无线传感器网络的LEACH算法改进与仿真研究

在无线传感器路由算法的研究中,由于无线传感器能量受限,针对LEACH路由算法中簇头选择不合理以及节点能耗不均衡等不足,为了提高能量的有效性,提出了一种改进的LEACH路由算法(E_LEACH).首先在簇首选择的过程中,充分考虑节点的能量状态,尽可能使剩余能量高的节点当选为簇首;然后在数据传输阶段采用单跳和多跳混合通信方式均衡簇头与基站能量消耗,提高网络能量的利用率,延长网络生存时间.采用OPNET对E_LEACH及LEACH算法进行了仿真实验,结果表明,对比LEACH算法,E_LEACH算法更能均衡节点的能耗,有效地延长了整个网络的生存周期.     

无线传感器网络分簇算法研究

研究无线传感器网络能耗和使用寿命问题.无线传感器网络由大量能量有限的传感器节点组成,节点靠电池供电,耗能不均衡将影响网络寿命.为了合理使能耗均衡、延长网络使用寿命,提出了一种新的高效节能无线传感器网络分簇算法.算法在簇头选择阶段,同时簇头的剩余能量及簇头与基站的距离来给选择;在簇生成阶段,考虑邻接簇头以及网关的剩余能量,选择一条最优化的路径,然后将融合后的数据以多跳方式传送到基站完成整个网络数据的转输.仿真结果表明,改进算法在簇头的选举和簇的生成两个阶段都综合考虑了能量和距离更能均衡各个节点能耗,证明延长了网络生存周期,提高网络的通信效率.     

一种基于测量数据相似性的WSN分簇路由方法

针对无线传感器网络(Wireless Sensor Network, WSN)中节点能量有限、节点能耗不均衡导致网络生命周期较短的问题,提出一种基于测量数据相似性的分簇路由算法CRMDS(A WSN Clustering Routing Method based on Measured Data Similarity)。在分簇阶段,设计自适应聚类算法,将测量值相似性高的节点分为一簇,在每个簇中选择一个节点传送数据,簇内其他节点进入睡眠模式以节省网络能量。在数据传输阶段,根据节点的剩余能量和传送能耗动态地更替簇头节点,并设计传送次数因子和相对距离因子生成簇头节点之间的路由路径,平衡节点能耗。仿真结果表明,CRMDS算法相较于现有算法能够有效节省网络能量,延长网络生命周期。     

基于FCM聚类和蚁群优化的WSN路由算法

在无线传感器网络(WSN)中生命周期的研究中,提出一种基于LEACH协议、Fuzzy C-Means(FCM)聚类算法和蚁群算法的改进路由算法。首先在预处理阶段FCM聚类算法将节点距离形成多个簇,避免每轮成簇造成能量浪费。然后在数据传输阶段使用蚁群算法寻找从簇头到基站的最优路径。仿真结果表明,该算法与LEACH协议相比,能够有效减少能量消耗、延长网络寿命。     

一种基于节点位置和密度的非均匀分簇路由算法

分析了现有分簇路由算法,提出了基于节点位置和密度的非均匀分簇路由算法。簇头选举阶段,考虑了节点的剩余能量,并引入竞争机制进行簇头选择;成簇阶段,综合考虑节点与基站的距离、节点密度以进行非均匀分簇,达到节点能耗均衡的效果,同时解决路由热区问题;簇间路由阶段,通过设立通信簇头节点,使簇间数据转发任务从簇头中分离,簇头节点只负责簇内的数据收集和融合,而通信簇头节点负责簇间数据传输,减少了簇头的能量消耗。实验结果表明,改进后的路由算法能够有效地均衡网络负载,并显著地延长网络的生命周期。     

无线传感器网络路由算法改进及仿真研究

由于无线传感器能量消耗影响网络的寿命,传感器节点的能量无法更新且种能量受限,传统路由算法忽略簇头剩余能量情况,使剩余能量低的节点成为簇头而过早死亡,导致整个网络能量不均衡,网络生存时间过短。为了有效延长网络生存时间,提出一种改进的LEACH路由算法。在簇头选择阶段,采用剩余能量的簇头节点优先选择机制,避免剩余能量低的节点成为簇头,然后在数据传输阶段,用单跳和多跳的混合传输模式,使整个网络能量尽量均衡。仿真结果表明,相对于传统LEACH路由算法,改进算法更加均衡了网络中各节点的能量消耗,有效地防止剩余能量低的节点成为簇头,可延长整个网络的生存寿命。     

基于模糊C-均值的无线传感器网络算法

在低功耗自适应集簇分层型协议算法的基础上,提出一种基于模糊C-均值的无线传感器网络算法。在簇形成阶段采用模糊C-均值方法根据基站预先指定的最优簇头个数Q,将整个传感器网络节点分成Q个簇,每个节点隶属于其中一个簇,在整个网络生命周期内,这个簇将固定不变。在新的一轮开始时,簇内簇头节点的选择基于节点的当前能量值。在数据传输阶段,在簇内通信采用单跳模式,簇间通信采用多跳模式。仿真实验表明,该算法具有可行性和有效性。     

