基于模糊逻辑推理的WSNs非均匀分簇算法

为了进一步降低无线传感网络WSNs(Wireless Sensor Networks)能耗,拓延网络寿命,提出了基于模糊逻辑推理的WSNs非均匀分簇算法,记为DUCF.DUCF算法充分考虑了节点剩余能量、节点度以及离基站距离.根据经验制定模糊规则,通过模糊推理系统得到节点当选为簇头的几率和簇尺寸.DUCF算法形成非均匀簇,进而平衡簇头间的能量消耗.仿真结果表明,DUCF算法在网络寿命、能量消耗方面的性能优于LEACH、CHEF和EAUCF算法.     

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.     

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in large-scale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network.     

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。     

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).     

基于吸引因子和混合传输的分簇路由算法

为了能够有效地降低无线传感器网络(WSN)的能耗,延长网络生命周期,对低功耗自适应集簇分层型(LEACH)协议等多个分簇路由协议进行分析,并针对其算法存在的缺陷提出基于吸引因子和多跳传输的分簇路由算法(CRAH)。针对不合理的簇头选择问题,采用加权和的方法将节点剩余能量与节点位置两个参数,作为簇头选择的新指标;对簇头节点的任务进行重新分配,选出新的融合节点;融合节点和基站的通信采用单跳与多跳相结合的混合传输方式,结合吸引因子和Dijkstra算法提出新的基于吸引因子的Dijkstra(AF-DK)算法,为融合节点找到最优转发路径。仿真结果表明,与LEACH、集中式低功耗自适应集簇分层型(LEACH-C)路由和固定簇半径的分簇(HEED)等协议相比,CRAH使网络寿命分别提高了约51.56%、47.1%和42%,网络能耗速度明显减缓,基站接收的数据量平均减少了69.9%。CRAH使簇头选择更加合理,有效减少了通信过程中的冗余数据,均衡了网络能耗,延长了网络生命周期。     

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.

     

基于均衡分簇的无线传感器网络压缩数据收集

应用压缩感知（CS）理论结合稀疏随机投影的无线传感器网络（WSN）压缩数据收集（CDG）可以大大减少网络传输的数据量。针对随机选择投影节点作为簇头来收集数据导致网络整体能耗不稳定和不平衡的问题,提出两种平衡投影节点的压缩数据收集方法。对于节点分布均匀WSN,提出基于空间位置的均衡分簇法：首先,均匀划分网格;然后,在每个网格选举投影节点,依距离最短原则成簇;最后,由投影节点收集簇内数据到汇聚节点完成数据收集,从而使得投影节点分布均匀、网络能耗均衡。对于节点分布不均匀的WSN,提出基于节点密度的均衡分簇法：同时考虑节点的位置和密度,对节点数量少的网格不再选择投影节点,将网格内的少量节点分配到邻近的网格,从而平衡网络能量,延长网络寿命。仿真结果表明,与随机投影节点法相比,所提的两种方法的网络寿命均延长了25%以上,剩余节点数在网络运行中期均能达到2倍左右,具有更好的网络连通性,显著提高了整个网络的生命周期。     

Distributed unequal clustering algorithm in large-scale wireless sensor networks using fuzzy logic

Clustering is a promising and popular approach to organize sensor nodes into a hierarchical structure, reduce transmitting data to the base station by aggregation methods, and prolong the network lifetime. However, a heavy traffic load may cause the sudden death of nodes due to energy resource depletion in some network regions, i.e., hot spots that lead to network service disruption. This problem is very critical, especially for data-gathering scenarios in which Cluster Heads (CHs) are responsible for collecting and forwarding sensed data to the base station. To avoid hot spot problem, the network workload must be uniformly distributed among nodes. This is achieved by rotating the CH role among all network nodes and tuning cluster size according to CH conditions. In this paper, a clustering algorithm is proposed that selects nodes with the highest remaining energy in each region as candidate CHs, among which the best nodes shall be picked as the final CHs. In addition, to mitigate the hot spot problem, this clustering algorithm employs fuzzy logic to adjust the cluster radius of CH nodes; this is based on some local information, including distance to the base station and local density. Simulation results demonstrate that, by mitigating the hot spot problem, the proposed approach achieves an improvement in terms of both network lifetime and energy conservation.     

