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。     

无线传感器网络中LEACH算法的改进研究

针对经典分簇路由算法LEACH在选取簇头时的不足,文中从节点区域能量平衡出发,尽量将剩余能量较高且所在位置覆盖密度较大的节点充当簇头,使整个区域"均匀"消耗能量,并基于此思想提出了一种对经典分簇路由算法LEACH的改进算法.实验仿真结果表明,改进算法能有效平衡区域能量消耗,延长网络的生命周期.     

无线传感器网络LEACH协议的研究

在无线传感器网络中,与平面路由协议相比,分簇路由协议具有一定的优势.它具有拓扑管理方便、能量利用高效.数据融合简单等优点,成为当前路由协议研究的重点.本文以低能量自适应聚类(LEACH)为例,对分簇路由协议进行了分析.在此基础上,提出改进了的LEACH ED_LEACH(Base on Energy and Di Stance factors-Low-Energy Adaptive Clustering Hierarchy),通过使用MATLAB进行了仿真,仿真结果表明ED_LEACH算法能更有效地延长网络寿命.     

一种基于LEACH的无线传感器网络路由算法及仿真

在低功耗自适应分簇（LEACH,Low Energy Adaptive Clustering Hierarch）算法中,由于每一轮循环都要重新构造簇,距离较远的簇头节点可能会因长距离发送数据而过早耗尽自身能量,能量较低的节点当选为簇头节点时将会加速该节点的死亡,影响整个网络的生命周期。针对LEACH算法分簇机制中存在的不足,提出了一种改进的路由算法。仿真结果表明,改进算法通过考虑节点的剩余能量与固定分簇的方法,有效的改善了网络能量均衡,提高了网络生存时间。     

无线传感器网络LEACH协议的改进与仿真

LEACH路由协议是无线传感器网络中经典的层次型拓扑组织算法。对LEACH协议进行研究和分析,指出LEACH协议在簇的区域分布和簇头负载不均衡等方面问题。文章通过节点剩余能量和与基站的距离对簇头选择、构造分簇以及非簇节点选择簇头进行改进。并利用MATLAB对改进后的算法进行仿真,表明改进后的算法有效地均衡节点能量消耗,并延长网络生存时间。     

基于分簇的无线传感器网络通信协议LEACH研究

针对于直接传输（DT,DirectTransmission）和最小化传输能量（MTE,MinimumTransmissionEnergy）两种传统通信协议的缺点和无线传感器网络（WSNs,WirelessSensorNetworks）的特点,分析了基于分簇的无线传感器网络通信的低功耗自适应分簇协议（LEACH,Low—EnergyAdaptiveCluste—ringHierarchy）的基本原理,并对三者进行仿真分析。分析与仿真结果表明,使用LEACH协议比使用DT协议要节约大约7到8倍的能源,同时也比MTE协议方式要减少4到8倍能源消耗。更适用于无线传感器网络。     

无线传感器网络LEACH协议的改进与仿真

LEACH路由协议是无线传感器网络中经典的层次型拓扑组织算法。对LEACH协议进行研究和分析,指出LEACH协议在簇的区域分布和簇头负载不均衡等方面问题。文章通过节点剩余能量和与基站的距离对簇头选择、构造分簇以及非簇节点选择簇头进行改进。并利用MATLAB对改进后的算法进行仿真,表明改进后的算法有效地均衡节点能量消耗,并延长网络生存时间。     

无线传感器网络LEACH协议的改进方案

基于LEACH协议,提出了一种改进方案,对LEACH协议的不足进行了改善。此方案中,簇头的选择取决于节点的剩余能量,且簇头数量与网络中的剩余节点数量相关,更符合网络特性。此外,改进方案对簇中节点数量进行了限制,使得分簇更加均匀,并采用了适应性信道分配原则,更加适用于动态网络。计算机仿真结果表明,与LEACH协议相比,采用改进方案后汇聚节点接收到的数据量明显增加,网络性能得到改善。     

LEACH算法的改进分簇模型在无线传感器网络中的研究

无线传感器网络是将传感器通过无线通信的方式,对网络信息进行处理及传递.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通信技术,可以实时监测、感知和采集网络分布区域内的各种对象的信息,并对这些信息进行处理,传送给所需用户.LEACH算法是一种典型的层次路由算法,该算法提出了低功耗持续运行的模型.但LEACH算...     

