合通信算法研究

基于经典的低能耗自适应分簇算法(LEACH),提出了一种能耗均衡的混合通信算法(EEHCA).由基站根据节点剩余能量和簇头之间的距离选举簇头,节点用单跳模式和多跳模式交替与簇头进行通信且概率分别是p和1-p,从而使簇头的分布更均匀,节点的负载更均衡.仿真结果表明该算法有效地平衡了节点的能量消耗,在延长生命周期方面明显好于LEACH.     

一种改进的基于时间竞争成簇的路由算法

为平衡无线传感器网络中的簇头负载并进一步降低多跳传输能耗,文中提出了一种改进的基于时间竞争成簇的路由算法。该算法通过限制近基站节点成簇入簇,以防止近基站节点成簇入簇的节能收益无法补偿成簇入簇能耗;利用基站广播公共信息和基于时间机制成簇,以减少节点基本信息交换能耗;通过候选簇头中继来平衡簇头负载。候选簇头的评价函数综合考虑了剩余能量和最优跳数的理想路径,以期在保持中继负载平衡的基础上尽量降低多跳能耗。仿真结果显示,该算法较LEACH和DEBUC算法延长了以30%节点死亡为网络失效的网络生存周期,表明该算法在降低节点能耗和平衡负载方面是有效的。     

无线传感器网络能量均衡路由算法

设计了一种能量均衡的路由算法.路由的建立分两个阶段,在簇头建立阶段,让候选节点在覆盖范围内以剩余能量的多少和所处位置为标准来竞选簇头,以使簇头分布均匀,平衡网络覆盖;在通信阶段,根据簇头节点的剩余能量大小和其距离基站的远近,采用多跳的簇间通信方式建立路由,以此来降低簇头节点的通信负载.仿真结果表明该算法能够有效降低网络能耗和延长网络生存时间.     

无线传感器网络LEACH算法的改进与仿真分析

对无线传感器网络分簇路由协议中LEACH算法进行了研究,针对算法中簇头节点随机选取、节点能量分布不均等不足之处,提出一种基于节点剩余能量、邻居密度与基站相对距离等因素改进的LEACH算法。改进后的算法通过网络最低能耗计算得出最优簇头数目,在簇头选取阶段,优化簇头的阈值计算公式,使节点剩余能量高、节点邻居数目多、相对基站距离近的节点更容易成为簇头。利用MATLAB软件,对LEACH算法、HEED算法及改进的LEACH算法的性能进行了仿真分析和比较。实验结果表明,改进的LEACH算法的网络整体能耗要低于LEACH算法和HEED算法,并显著地延长了网络的生存时间。     

基于能量和密度的WSNs动态分区成簇路由算法

针对传统的层次型网络存在的分簇不合理和能耗不均衡等问题,提出了一种基于能量和密度的动态非均匀分区成簇路由算法。该算法先根据节点与基站之间的距离将网络合理地进行动态的区域划分,在区域内成簇,使靠近基站的簇规模小于距离基站较远的簇,减少靠近基站的簇首负担和能量消耗;通过综合考虑节点剩余能量和节点密度等因素来优化簇的非均匀划分和簇首的选择,簇首间采取基于数据聚合的多跳传输机制。仿真结果表明,与经典路由算法LEACH相比,该算法能有效均衡节点能耗,延长网络生命周期。     

基于环形搜索的WSN簇间最优路由选择

为了解决LEACH协议中由单跳路由算法造成的节点能量消耗不均衡的问题,首先,计算分析得出簇间多跳的最优跳数,得到簇头节点到基站的一条理想路径;然后,提出一种环形搜索方法去寻找与理想路径最接近的最优路径;最后,提出了簇头到基站的多跳路由算法。仿真实验结果表明,簇头到基站的多跳路由算法较LEACH协议在均衡节点能量消耗和延长网络生命时间方面的性能有明显的提高。     