Trust- and energy-aware cluster head selection in a UAV-based wireless sensor network using Fit-FCM

Due to the emerging applications of unmanned aerial vehicle (UAV)-based technologies, UAV-based wireless communication techniques, such as UAV-based coverage extension, UAV-based data distribution and UAV-based relaying, are being used to collect information in different processing sectors. In particular, UAV-based data gathering and distribution can be executed using a UAV-based wireless sensor network (WSN). In UAV-based WSNs, the cluster heads (CHs) serve important functions in both data gathering and data transfer between members and UAVs. Due to the important functions of CHs, many attackers attempt hack CH nodes. Typically, a hacked CH utilizes excess energy compared to a normal CH since it performs the CH function of delivering information to a sink greedily. To resolve this, this paper develops a novel UAV-based CH selection (CHS) algorithm for use in WSNs, namely, the Fitness-based Fuzzy C-Means (Fit-FCM) algorithm, which gathers the remaining energy of nodes and utilizes the energy for selecting new CHs while neglecting the nodes with the lowest energy. Initially, UAV-based WSN nodes are simulated, and then, CHS is performed using the developed Fit-FCM algorithm, in which fitness functions such as energy, distance and trust are considered. After CHS, information is transmitted through the selected CHs. Experimental results demonstrate that the developed Fit-FCM achieves better results in terms of distance, energy, and trust, with values of 51.9076 m, 0.4882 J, and 0.536439, respectively.

     

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。     

无线传感网中一种能量均衡的分簇路由算法

无线传感器网络的一个极富挑战性、极其关键的课题就是降低能源消耗以延长网络寿命。文中提出了一种能量均衡的分簇路由算法（CRA—EB）。算法分为三个阶段,即：簇头选择、聚的生成及数据传输。首先基于节点的剩余能量和邻居节点数目来选择簇头。然后每一个非簇头节点根据簇头代价值加入自身通信范围内的簇头。在数据传输阶段,CRA-EB首先在簇内使用单跳通信,然后在簇间使用多跳通信。对簇间通信,簇头以自身为起点对通往基站的各路径代价进行衡量,同时选择其他簇头作为中继节点在这些路径上转发数据。仿真实验结果表明,与LEACH和DEBR算法进行比较,CRA-EB算法在能耗和活跃节点数量方面的性能表现更加高效。     

HDEEC：面向异构网络的一种基于簇路由协议

有效地使用传感节点的能量进而延长网络寿命成为设计无线传感网路由协议的一项挑战性的工作.为了延长网络,现存的多数簇方案是面向同构网络.为此,面向异构网络,提出基于簇的分布式能量有效路由HDEEC(heterogeneous WSN distributed energy-efficient clustering)协议.HDEEC协议首先提出异构网络模型,考虑了普通节点、特优节点和超特优节点三级能量节点;然后,提出能量消耗模型;最后依据这两个模型,提出了簇头选择方案.HDEEC协议以平衡、有效方式动态改变节点被选为簇头的概率.仿真结果表明,提出的HDEEC协议能够有效延长网络寿命,比DEEC、DDEEC的网络寿命分别提高了72％、68％.     

Distance based thresholds for 2-tier relay nodes selection in WSN

Relay selection technique approaches are increasingly used to improve the reliability of Wireless Sensor Networks (WSNs) communication, thus providing energy efficiency and reducing energy consumption. Due to the adoption of direct transmission from Cluster Heads (CHs) to the Base Station (BS), faster energy depletion may arise. Hence, the deployment of proper relay nodes techniques in WSN is a crucial task. This paper proposed, implemented and evaluated a new technique for the selection of relay nodes according to the nearest distance with Base Station (BS) in a 2- tier network. The selection was performed using K-Optimum that target the selection of the nearest number of relay nodes and, at the same time, ensure that all CHs are connected to at least one corresponding relay node based on K-Mean approach. Simulation results show that the distance based threshold for relay nodes selection implemented in Relay Access Protocol (RAP) performs better than the Multi-Tier Protocol (MAP) and Stable Election Protocol (SEP) protocols in terms of First Node Dead (FND), Last Node Dead (LND) and network lifetime.     

