Low-Energy Adaptive Unequal Clustering Protocol Using Fuzzy c-Means in Wireless Sensor Networks

Clustering technique in wireless sensor networks incorporate proper utilization of the limited energy resources of the deployed sensor nodes with the highest residual energy that can be used to gather data and send the information. However, the problem of unbalanced energy consumption exists in a particular cluster node in the network. Some more powerful nodes act as cluster head to control sensor network operation when the network is organized into heterogeneous clusters. It is important to assume that energy consumption of these cluster head nodes is balanced. Often the network is organized into clusters of equal size where cluster head nodes bear unequal loads. Instead in this paper, we proposed a new protocol low-energy adaptive unequal clustering protocol using Fuzzy c-means in wireless sensor networks (LAUCF), an unequal clustering size model for the organization of network based on Fuzzy c-means (FCM) clustering algorithm, which can lead to more uniform energy dissipation among the cluster head nodes, thus increasing network lifetime. A heuristic comparison between our proposed protocol LAUCF and other different energy-aware protocol including low energy adaptive clustering hierarchy (LEACH) has been carried out. Simulation result shows that our proposed heterogeneous clustering approach using FCM protocol is more effective in prolonging the network lifetime compared with LEACH and other protocol for long run.     

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.     

Optimal load balanced clustering in homogeneous wireless sensor networks

Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering for hierarchical cluster‐based wireless sensor networks. We formulate the network design problem as mixed‐integer linear programming. Our contribution is 3‐fold: First, we propose an energy aware cluster head selection model for optimal cluster head selection. Then we propose a delay and energy‐aware routing model for optimal inter‐cluster communication. Finally, we propose an equal traffic for energy efficient clustering for optimal load balanced clustering. We consider the worst case scenario, where all nodes have the same capability and where there are no ways to use mobile sinks or add some powerful nodes as gateways. Thus, our models perform load balancing and maximize network lifetime with no need for special node capabilities such as mobility or heterogeneity or pre‐deployment, which would greatly simplify the problem. We show that the proposed models not only increase network lifetime but also minimize latency between sensor nodes. Numerical results show that energy consumption can be effectively balanced among sensor nodes, and stability period can be greatly extended using our models.     

Two-phase data traffic optimization of wireless sensor networks for prolonging network lifetime

Wireless network sensing and control systems are becoming increasingly important in many application domains due to advent of nanotechnology. The size of a wireless sensor network can easily reach hundreds or even thousands of sensor nodes. Since these types of networks usually have limited battery resources, power consumption optimization for prolonging system lifetime of such networks have received a great attention by the researchers in this field in recent years. In this paper, a centralized approach for clustering and data transmission mechanism is proposed that optimizes the power consumption and hence lifetime of the network. The mechanism is comprised of two phases. In the first phase, a mechanism based on a centralized cluster head selection that utilizes information such as nodes residual energies and their locations in the network is proposed in order to select the most appropriate candidates as cluster heads. In the second phase, the concept of a “window size” is introduced where minimization of the number of cluster head changes of a node and consequently maximization of the network lifetime is considered. Simulation results validate that the proposed mechanism does effectively reduce data traffic and therefore increases network lifetime.     

An efficient cluster-based communication protocol for wireless sensor networks

A wireless sensor network is a network of large numbers of sensor nodes, where each sensor node is a tiny device that is equipped with a processing, sensing subsystem and a communication subsystem. The critical issue in wireless sensor networks is how to gather sensed data in an energy-efficient way, so that the network lifetime can be extended. The design of protocols for such wireless sensor networks has to be energy-aware in order to extend the lifetime of the network because it is difficult to recharge sensor node batteries. We propose a protocol to form clusters, select cluster heads, select cluster senders and determine appropriate routings in order to reduce overall energy consumption and enhance the network lifetime. Our clustering protocol is called an Efficient Cluster-Based Communication Protocol (ECOMP) for Wireless Sensor Networks. In ECOMP, each sensor node consumes a small amount of transmitting energy in order to reach the neighbour sensor node in the bidirectional ring, and the cluster heads do not need to receive any sensed data from member nodes. The simulation results show that ECOMP significantly minimises energy consumption of sensor nodes and extends the network lifetime, compared with existing clustering protocol.     