无线传感器网络LEACH协议的研究与改进

针对LEACH路由协议簇头选举问题,提出一种改进的路由协议LEACH_improve。如果簇头平均剩余能量小于阈值,则算法采用全网选举簇头方式。否则,依据节点剩余能量、距簇内质心的距离等约束条件算法使用簇内选取簇头方式。仿真结果表明:与经典路由协议LEACH相比,LEACH_improve提高了能量利用率,延长了网络生命。     

A new algorithm for cluster head selection in LEACH protocol for wireless sensor networks

In wireless sensor network, a large number of sensor nodes are distributed to cover a certain area. Sensor node is little in size with restricted processing power, memory, and limited battery life. Because of restricted battery power, wireless sensor network needs to broaden the system lifetime by reducing the energy consumption. A clustering‐based protocols adapt the use of energy by giving a balance to all nodes to become a cluster head. In this paper, we concentrate on a recent hierarchical routing protocols, which are depending on LEACH protocol to enhance its performance and increase the lifetime of wireless sensor network. So our enhanced protocol called Node Ranked–LEACH is proposed. Our proposed protocol improves the total network lifetime based on node rank algorithm. Node rank algorithm depends on both path cost and number of links between nodes to select the cluster head of each cluster. This enhancement reflects the real weight of specific node to success and can be represented as a cluster head. The proposed algorithm overcomes the random process selection, which leads to unexpected fail for some cluster heads in other LEACH versions, and it gives a good performance in the network lifetime and energy consumption comparing with previous version of LEACH protocols.     

基于LEACH协议的无线传感器网络路由算法的改进与仿真

针对无线传感器网络中传感器能量有限的问题,从路由算法的角度出发,提出LEACH协议的低功耗改进方案.本文采用划定区域的方式对LEACH协议中的簇头选举进行改进,从而减少网络中节点分布不均匀的情况对簇头节点能量损耗所造成的影响,并使用NS-2进行协议改进前后的仿真.仿真实验结果表明,协议改进后网络生存期有效增长,能量消耗...     

Dual head static clustering algorithm for wireless sensor networks

Clustering of nodes is often used in wireless sensor networks to achieve data aggregation and reduce the number of nodes transmitting the data to the sink. This paper proposes a novel dual head static clustering algorithm (DHSCA) to equalise energy consumption by the sensor nodes and increase the wireless sensor network lifetime. Nodes are divided into static clusters based on their location to avoid the overhead of cluster re-formation in dynamic clustering. Two nodes in each cluster, selected on the basis of the their residual energy and their distance from the sink and other nodes in the cluster, are designated as cluster heads, one for data aggregation and the other for data transmission. This reduces energy consumption during intra-cluster and inter-cluster communication. A multi-hop technique avoiding the hot-spot problem is used to transmit the data to the sink. Experiments to observe the energy consumption patterns of the nodes and the fraction of packets successfully delivered using the DHSCA suggest improvements in energy consumption equalisation, which, in turn, enhances the lifetime of the network. The algorithm is shown to outperform all the other static clustering algorithms, while being comparable with the performance of the best dynamic algorithm.     

基于LEACH路由协议的混合光无线传感网络研究

分析了基于低功耗自适应分簇(LEACH)路由协议的无线传感网络(WSN)在不同拓扑形状下的生命周期,并改进了长方形拓扑形状的路由协议。进而针对WSN在某些场合能量有限、易受干扰和安全性差等缺点,在长方形区域中引入分布式光纤传感结构。将传感光纤铺设在环境复杂和外界电磁波干扰大的监测区域,从而提高整个传感网络的生命周期和可靠性。理论分析和仿真结果表明,改进的拓扑和协议在提高可靠性的同时,有效地延长了光WSN的生存时间,性能优于传统LEACH协议。     

DEICA: A differential evolution-based improved clustering algorithm for IoT-based heterogeneous wireless sensor networks

With the evolution of technology, many modern applications like habitat monitoring, environmental monitoring, disaster prediction and management, and telehealth care have been proposed on wireless sensor networks (WSNs) with Internet of Things (IoT) integration. However, the performance of these networks is restricted because of the various constraints imposed due to the participating sensor nodes, such as nonreplaceable limited power units, constrained computation, and limited storage. Power limitation is the most severe among these restrictions. Hence, the researchers have sought schemes enabling energy-efficient network operations as the most crucial issue. A metaheuristic clustering scheme is proposed here to address this problem, which employs the differential evolution (DE) technique as a tool. The proposed scheme achieves improved network performance via the formulation of load-balanced clusters, resulting in a more scalable and adaptable network. The proposed scheme considers multiple parameters such as nodes' energy level, degree, proximity, and population for suitable network partitioning. Through various simulation results and experimentation, it establishes its efficacy over state-of-the-art schemes in respect of load-balanced cluster formation, improved network lifetime, network resource utilization, and network throughput. The proposed scheme ensures up to 57.69%, 33.16%, and 57.74% gains in network lifetime, energy utilization, and data packet delivery under varying network configurations. Besides providing the quantitative analysis, a detailed statistical analysis has also been performed that describes the acceptability of the proposed scheme under different network configurations.     

