Fuzzy logic based clustering in wireless sensor networks: a survey

Wireless sensor networks (WSNs) have limited resources, thus extending the lifetime has always been an issue of great interest. Recent developments in WSNs have led to various new fuzzy systems, specifically designed for WSNs where energy awareness is an essential consideration. In several applications, the clustered WSN are known to perform better than flat WSN, if the energy consumption in clustering operation itself could be minimised. Routing in clustered WSN is very efficient, especially when the challenge of finding the optimum number of intermediate cluster heads can be resolved. Fortunately, several fuzzy logic based solutions have been proposed for these jobs. Both single- and two-level fuzzy logic approaches are being used for cluster head election in which several distinguished features of WSN have been considered in making a decision. This article surveys the recent fuzzy applications for cluster head selection in WSNs and presents a comparative study for the various approaches pursued.     

Energy-balanced unequal clustering protocol for wireless sensor networks

Clustering provides an effective way to prolong the lifetime of wireless sensor networks.One of the major issues of a clustering protocol is selecting an optimal group of sensor nodes as the cluster heads to divide the network.Another is the mode of inter-cluster communication.In this paper,an energy-balanced unequal clustering(EBUC)protocol is proposed and evaluated.By using the particle swarm optimization(PSO)algorithm,EBUC partitions all nodes into clusters of unequal size,in which the clusters closer to the base station have smaller size.The cluster heads of these clusters can preserve some more energy for the inter-cluster relay traffic and the 'hot-spots' problem can be avoided.For inter-cluster communication,EBUC adopts an energy-aware multihop routing to reduce the energy consumption of the cluster heads.Simulation results demonstrate that the protocol can efficiently decrease the dead speed of the nodes and prolong the network lifetime.     

Fuzzy-based unequal clustering protocol for underwater wireless sensor networks

Underwater wireless sensor network (UWSN) is a network made up of underwater sensor nodes, anchor nodes, surface sink nodes or surface stations, and the offshore sink node. Energy consumption, limited bandwidth, propagation delay, high bit error rate, stability, scalability, and network lifetime are the key challenges related to underwater wireless sensor networks. Clustering is used to mitigate these issues. In this work, fuzzy-based unequal clustering protocol (FBUCP) is proposed that does cluster head selection using fuzzy logic as it can deal with the uncertainties of the harsh atmosphere in the water. Cluster heads are selected using linguistic input variables like distance to the surface sink node, residual energy, and node density and linguistic output variables like cluster head advertisement radius and rank of underwater sensor nodes. Unequal clustering is used to have an unequal size of the cluster which deals with the problem of excess energy usage of the underwater sensor nodes near the surface sink node, called the hot spot problem. Data gathered by the cluster heads are transmitted to the surface sink node using neighboring cluster heads in the direction of the surface sink node. Dijkstra's shortest path algorithm is used for multi-hop and inter-cluster routing. The FBUCP is compared with the LEACH-UWSN, CDBR, and FBCA protocols for underwater wireless sensor networks. A comparative analysis shows that in first node dies, the FBUCP is up to 80% better, has 64.86% more network lifetime, has 91% more number of packets transmitted to the surface sink node, and is up to 58.81% more energy efficient than LEACH-UWSN, CDBR, and FBCA.     

Distributed fuzzy logic based energy‐aware and coverage preserving unequal clustering algorithm for wireless sensor networks

In an energy‐constrained wireless sensor networks (WSNs), clustering is found to be an effective strategy to minimize the energy depletion of sensor nodes. In clustered WSNs, network is partitioned into set of clusters, each having a coordinator called cluster head (CH), which collects data from its cluster members and forwards it to the base station (BS) via other CHs. Clustered WSNs often suffer from the hot spot problem where CHs closer to the BS die much early because of high energy consumption contributed by the data forwarding load. Such death of nodes results coverage holes in the network very early. In most applications of WSNs, coverage preservation of the target area is a primary measure of quality of service. Considering the energy limitation of sensors, most of the clustering algorithms designed for WSNs focus on energy efficiency while ignoring the coverage requirement. In this paper, we propose a distributed clustering algorithm that uses fuzzy logic to establish a trade‐off between the energy efficiency and coverage requirement. This algorithm considers both energy and coverage parameters during cluster formation to maximize the coverage preservation of target area. Further, to deal with hot spot problem, it forms unequal sized clusters such that more CHs are available closer to BS to share the high data forwarding load. The performance of the proposed clustering algorithm is compared with some of the well‐known existing algorithms under different network scenarios. The simulation results validate the superiority of our algorithm in network lifetime, coverage preservation, and energy efficiency.     