基于多跳中继转发的簇头拓扑维护算法研究

文章借鉴簇头节点与汇聚节点之间多跳中继转发数据的构想,重点针对拓扑维护的簇头重新选举,提出了一种以能量和节点距离综合考虑的数据中继转发的簇头维护算法。仿真证明该算法有效节约了节点工作耗能,达到了节点能量均衡的效果,使数据通信消耗的能量最低,从而减少了节点能耗,优化了网络拓扑结构,延长了网络生命期。     

基于最小生成树的LEACH协议改进

LEACH(Low Energy Adaptive Clustering Hierarchy)是一种被广泛应用于无线传感网络的路由协议.本文针对LEACH协议中,远距离节点作为簇首时能量消耗过多的缺点,提出了一种改进的无线传感网络路由协议.多跳传输LEACH改进协议在节点选出簇首之后,采用最小生成树中Prim算法,在簇首与基站间采用多跳传输.为克服多跳传输时距基站第一跳的簇首能量消耗较多的缺点,阈值需要采用节点距基站的距离和节点距簇首的平均距离来调整,已达到平衡节点能量消耗的目的.仿真结果表明,改进的算法可减少和均衡能量消耗,提高能量的使用,以及延长整个网络寿命方面具有很大的优势.     

基于繁忙因子的VVSN自适应双簇头分簇算法研究

在WSN中,节点之间不平衡通信消耗大量能量,因此网络生存时间较短。为改善网络整体生存时间,提出一种基于网络繁忙因子的簇头自适应切换算法。首先,算法一次性选举双簇头,再根据网络实际情况自适应切换簇头。然后,在簇头选举完成后,节点通过当前簇头与基站通信,若当前簇头的能量低于门限值,则网络中的当前簇头将被切换到次级簇头,此时节点通过次级簇头与基站通信,从而降低节点能耗,减少节点的死亡率。双簇头切换机制缩短了整体通信距离,缓解簇头节点过早死亡,使网络生存时间增长。仿真结果表明,该算法通过缩短整体通信距离可显著降低整体网络的能量消耗,增加网络的生存时间。     

有效降低无线网络节点能量的LEACH新算法

LEACH是一种低功耗自适应按簇分层路由算法.为了降低节点能耗,在LEACH协议的基础上提出了在选举簇头时,改变阈值T(n)的大小以降低节点成为簇头的概率,从而节省网络因分簇而消耗的能量.同时又提出了一种基于节点剩余能量的二层簇头的算法,该算法能使节点减少将冗余信息传输到基站,从而达到降低节点消耗能量的目的.通过实验仿真,表明这些方法能使网络节点能量的消耗减少,达到了延长网络生命周期的目的.     

UCRP—一种能量有效的WSN非均匀分簇路由协议

提出了一种新的能量有效的非均匀分簇路由协议—UCRP.根据距离基站的远近将网络分为大小不同的簇;簇内数据传输根据簇范围的大小采用单跳或多跳;簇间数据传输构建多跳路由,簇首选择下一跳节点时将能量与最小跳数路由算法相结合;最后在能量有效的前提下对LEACH协议易受到HELLO flooding攻击提出了安全设想.仿真结果表明:该协议能够有效地均衡簇首和全网能耗,延长网络生存时间.     

Multi-factor and Distributed Clustering Routing Protocol in Wireless Sensor Networks

One of important issues in wireless sensor networks is how to effectively use the limited node energy to prolong the lifetime of the networks. Clustering is a promising approach in wireless sensor networks, which can increase the network lifetime and scalability. However, in existing clustering algorithms, too heavy burden of cluster heads may lead to rapid death of the sensor nodes. The location of function nodes and the number of the neighbor nodes are also not carefully considered during clustering. In this paper, a multi-factor and distributed clustering routing protocol MFDCRP based on communication nodes is proposed by combining cluster-based routing protocol and multi-hop transmission. Communication nodes are introduced to relay the multi-hop transmission and elect cluster heads in order to ease the overload of cluster heads. The protocol optimizes the election of cluster nodes by combining various factors such as the residual energy of nodes, the distance between cluster heads and the base station, and the number of the neighbor nodes. The local optimal path construction algorithm for multi-hop transmission is also improved. Simulation results show that MFDCRP can effectively save the energy of sensor nodes, balance the network energy distribution, and greatly prolong the network lifetime, compared with the existing protocols.     