改进遗传算法的WSN节点最优路由选择策略

针对无线传感器节点数据传输过程中的能量消耗问题,为了提高节点数据传输实时性,提出一种改进遗传算法的无线传感器网络节点最优路由选择策略。根据无线传感器网络的拓扑结构将监测区域划分不同大小的簇,并根据节点剩余能量选择每一个簇的簇头节点,然后将簇头节点编码成遗传算法的个体,根据数据转发能量耗能和延迟时间构建个体的适应度函数,并通过模拟自然界生物进化过程中的选择、交叉、变异等操作,找到节点数据转发的最优路径,在Matlab 2012平台上对数据路由算法的性能进行仿真测试。仿真结果表明,相对其他路由选择策略,提出的路由选择策略不仅可以均衡各个传感器节点的剩余能量,而且大幅度减少了数据转发路由过程中的能量消耗和延迟时间。     

基于负载均衡和最短路径的异构无线传感器网络成簇算法

针对节点负载不均衡和数据传输距离的问题,提出一种适用于异构网络的基于负载均衡和最短路径的分布式成簇算法DUBP(distributed and unequal clustering algorithm based on load balance and shortest path)。DUBP首先基于节点的能耗因子对网络动态分区,以均衡负载;然后结合网络拓扑结构和图论,利用Floyd算法求出节点间的最短距离作为路径因子;最后以节点的能量因子和路径因子作为辅助参数来竞争簇头,以避免低能量节点担任簇头,节省传输能耗。仿真表明,DUBP算法能显著延长网络寿命,有良好的适应性和能效性。     

WSN中基于非均匀簇的混合多跳路由协议*

为节省数据传输过程中消耗的能量,均衡网络节点间的能耗,提出一种基于非均匀簇的混合多跳路由协议。在无线传感器网络数据传输阶段,源簇头节点通过转发权值函数选择数据转发的中继节点,转发权值由用于降低链路通信代价的距离因子和减少剩余能量较少的簇头节点成为中继节点的概率惩罚因子共同决定,达到均衡网络能耗的目的。通过NS2仿真实验验证了算法的有效性,能够很好地均衡节点负载和提高能量利用率。     

采用半贪心优化的节点非均匀分布路由协议

为避免无线传感器网络中因节点能耗不均衡而产生的能量空洞现象,延长网络生命周期,提出采用半贪心优化的节点非均匀分布路由协议。首先在网络监测区域分层的基础上,计算各层感知数据转发能耗,根据各层网络能耗比例和监测区域覆盖要求,设计了密度递减的节点部署模型;然后基于两跳通信的贪心范围,提出两跳能耗代价估计函数,改进半贪心算法;在簇间多跳通信阶段,利用优化的半贪心算法求解簇头到基站的最优转发路径。仿真实验表明,与现有的几种路由协议相比,新协议能够均衡各层网络节点能耗,延长网络生命周期,有效避免能量空洞现象。     

基于双模糊逻辑的无线传感器网络分簇算法

为降低无线传感器网络的能量消耗,延长网络生命周期,提出基于双模糊逻辑的无线传感器网络分簇算法(DFCP)。模糊逻辑一综合了节点剩余能量和节点与基站距离2个参数,确保输出高能量低能耗的节点竞争簇头的优势;模糊逻辑二综合了节点度与簇内平均节点能耗值2个参数,确保输出以簇为单位的局部能耗最小。簇生成阶段,基于非概率模式的延时机制保证了簇簇之间的均匀分布。通过与其他算法(LEACH、ECPF)对比,仿真结果表明:DFCP能克服LEACH协议运行下的网络簇分布不均、低能量节点担任簇头等缺点,并降低网络能量消耗;当网络中节点能量不一致时,DFCP运行下的网络簇头位置分布、网络局部能耗均衡优于ECPF。     

An improved ant colony optimization-based approach with mobile sink for wireless sensor networks

Traditional wireless sensor networks (WSNs) with one static sink node suffer from the well-known hot spot problem, that of sensor nodes near the static sink bear more traffic load than outlying nodes. Thus, the overall network lifetime is reduced due to the fact some nodes deplete their energy reserves much faster compared to the rest. Recently, adopting sink mobility has been considered as a good strategy to overcome the hot spot problem. Mobile sink(s) physically move within the network and communicate with selected nodes, such as cluster heads (CHs), to perform direct data collection through short-range communications that requires no routing. Finding an optimal mobility trajectory for the mobile sink is critical in order to achieve energy efficiency. Taking hints from nature, the ant colony optimization (ACO) algorithm has been seen as a good solution to finding an optimal traversal path. Whereas the traditional ACO algorithm will guide ants to take a small step to the next node using current information, over time they will deviate from the target. Likewise, a mobile sink may communicate with selected node for a relatively long time making the traditional ACO algorithm delays not suitable for high real-time WSNs applications. In this paper, we propose an improved ACO algorithm approach for WSNs that use mobile sinks by considering CH distances. In this research, the network is divided into several clusters and each cluster has one CH. While the distance between CHs is considered under the traditional ACO algorithm, the mobile sink node finds an optimal mobility trajectory to communicate with CHs under our improved ACO algorithm. Simulation results show that the proposed algorithm can significantly improve wireless sensor network performance compared to other routing algorithms.     