无线传感器网络的分布式能量有效非均匀成簇算法

研究了现有成簇路由算法,提出了分布式能量有效成簇路由算法,该算法主要包括3部分内容:一是选择候选簇头时通过引入平均能量因子来平衡全网节点的剩余能量情况;二是构造了基于能量和距离的能量消耗率函数以平衡节点的能量效率;三是首次提出了基于同构单跳网络的分布式能量有效非均匀成簇路由算法.理论和仿真结果均说明该算法优于EECS,生命周期比EECS延长达到约30%.     

Novel chemical reaction optimization based unequal clustering and routing algorithms for wireless sensor networks

Energy conserving of sensor nodes is the most crucial issue in the design of wireless sensor networks (WSNs). In a cluster based routing approach, cluster heads (CHs) cooperate with each other to forward their data to the base station (BS) via multi-hop routing. In this process, CHs closer to the BS are burdened with heavier relay traffic and tend to die prematurely which causes network partition is popularly known as a hot spot problem. To mitigate the hot spot problem, in this paper, we propose unequal clustering and routing algorithms based on novel chemical reaction optimization (nCRO) paradigm, we jointly call these algorithms as novel CRO based unequal clustering and routing algorithms (nCRO-UCRA). In clustering, we partition the network into unequal clusters such that smaller size clusters near to the sink and larger size clusters relatively far away from the sink. For this purpose, we develop the CH selection algorithm based on nCRO paradigm and assign the non-cluster head sensor nodes to the CHs based on derived cost function. Then, a routing algorithm is presented which is also based on nCRO based approach. All these algorithms are developed with the efficient schemes of molecular structure encoding and novel potential energy functions. The nCRO-UCRA is simulated extensively on various scenarios of WSNs and varying number of sensors and the CHs. The results are compared with some existing algorithms and original CRO based algorithm called as CRO-UCRA to show the superiority in terms of various performance metrics like residual energy, network lifetime, number of alive nodes, data packets received by the BS and convergence rate.     

HEEC: a hybrid unequal energy efficient clustering for wireless sensor networks

Wireless Networks - An important challenge in wireless sensor networks is energy conservation. Recently, several hybrid, dynamic and static clustering protocols have been proposed to solve this...     

基于分簇的无线传感器网络协议

本文针对目前无线传感器网络中传统MAC协议在动态性、低时延方面的不足,在前人研究的基础上,提出一种基于分簇的自适应AMAC协议.该协议将簇分为簇首节点和簇内成员节点,簇内成员节点可以根据自身的状态向簇首节点提出时隙申请,簇首节点对这些申请信息进行仲裁,从而及时调整时间帧的长度,使其能更符合当前网络的负载情况和拓扑结构....     

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.     

Energy efficient secured K means based unequal fuzzy clustering algorithm for efficient reprogramming in wireless sensor networks

Wireless Networks - Wireless Sensor Networks (WSNs) is a collection of tiny distributed sensor nodes that have been used to sense the physical parameters of the environment where it has been...     

基于分环多跳的无线传感网分簇路由算法

为了提高无线传感网在大区域情形下的能量效率,提出了一种分环多跳分簇路由算法RBMC(ring based multi-hop clustering routing algorithm).RBMC算法采用分环的方式实现簇头间的多跳通信,通过在不同环内构建大小不同的簇解决传感器网络中存在的"热点"问题,在不同的簇头选举策略下,能够同时满足节点能量同构及异构两种情形.仿真结果表明,在大区域环境下,分环多跳分簇路由算法能在很大程度上均衡节点能量消耗,延长网络的生命周期.     

An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic

Clustering is an effective approach for organizing a network into a connected hierarchy, load balancing, and prolonging the network lifetime. On the other hand, fuzzy logic is capable of wisely blending different parameters. This paper proposes an energy-aware distributed dynamic clustering protocol (ECPF) which applies three techniques: (1) non-probabilistic cluster head (CH) elections, (2) fuzzy logic, and (3) on demand clustering. The remaining energy of the nodes is the primary parameter for electing tentative CHs via a non-probabilistic fashion. A non-probabilistic CH election is implemented by introducing a delay inversely proportional to the residual energy of each node. Therefore, tentative CHs are selected based on their remaining energy. In addition, fuzzy logic is employed to evaluate the fitness (cost) of a node in order to choose a final CH from the set of neighboring tentative CHs. On the other hand, every regular (non CH) node elects to connect to the CH with the least fuzzy cost in its neighborhood. Besides, in ECPF, CH elections are performed sporadically (in contrast to performing it every round). Simulation results demonstrate that our approach performs better than well known protocols (LEACH, HEED, and CHEF) in terms of extending network lifetime and saving energy.     