Clustering routing protocol based on improved PSO algorithm in WSN

Aiming at the problem that the location distribution of cluster head nodes filtered by wireless sensor network clustering routing protocol was unbalanced and the data transmission path of forwarding nodes was unreasonable,which would increase the energy consumption of nodes and shorten the network life cycle,a clustering routing protocol based on improved particle swarm optimization algorithm was proposed.In the process of cluster head election,a new fitness function was established by defining the energy factor and position equalization factor of the node,the better candidate cluster head node was evaluated and selected,the position update speed of the candidate cluster head nodes was adjusted by the optimized update learning factor,the local search and speeded up the convergence of the global search was expanded.According to the distance between the forwarding node and the base station,the single-hop or multi-hop transmission mode was adopted,and a multi-hop method was designed based on the minimum spanning tree to select an optimal multi-hop path for the data transmission of the forwarding node.Simulation results show that the clustering routing protocol based on improved particle swarm optimization algorithm can elect cluster head nodes and forwarding nodes with more balanced energy and location,which shortened the communication distance of the network.The energy consumption of nodes is lower and more balanced,effectively extending the network life cycle.     

An Improved Fuzzy Unequal Clustering Algorithm for Wireless Sensor Network

This paper introduces IFUC, which is an Improved Fuzzy Unequal Clustering scheme for large scale wireless sensor networks (WSNs).It aims to balance the energy consumption and prolong the network lifetime. Our approach focuses on energy efficient clustering scheme and inter-cluster routing protocol. On the one hand, considering each node’s local information such as energy level, distance to base station and local density, we use fuzzy logic system to determine each node’s chance of becoming cluster head and estimate the cluster head competence radius. On the other hand, we use Ant Colony Optimization (ACO) method to construct the energy-aware routing between cluster heads and base station. It reduces and balances the energy consumption of cluster heads and solves the hot spots problem that occurs in multi-hop WSN routing protocol to a large extent. The validation experiment results have indicated that the proposed clustering scheme performs much better than many other methods such as LEACH, CHEF and EEUC.     

基于CMRA路由协议改进算法

为了延长无线传感网络的生存时间,需要设计满足高效率、低功耗的路由算法。一种CMRA（intercluster head multi-hop routing algorithm）算法被提出来,这种算法通过节点通信能量消耗模型建立最小能量路径树,但CMRA对于簇头选择的能量分配不均衡,造成簇头结点负载过重。提出一种新的路由算法CMRA-EE（CMRA-energy efficient）,在簇头选举阶段引入节点能量参数,同时将簇头节点能量与距离作为代价参数,从而平衡了网络节点能耗。通过仿真对CMRA-EE算法进行性能分析与评价,结果显示,CMRA-EE算法在延长无线传感网有效生存时间方面比CMRA算法有了明显的改善。     

An optimised fuzzy clustering for wireless sensor networks

Minimising energy consumption has always been an issue of crucial importance in sensor networks. Most of the energy is consumed in data transmission from sensor nodes to the base station due to the long distance of nodes from the base station. In the recent past, a number of researchers have proposed that clustering is an efficient way of reducing the energy consumption during data transmission and enhancing the lifetime of wireless sensor networks. Many algorithms have been already proposed for cluster head selection. In this work, we analyse and compare the lifetime of the network with three different fuzzy-based approaches of cluster head selection. The three strong parameters which play an important role in lifetime enhancement – energy, centrality and node density – are considered for cluster head selection in our proposed fuzzy approaches. In the first approach, energy and centrality are considered simultaneously in a fuzzy system to select the cluster heads. In the second approach, energy and node density have been taken in a fuzzy system to select the cluster heads. In the third approach, node density and centrality are considered simultaneously by a fuzzy system to select the cluster heads. Simulation results of these fuzzy logic-based approaches show that all the three approaches are superior to the Low-Energy Adaptive Clustering Hierarchy (LEACH). Simulation results also show that the energy-centrality-based fuzzy clustering scheme gives best performance among all the three fuzzy-based algorithms and it enhances the lifetime of wireless sensor networks by a significant amount.