A power- and coverage-aware clustering scheme for wireless sensor networks

A common and critical operation for wireless sensor networks is data gathering. The efficient clustering of a sensor network that can save energy and improve coverage efficiency is an important requirement for many upper layer network functions. This study concentrates on how to form clusters with high uniformity while prolonging the network lifetime. A novel clustering scheme named power- and coverage- aware clustering (PCC) is proposed, which can adaptively select cluster heads according to a hybrid of the nodes' residual energy and loyalty degree. Additionally, the PCC scheme is independent of node distribution or density, and it is free of node hardware limitations, such as self-locating capability and time synchronization. Experiment results show that the scheme performs well in terms of cluster size (and its standard deviation), number of nodes alive over time, total energy consumption, etc.     

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.     

基于簇的无线传感器网络交会路由协议

针对现有无线传感器网络(WSN)协议中更多消耗sink附近节点能量导致网络寿命短的问题,本文提出一种基于簇的无线传感器网络交会路由协议(Cluster-based Rendezvous Routing Protocol, CRRP)。该协议是基于交会的路由协议,其中在网络的中间构建交会区域,该交会区域划分整个网络区域并在传感器节点之间分配网络负载,这延长了网络寿命。此交会区域内的节点分为不同的簇,每个簇的簇头(CH)负责不同簇之间的通信,sink在此交会区域内发送其更新的位置信息,并且当传感器节点想要发送数据时,会从该交会区域检索sink的当前位置信息并直接将数据发送到sink。仿真实验结果表明,在能耗与网络寿命性能方面,本文CRRP协议优于Rendezvous协议、LBDD协议、Railroad协议和Ring协议。     

一种适用于煤矿井下无线传感网的能量均衡路由协议

矿井无线传感网的拓扑呈长距离带状,节点间能耗不均问题十分严重。非均匀分簇策略能从全局均衡节点能量负载,在矿井中具有良好的适用性。针对矿井传感网的带状特性提出了一个簇规模自适应调节的能量均衡分簇路由协议。协议根据节点离汇聚点的距离、剩余能量及分布密度来构造规模不等的簇。簇首的竞选以节点相对于周围候选者的能量水平为依据,避免了低能量节点被当选为簇首。簇间多跳路由算法依簇首近似线型的分布特点设计,不但考虑链路能耗最优,亦注重转发节点间的能量均衡。模拟实验结果表明,该路由协议显著平衡了网中节点能耗,延长了网络生存时间。     

无线传感器网络中高能力簇首节点部署问题

分簇对用于环境监测的无线传感器网络具有较好的适应性和节能性,由高能力节点担任簇首可以更好的实现节能并改善网络性能,从而延长网络生命期.当网络规模较大时,高能力簇首节点的部署问题是一类NP-hard问题.本文首先对此问题进行了形式化描述,进而分析了跳数与网络性能之间的关系,总结出跳数是影响网络能耗和报文传输实时性的主要影响因素,簇首的部署应使簇成员和簇首之间的跳数尽可能小.因此提出了一种基于K-平均的簇首部署策略(KMCD),通过有策略的部署少量簇首节点来实现网络性能的最优化.仿真分析表明,与现有算法相比,KMCD算法具有较好的节能性和实时性.     

一种基于改进粒子群的无线传感器网络层次化聚类协议

延长网络的生存周期是无线传感器网络路由设计的主要目标之一。簇头的高能耗是网络快速死亡的一个重要原因。提出一种基于改进粒子群PSO( Particle Swarm Optimation)的无线传感器网络聚类路由协议IPSOCH。利用中继节点来分担簇头数据转发的任务,减轻簇头节点的负载,并利用改进的粒子群算法通过节点的剩余能量信息和位置信息来选择簇头和中继节点。仿真实验表明,IPSOCH协议比起现有的几种路由协议,能有效提高能量使用率,延长网络生存周期。