FUCA: Fuzzy‐based unequal clustering algorithm to prolong the lifetime of wireless sensor networks

Wireless sensor network comprises billions of nodes that work collaboratively, gather data, and transmit to the sink. “Energy hole” or “hotspot” problem is a phenomenon in which nodes near to the sink die prematurely, which causes the network partition. This is because of the imbalance of the consumption of energy by the nodes in wireless sensor networks. This decreases the network's lifetime. Unequal clustering is a technique to cope up with this issue. In this paper, an algorithm, “fuzzy‐based unequal clustering algorithm,” is proposed to prolong the lifetime of the network. This protocol forms unequal clusters. This is to balance the energy consumption. Cluster head selection is done through fuzzy logic approach. Input variables are the distance to base station, residual energy, and density. Competition radius and rank are the two output fuzzy variables. Mamdani method is employed for fuzzy inference. The protocol is compared with well‐known algorithms, like low‐energy adaptive clustering hierarchy, energy‐aware unequal clustering fuzzy, multi‐objective fuzzy clustering algorithm, and fuzzy‐based unequal clustering under different network scenarios. In all the scenarios, the proposed protocol performs better. It extends the lifetime of the network as compared with its counterparts.     

新的无线传感器网络分簇算法

针对无线传感器网络节点能量受限的特点,提出了一种响应式分布分簇算法(RDCA,responsive distributedclustering algorithm).该算法不需预先得知节点自身及其他节点的位置信息,而仅根据局部拓扑信息快速进行分布式的簇头选举,并根据代价函数进行簇的划分,适用于周期性获取信息的无线传感器网络.分析与仿真表明,该算法具有良好的负载平衡性能和较小的协议开销,与LEACH协议相比,能够减少能量消耗,网络生存期大约延长了40%.     

Energy constraint clustering algorithms for wireless sensor networks

Using partitioning in sensor networks to create clusters for routing, data management, and for controlling communication has been proven as a way to ensure long range deployment and to deal with sensor network shortcomings such as limited energy and short communication ranges. Choosing a cluster head within each cluster is important because cluster heads use additional energy for their responsibilities and that burden needs to be carefully passed around among nodes in a cluster. Many existing protocols either choose cluster heads randomly or use nodes with the highest remaining energy. We present an Energy Constrained minimum Dominating Set based efficient clustering called ECDS to model the problem of optimally choosing cluster heads with energy constraints. Our proposed randomized distributed algorithm for the constrained dominating set runs in O(log n log Δ) rounds with high probability where Δ is the maximum degree of a node in the graph. We provide an approximation ratio for the ECDS algorithm of expected size 8H_Δ∣OPT∣ and with high probability a size of O(∣OPT∣log n) where n is the number of nodes, H is the harmonic function and OPT means the optimal size. We propose multiple extensions to the distributed algorithm for the energy constrained dominating set. We experimentally show that these extensions perform well in terms of energy usage, node lifetime, and clustering time in comparison and, thus, are very suitable for wireless sensor networks.     

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.     

Efficient clustering algorithms for self-organizing wireless sensor networks

Self-organization of wireless sensor networks, which involves network decomposition into connected clusters, is a challenging task because of the limited bandwidth and energy resources available in these networks. In this paper, we make contributions towards improving the efficiency of self-organization in wireless sensor networks. We first present a novel approach for message-efficient clustering, in which nodes allocate local “growth budgets” to neighbors. We introduce two algorithms that make use of this approach. We analyze the message complexity of these algorithms and provide performance results from simulations. The algorithms produce clusters of bounded size and low diameter, using significantly fewer messages than the earlier, commonly used, Expanding Ring approach. Next, we present a new randomized methodology for designing the timers of cluster initiators. This methodology provides a probabilistic guarantee that initiators will not interfere with each other. We derive an upper bound on the expected time for network decomposition that is logarithmic in the number of nodes in the network. We also present a variant that optimistically allows more concurrency among initiators and significantly reduces the network decomposition time. However, it produces slightly more clusters than the first method. Extensive simulations over different topologies confirm the analytical results and demonstrate that our proposed methodology scales to large networks.     