基于改进LEACH的多簇头分簇路由算法

为了降低无线传感器网络(WSN)的能耗,延长网络的生存周期,提出一种多簇头双工作模式的分簇路由算法.算法对低功耗自适应集簇分层(LEACH)协议作了以下改进:采用多簇头双工作模式来分担单簇头的负荷,以解决单簇头因能耗较大而过早消亡的问题;选举簇头时充分考虑节点位置和节点剩余能量,并应用粒子群优化(PSO)算法优化簇头的选举,以均衡网络内各节点的能耗;建立簇与簇之间的数据传输路由,以减少簇间通信的能耗.仿真结果表明,算法有效降低了网络的能耗,延长了网络的生存周期.     

基于LEACH的WSN路由算法研究

为了提高无线传感器网络的可扩展性在其路由协议中通常会采用分簇技术。由于无线传感器网络由能量有限的节点组成,网络中节点的能量多为电池供电,因此高效节能以延长网络生命周期是无线传感器网络必须要充分考虑的问题。针对LEACH(低功耗自适应分簇)路由算法在簇首选择时存在的问题,提出一种改进建议。对改进算法利用仿真工具NS2进行仿真,并对节点存活率和网络能耗两个方面进行比较与分析,仿真结果表明,改进算法很好地延长了网络的寿命。     

Distributed fuzzy approach to unequal clustering and routing algorithm for wireless sensor networks

Due to inherent issue of energy limitation in sensor nodes, the energy conservation is the primary concern for large‐scale wireless sensor networks. Cluster‐based routing has been found to be an effective mechanism to reduce the energy consumption of sensor nodes. In clustered wireless sensor networks, the network is divided into a set of clusters; each cluster has a coordinator, called cluster head (CH). Each node of a cluster transmits its collected information to its CH that in turn aggregates the received information and sends it to the base station directly or via other CHs. In multihop communication, the CHs closer to the base station are burdened with high relay load; as a result, their energy depletes much faster as compared with other CHs. This problem is termed as the hot spot problem. In this paper, a distributed fuzzy logic‐based unequal clustering approach and routing algorithm (DFCR) is proposed to solve this problem. Based on the cluster design, a multihop routing algorithm is also proposed, which is both energy efficient and energy balancing. The simulation results reinforce the efficiency of the proposed DFCR algorithm over the state‐of‐the‐art algorithms, ie, energy‐aware fuzzy approach to unequal clustering, energy‐aware distributed clustering, and energy‐aware routing algorithm, in terms of different performance parameters like energy efficiency and network lifetime.     

Load balancing clustering and routing for IoT-enabled wireless sensor networks

Internet of things (IoT) devices are equipped with a number of interconnected sensor nodes that relies on ubiquitous connectivity between sensor devices to optimize information automation processes. Because of the extensive deployments in adverse areas and unsupervised nature of wireless sensor networks (WSNs), energy efficiency is a significant aim in these networks. Network survival time can be extended by optimizing its energy consumption. It has been a complex struggle for researchers to develop energy-efficient routing protocols in the field of WSNs. Energy consumption, path reliability and Quality of Service (QoS) in WSNs became important factors to be focused on enforcing an efficient routing strategy. A hybrid optimization technique presented in this paper is a combination of fuzzy c-means and Grey Wolf optimization (GWO) techniques for clustering. The proposed scheme was evaluated on different parameters such as total energy consumed, packet delivery ratio, packet drop rate, throughput, delay, remaining energy and total network lifetime. According to the results of the simulation, the proposed scheme improves energy efficiency and throughput by about 30% and packet delivery ratio and latency by about 10%, compared with existing protocols such as Chemical Reaction Approach based Cluster Formation (CHRA), Hybrid Optimal Based Cluster Formation (HOBCF), GWO-based clustering (GWO-C) and Cat Swarm Optimization based Energy-Efficient Reliable sectoring Scheme with prediction algorithms (P_CSO_EERSS). The study concludes that the protocol suitable for creating IoT monitoring system network lifetime is an important criteria.     

A novel chain-based clustering for green communication in wireless sensor network

The rapid growth of the Internet of Things (IoT) creates a high requirement for data collected through wireless sensor networks (WSNs), resulting in a lot of emphasis on WSN data collecting in recent decades. However, the resource-constrained nature of sensor devices exerts heavy constraints for acquiring the optimal network performance. To resolve this concern, this paper proposes the Chain-based Energy-Efficient Clustering (CBEEC) Routing Protocol that considers nodes of two heterogeneous levels of energy (normal level and advanced level). The number of advanced nodes in CBEEC is analytically determined and hence are analytically allocated. However, normal nodes are stochastically deployed in the vicinity of advanced nodes. The data transmission is performed among the advanced nodes in the form of chain and from there it is forwarded to base station (BS). Consequently, it preserves immense amount of energy for the network. The performance of CBEEC is empirically investigated with benchmarks of different performance metrics and simulation outcomes showing that the CBEEC outperforms state-of-the-art routing protocols.