Near optimal cluster-head selection for wireless sensor networks

Clustering in wireless sensor networks is an effective way to save energy and reuse band- width. To our best knowledge, most of the clustering protocols proposed in literature are of a dynamic type, where cluster heads are selected in each period, followed by cluster formation. In this paper, a new static type clustering method called Hausdorff clustering, which is based on the location of sensor nodes as well as communication efficiency and network connectivity, is proposed. The cluster head, however, is rotated within the cluster by a fuzzy logic algorithm that optimizes the network lifetime. Simulation results show that this approach can significantly increase the lifetime of the sensor network.     

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.     

Clustering and multi-hop routing with power control in wireless sensor networks

Clustering routing protocols excel in several aspects of wireless sensor networks (WSNs). This article proposes a clustering and multihop routing protocol (CMRP). In CMRP, a node independently makes its decision to compete for becoming a cluster head or join a cluster, according to its residual energy and average broadcast power of all its neighbors. To minimize the power consumption of the cluster head, CMRP sends the data in a power-aware multihop manner to the base station (BS) through a quasi-fixed route (QFR). In addition, CMRP presents a transmission power control algorithm with dynamic intercluster neighbor position estimation (DCNPE) to save energy. Simulation results show that the performance of CMRP is better than the hybrid, energy-efficient, distributed clustering approach (HEED). In the best case, CMRP increases the sensor network lifetime by 150.2%.     

一种基于多权值优化的无线传感网分簇算法的研究

在无线传感网(WSN)中,网络的拓扑结构影响传感器节点的负载平衡,关系网络的容量与生存周期,而分簇结构是一种有效的拓扑控制方式。该文着眼于无线传感网络的拓扑结构,提出基于多权值的分簇算法MWBC(Multi-WeightBasedClustering),在初期通过节点间的信息交互,获得较多的局部网络信息,如:节点的度、当前能量值、发射功率、链路质量、相对位置等,在此基础上根据不同的网络应用背景作出不同的分簇决策,并预设簇的最大规模以利于接入协议的资源管理与分配。仿真结果表明,与具有代表性的分簇算法LEACH与HEED相比,在分簇的合理性上有较大的优势。     

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.     

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.     

A Green Clustering Protocol for Mobile Sensor Network Using Particle Swarm Optimization

Energy consumption of sensor nodes is one of the crucial issues in prolonging the lifetime of wireless sensor networks. One of the methods that can improve the utilization of sensor nodes batteries is the clustering method. In this paper, we propose a green clustering protocol for mobile sensor networks using particle swarm optimization (PSO) algorithm. We define a new fitness function that can optimize the energy consumption of the whole network and minimize the relative distance between cluster heads and their respective member nodes. We also take into account the mobility factor when defining the cluster membership, so that the sensor nodes can join the cluster that has the similar mobility pattern. The performance of the proposed protocol is compared with well-known clustering protocols developed for wireless sensor networks such as LEACH (low-energy adaptive clustering hierarchy) and protocols designed for sensor networks with mobile nodes called CM-IR (clustering mobility-invalid round). In addition, we also modify the improved version of LEACH called MLEACH-C, so that it is applicable to the mobile sensor nodes environment. Simulation results demonstrate that the proposed protocol using PSO algorithm can improve the energy consumption of the network, achieve better network lifetime, and increase the data delivered at the base station.     

Heterogeneous HEED Protocol for Wireless Sensor Networks

One of the important protocols for increasing the network lifetime in wireless sensor networks (WSNs) is hybrid energy efficient distributed (HEED) protocol. This protocol considers two parameters for deciding the cluster heads, i.e., residual energy and node density and has been designed for the homogeneous WSNs. In this paper, we consider the implementation of HEED for a heterogeneous network. Depending upon the type of nodes, it defines one-level, two-level, and three-level heterogeneity and accordingly the implementation of HEED is referred to as hetHEED-1, hetHEED-2, and hetHEED-3, respectively. We also consider one more parameter, i.e., distance and apply fuzzy logic to determine the cluster heads and accordingly the hetHEED-1, hetHEED-2, and hetHEED-3 are named as HEED-FL, hetHEED-FL-2, hetHEED-FL-3, respectively. The simulation results show that as the level of heterogeneity increases in the network, the nodes remain alive for longer time and the rate of energy dissipation decreases. And also, increasing the heterogeneity level helps sending more packets to the base station and increases the network lifetime. The increase in the network energy increases the network lifetime manifold. In fact, using fuzzy logic, the network lifetime increases by 114.85 % that of the original HEED without any increase in the network energy. Thus, the hetHEED-FL-3 provides the longest lifetime (387.94 % increase) in lifetime at the cost of 19 % increase in network energy), sends maximum number of packets to the base station, and has minimum rate of energy dissipation.     