An unequal cluster-based routing protocol in wireless sensor networks

Clustering provides an effective method for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques; selecting cluster heads with more residual energy, and rotating cluster heads periodically to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, they rarely consider the hot spot problem in multihop sensor networks. When cluster heads cooperate with each other to forward their data to the base station, the cluster heads closer to the base station are burdened with heavier relay traffic and tend to die much faster, leaving areas of the network uncovered and causing network partitions. To mitigate the hot spot problem, we propose an Unequal Cluster-based Routing (UCR) protocol. It groups the nodes into clusters of unequal sizes. Cluster heads closer to the base station have smaller cluster sizes than those farther from the base station, thus they can preserve some energy for the inter-cluster data forwarding. A greedy geographic and energy-aware routing protocol is designed for the inter-cluster communication, which considers the tradeoff between the energy cost of relay paths and the residual energy of relay nodes. Simulation results show that UCR mitigates the hot spot problem and achieves an obvious improvement on the network lifetime. Guihai Chen obtained his B.S. degree from Nanjing University, M. Engineering from Southeast University, and PhD from University of Hong Kong. He visited Kyushu Institute of Technology, Japan in 1998 as a research fellow, and University of Queensland, Australia in 2000 as a visiting professor. During September 2001 to August 2003, he was a visiting professor at Wayne State University. He is now a full professor and deputy chair of Department of Computer Science, Nanjing University. Prof. Chen has published more than 100 papers in peer-reviewed journals and refereed conference proceedings in the areas of wireless sensor networks, high-performance computer architecture, peer-to-peer computing and performance evaluation. He has also served on technical program committees of numerous international conferences. He is a member of the IEEE Computer Society. Chengfa Li was born 1981 and obtained his Bachelor’s Degree in mathematics in 2003 and his Masters Degree in computer science in 2006, both from Nanjing University, China. He is now a system programmer at Lucent Technologies Nanjing Telecommunication Corporation. His research interests include wireless ad hoc and sensor networks. Mao Ye was born in 1981 and obtained his Bachelor’s Degree in computer science from Nanjing University, China, in 2004. He served as a research assistant At City University of Hong Kong from September 2005 to August 2006. He is now a PhD candidate with research interests in wireless networks, mobile computing, and distributed systems. Jie Wu is a professor in the Department of Computer Science and Engineering at Florida Atlantic University. He has published more than 300 papers in various journal and conference proceedings. His research interests are in the areas of mobile computing, routing protocols, fault-tolerant computing, and interconnection networks. Dr. Wu serves as an associate editor for the IEEE Transactions on Parallel and Distributed Systems and several other international journals. He served as an IEEE Computer Society Distinguished Visitor and is currently the chair of the IEEE Technical Committee on Distributed Processing (TCDP). He is a member of the ACM, a senior member of the IEEE, and a member of the IEEE Computer Society.     

Energy-efficiency clustering protocol in wireless sensor networks

The conventional clustering method has the unique potential to be the framework for power-conserving ad hoc networks. In this environment, studies on energy-efficient strategies such as sleeping mode and redirection have been reported, and recently some have even been adopted by some standards like Bluetooth and IEEE 802.11. However, consider wireless sensor networks. The devices employed are power-limited in nature, introducing the conventional clustering approach to the sensor networks provides a unique challenge due to the fact that cluster-heads, which are communication centers by default, tend to be heavily utilized and thus drained of their battery power rapidly. In this paper, we introduce a re-clustering strategy and a redirection scheme for cluster-based wireless sensor networks in order to address the power-conserving issues in such networks, while maintaining the merits of a clustering approach. Based on a practical energy model, simulation results show that the improved clustering method can obtain a longer lifetime when compared with the conventional clustering method.     

基于逻辑网格的无线传感器网络密钥分配方案

由于无线传感器网络能源受限、拓扑易变化等特性,需要解决其密钥管理机制涉及到的机密性、完整性、源端认证和无充足空间存储大量密钥信息等问题.针对当前研究工作的一些局限,提出了一种基于逻辑网格的无线传感器网络密钥分配方案,基于层簇式的网络拓扑,描述了系数矩阵求解、密钥设定和具体实现的流程.最后通过与多种现存方法(例如SPIN协议和逻辑密钥树方案)的仿真实验比较,验证了该方案在安全性、存储性和节能性方面的优势.