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.     

一种无线传感器网络簇头选择算法——基于簇头发送能耗

分簇算法中,簇头的选择对无线传感器网络的能耗有重要的影响,为了提高网络生存周期,提出了一种基于簇头发送能耗的簇头选择算法（SECCS）。为了平衡节点间的不同能耗,使已做过簇头的节点在其后若干轮内不能再次成为簇头,其预计不能做簇头的轮次根据簇头发送能耗来决定,并动态调整不能做簇头的轮次,保证候选节点数量在合适的范围内。在选择簇头时,限制簇头间的距离不能过小,并优先选择周围节点数量适中而平均距离较近的节点成为簇头,使簇头尽可能均匀分布以减少全网能耗。该算法不需要节点的剩余能量和位置信息,计算简单。通过仿真和数据分析,证明其网络生存周期较长。     

Energy Efficient Gravitational Search Algorithm and Fuzzy Based Clustering With Hop Count Based Routing For Wireless Sensor Network

In Wireless sensor networks, energy efficiency is the significant attribute to be improved. Clustering is the major technique to enhance energy efficiency. Using this technique, sensor nodes in the network region are grouped as several clusters and cluster head (CH) is chosen for each and every cluster. This CH gathers data packet from the non-CH members inside the cluster and forwards the collected data packet to the base station. However, the CH may drain its energy after a number of transmissions. So, we present the Energy efficient Gravitational search algorithm (GSA) and Fuzzy based clustering with Hop count based routing for WSN in this paper. Initially, CH is selected using Gravitational Search Algorithm (GSA), based on its weight sensor nodes are joined to the CH and thus cluster is formed. Among the selected CHs in the network, supercluster head (SCH) is selected using a fuzzy inference system (FIS). This selected SCH gathers the data packet from all CHs and forwards it to the sink or base station. For transmission, the efficient route is established based on the hop count of the sensor nodes. Simulation results show that the performance of our proposed approach is superior to the existing work in terms of delivery ratio and energy efficiency.

     

基于DSP的障碍物环境的簇状传感网络路由算法

分簇是延长无线传感网络寿命的有效技术之一,然而现有的簇状传感网络的路由技术没有考虑障碍物环境.为此,提出了面向障碍物的簇状传感网络的Dijkstra最短路径路由DSPR(Dijkstra shortestpath-based routing)算法.DSPR算法首先利用能量有效的同质簇EHC(energy-efficient homogeneous clus-tering)技术周期地选举簇头CH(cluster head).每周期定义一帧,每帧利用EHC技术选举簇头CH.簇头CHs构成数据传输的主干路径,并利用Dijkstra最短路径DSP(Dijkstra shortest path)算法选择最优路径,当遭遇障碍物时,将障碍物的顶点作为中间终点,再运行DSP,从而缩短数据传输路径.仿真结果表明,提出的DSPR有效减少传输路径和能量消耗,并提高了数据传输效率.     

Hybrid metaheuristic algorithm for optimal cluster head selection in wireless sensor network

Clustering is one of the major techniques for maximizing the network lifetime in wireless sensor networks (WSNs). Here, the sensor nodes (SNs) are grouped into clusters and the cluster heads (CHs) are selected for each cluster. CHs gather data from particular cluster nodes and then forward it to Base Station (BS). However, the selection of CHs is the major issue in this scenario. The sensor nodes consume more energy for the data transmission and also affect the lifetime of the network. The clustering technique is used to provide the energy-efficient data transmission that consumes less energy and also increases the network lifetime. This paper aims to propose a new energy-aware CH selection framework by hierarchical routing in WSN via a hybrid optimization algorithm. Moreover, the selection of CH is carried out under the consideration of energy, distance, delay and Quality of Service (QoS) as well. For selecting the optimal CH, a new hybrid algorithm named as Particle Distance Updated Sea Lion Optimization (PDU-SLnO) algorithm is introduced that combines the concept of Sea Lion Optimization (SLnO) and Particle swarm optimization (PSO) algorithm. Finally, the performance of adopted method is computed over other traditional models with respect to certain metrics.     

WSN中一种改进的基于LEACH-C算法的簇间路由算法研究

在WSN(Wireless Senor Network,无线传感器网络)中的分级路由算法中,如果簇头仅仅能够进行单跳通信或者多跳通信,都会造成网络负载不均衡以及簇头能量消耗过快的问题出现。针对这一问题,文章提出了一种改进的基于LEACH-C(Low Energy Adaptive Clustering Hierarchy Centralized,低功耗自适应集中分层型)算法的簇间路由(Cluster Routing based on LEACH-C Algorithm,简称CRLA)算法。该算法通过距离阀值来控制簇头是进行单挑通信还是多跳通信。仿真分析表明,CRLA算法能够实现网络负载的均衡以及减少簇头能量的消耗,从而实现网络生存时间的延长。