多簇无线传感网的优化生存时间近邻功率控制算法

针对非均匀分布的无线传感网的生存时间问题,提出多簇无线传感网的优化生存时间近邻功率控制(NPCAOL_MC)算法。该算法采用K-means算法确定网络的簇个数和对应每个簇的节点,利用近邻算法评估每个簇的节点密度,确定簇的最优通信距离。结合Friss自由空间模型计算当前簇的最优发送功率。Sink节点广播通知其他节点,如果是同一簇内的节点相互通信,则采用簇最优功率发送数据,否则采用默认最大发送功率发送数据。仿真结果表明,利用NPCAOL_MC算法可以分析整个网络节点的位置信息,采用簇最优发送功率发送数据,从而提高生存时间,并使能耗经济有效。在密度分布不均的无线传感网中,NPCAOL_MC比采用固定发送功率的Ratio_w算法更优。     

无线传感器网络中簇首选择算法研究

传统的低功耗自适应集簇分层型协议（LEACH）算法在选择簇首时未能考虑到节点剩余能量对网络寿命的影响,使得簇首分布不够合理。为了克服该问题,在完全分布式成簇算法（HEED）协议的基础上,设计了一种根据节点剩余能量选择簇首的算法。在该算法中,剩余能量越大的节点越有可能成为簇首,进而承担更多数据传输责任,能量消耗更加平均,增强了算法的健壮性。仿真结果证实,提出的算法可以有效提高网络能量的使用效率,减少功耗,延长网络生存时间。     

A green cluster‐based routing scheme for large‐scale wireless sensor networks

In wireless sensor networks (WSNs), clustering has been shown to be an efficient technique to improve scalability and network lifetime. In clustered networks, clustering creates unequal load distribution among cluster heads (CHs) and cluster member (CM) nodes. As a result, the entire network is subject to premature death because of the deficient active nodes within the network. In this paper, we present clustering‐based routing algorithms that can balance out the trade‐off between load distribution and network lifetime “green cluster‐based routing scheme.” This paper proposes a new energy‐aware green cluster‐based routing algorithm to preventing premature death of large‐scale dense WSNs. To deal with the uncertainty present in network information, a fuzzy rule‐based node classification model is proposed for clustering. Its primary benefits are flexibility in selecting effective CHs, reliability in distributing CHs overload among the other nodes, and reducing communication overhead and cluster formation time in highly dense areas. In addition, we propose a routing scheme that balances the load among sensors. The proposed scheme is evaluated through simulations to compare our scheme with the existing algorithms available in the literature. The numerical results show the relevance and improved efficiency of our scheme.     

An unequal cluster-based routing scheme for multi-level heterogeneous wireless sensor networks

In wireless sensor networks (WSNs), clustering can significantly reduce energy dissipation of nodes, and also increase communication load of cluster heads. When multi-hop communication model is adopted in clustering, “energy hole” problem may occur due to unbalanced energy consumption among cluster heads. Recently, many multi-hop clustering protocols have been proposed to solve this problem. And the main way is using unequal clustering to control the size of clusters. However, many of these protocols are about homogeneous networks and few are about heterogeneous networks. In this paper, we present an unequal cluster-based routing scheme for WSNs with multi-level energy heterogeneity called UCR-H. The sensor field is partitioned into a number of equal-size rectangular units. We first calculate the number of clusters in each unit by balancing energy consumption among the cluster heads in different units. And then we find the optimal number of units by minimizing the total energy consumption of inter-cluster forwarding. Finally, the size of clusters in each unit is elaborately designed based on node’s energy level and the number of clusters in this unit. And a threshold is also designed to avoid excessive punishment to the nodes with higher energy level. Simulation results show that our scheme effectively mitigates the “energy hole” problem and achieves an obvious improvement on the network lifetime.     

An intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks

Overlapping is one of the topics in wireless sensor networks that is considered by researchers in the last decades. An appropriate overlapping management system can prolong network lifetime and decrease network recovery time. This paper proposes an intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks, called IKOCP. This protocol uses some of the intelligent and knowledge‐based systems to construct a robust overlapping strategy for sensor networks. The overall network is partitioned to several regions by a proposed multicriteria decision‐making controller to monitor both small‐scale and large‐scale areas. Each region is managed by a sink, where the whole network is managed by a base station. The sensor nodes are categorized by various clusters using the low‐energy adaptive clustering hierarchy (LEACH)‐improved protocol in a way that the value of p is defined by a proposed support vector machine–based mechanism. A proposed fuzzy system determines that noncluster heads associate with several clusters in order to manage overlapping conditions over the network. Cluster heads are changed into clusters in a period by a suggested utility function. Since network lifetime should be prolonged and network traffic should be alleviated, a data aggregation mechanism is proposed to transmit only crucial data packets from cluster heads to sinks. Cluster heads apply a weighted criteria matrix to perform an inner‐cluster routing for transmitting data packets to sinks. Simulation results demonstrate that the proposed protocol surpasses the existing methods in terms of the number of alive nodes, network lifetime, average time to recover, dead time of first node, and dead time of last node.     

A novel residual energy‐based distributed clustering and routing approach for performance study of wireless sensor network

Non‐uniform energy consumption during operation of a cluster‐based routing protocol for large‐scale wireless sensor networks (WSN) is major area of concern. Unbalanced energy consumption in the wireless network results in early node death and reduces the network lifetime. This is because nodes near the sink are overloaded in terms of data traffic compared with the far away nodes resulting in node deaths. In this work, a novel residual energy–based distributed clustering and routing (REDCR) protocol has been proposed, which allows multi‐hop communication based on cuckoo‐search (CS) algorithm and low‐energy adaptive‐clustering–hierarchy (LEACH) protocol. LEACH protocol allows choice of possible cluster heads by rotation at every round of data transmission by a newly developed objective function based on residual energy of the nodes. The information about the location and energy of the nodes is forwarded to the sink node where CS algorithm is implemented to choose optimal number of cluster heads and their positions in the network. This approach helps in uniform distribution of the cluster heads throughout the network and enhances the network stability. Several case studies have been performed by varying the position of the base stations and by changing the number of nodes in the area of application. The proposed REDCR protocol shows significant improvement by an average of 15% for network throughput, 25% for network scalability, 30% for network stability, 33% for residual energy conservation, and 60% for network lifetime proving this approach to be more acceptable one in near future.     

DUCR: Distributed unequal cluster‐based routing algorithm for heterogeneous wireless sensor networks

Designing energy efficient communication protocols for wireless sensor networks (WSNs) to conserve the sensors' energy is one of the prime concerns. Clustering in WSNs significantly reduces the energy consumption in which the nodes are organized in clusters, each having a cluster head (CH). The CHs collect data from their cluster members and transmit it to the base station via a single or multihop communication. The main issue in such mechanism is how to associate the nodes to CHs and how to route the data of CHs so that the overall load on CHs are balanced. Since the sensor nodes operate autonomously, the methods designed for WSNs should be of distributed nature, i.e., each node should run it using its local information only. Considering these issues, we propose a distributed multiobjective‐based clustering method to assign a sensor node to appropriate CH so that the load is balanced. We also propose an energy‐efficient routing algorithm to balance the relay load among the CHs. In case any CH dies, we propose a recovery strategy for its cluster members. All our proposed methods are completely distributed in nature. Simulation results demonstrate the efficiency of the proposed algorithm in terms of energy consumption and hence prolonging the network lifetime. We compare the performance of the proposed algorithm with some existing algorithms in terms of number of alive nodes, network lifetime, energy efficiency, and energy population.