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.     

Energy-Efficient Cluster Head Selection Scheme Based on Multiple Criteria Decision Making for Wireless Sensor Networks

Energy efficiency is an essential issue in the applications of wireless sensor networks (WSNs) all along. Clustering with data aggregation is a significant direction to improve energy efficiency through software. The selection of cluster head (CH) is the key issue in the clustering algorithm, which is also a multiple criteria decision making (MCDM) procedure. In this paper, a novel fuzzy multiple criteria decision making approach, which is based on trapezoidal fuzzy AHP and hierarchical fuzzy integral (FAHP), is introduced to optimize the selection of cluster heads to develop a distributed energy-efficient clustering algorithm. Energy status, QoS impact and location are taken into account simultaneously as the main factors that can influence the selection of cluster heads while each factor contains some sub-criteria. Fuzzy multiple attribute decision making is adopted to select optimal cluster heads by taking all factors into account synthetically. According to these criteria, each node computes a composite value by using fuzzy Integral. Then this composite value is mapped onto the time axis, and a time-trigger mechanism makes the node broadcast cluster head information. The rule that “first declaration wins” is adopted to form the cluster. Simulation results denote that our proposed scheme has longer lifetime and more eximious expansibility than other algorithms.     

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.     

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.     

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

The problems related to energy consumption and improvement of the network lifetime of WSN (wireless sensor network) have been considered. The base station (BS) location is the main concern in WSN. BSs are fixed, yet, they have the ability to move in some situations to collect the information from sensor nodes (SNs). Recently, introducing mobile sinks to WSNs has been proved to be an efficient way to extend the lifespan of the network. This paper proposes the assimilation of the fuzzy clustering approach and the Elephant Herding Optimization (EHO)‐Greedy algorithm for efficient routing in WSN. This work considers the separate sink nodes of a fixed sink and movable sink to decrease the utilization of energy. A fixed node is deployed randomly across the network, and the movable sink node moves to different locations across the network for collecting the data. Initially, the number of nodes is formed into the multiple clusters using the enhanced expectation maximization algorithm. After that, the cluster head (CH) selection done through a fuzzy approach by taking the account of three factors of residual energy, node centrality, and neighborhood overlap. A suitable collection of CH can extremely reduce the utilization of energy and also enhancing the lifespan. Finally, the routing protocol of the hybrid EHO‐Greedy algorithm is used for efficient data transmission. Simulation results display that the proposed technique is better to other existing approaches in regard to energy utilization and the system 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.     

一种无线传感器网络分簇路由算法研究

在分析LEACH协议的基础上提出一种基于能量和距离的多跳路由算法（CAED）。由基站依据节点剩余能量和簇头与基站的距离分别选出二层簇头,簇内节点利用单跳和多跳模式与簇头进行通信。仿真实验表明,新算法有效地平衡了节点的能量消耗,并显著地延长了网络的生命周期。     

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 Improved Fuzzy Unequal Clustering Algorithm for Wireless Sensor Network

This paper introduces IFUC, which is an Improved Fuzzy Unequal Clustering scheme for large scale wireless sensor networks (WSNs).It aims to balance the energy consumption and prolong the network lifetime. Our approach focuses on energy efficient clustering scheme and inter-cluster routing protocol. On the one hand, considering each node’s local information such as energy level, distance to base station and local density, we use fuzzy logic system to determine each node’s chance of becoming cluster head and estimate the cluster head competence radius. On the other hand, we use Ant Colony Optimization (ACO) method to construct the energy-aware routing between cluster heads and base station. It reduces and balances the energy consumption of cluster heads and solves the hot spots problem that occurs in multi-hop WSN routing protocol to a large extent. The validation experiment results have indicated that the proposed clustering scheme performs much better than many other methods such as LEACH, CHEF and EEUC.     

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.     

一种基于反馈的K-means分簇算法研究

针对典型的LEACH分簇式路由协议分簇不均匀,簇头节点分布随机导致网络能量消耗大的情况,本文提出一种基于死亡节点数目反馈的K-means分簇算法。首先通过K-means算法划分簇的个数,选择簇的中心节点为该簇的簇头,并通过位置集中性得到集中性较大的若干个节点为主簇头群,其中最大的为主簇头,自此完成初始化。此后用一个受死亡节点数调控的自适应打分函数更新每一轮的簇头和主簇头。主簇头只用于融合并传输数据并不负责感知环境信息。仿真实验结果表明:本算法相较LEACH以及传统的基于K-means的分簇算法,在整个网络的生存时间上分别提高了35%和25%。同时证明:反馈机制的加入和主簇头的选取都有利于网络寿命的提升。      

Sink selection and clustering using fuzzy‐based controller for wireless sensor networks

Applying multiple sink nodes in a large‐scale wireless sensor networks (WSN) can increase the scalability and lifetime of the network. The current sink selection mechanisms assume an unlimited amount of buffer and bandwidth for the sink nodes. This can be problematic in real‐world applications, especially when many cluster heads select a specific sink node and send their data to the sink at the same time. In this situation, the sink node may not have enough buffer to receive and process data; consequently, some packets are dropped. To mitigate these occasions, a fuzzy‐based controller with reduced rules is proposed for sink selection by considering the capacity of the sink nodes. The capacity of the sink nodes is estimated using the long short‐term memory (LSTM) technique. Then another fuzzy‐based controller with reduced rules is designed to select the cluster head. The fuzzy rules are reduced by employing R‐implications method. Reducing the number of fuzzy rules decreases the complexity of the fuzzy controllers. The results show the efficiency of the proposed sink selection and clustering techniques in terms of consumed energy, remaining energy, first node dead (FND), half nodes dead (HND), last node dead (LND), packet loss, and delay.     

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.     

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.     

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.     

一种改进的基于时间竞争成簇的路由算法

为平衡无线传感器网络中的簇头负载并进一步降低多跳传输能耗,文中提出了一种改进的基于时间竞争成簇的路由算法。该算法通过限制近基站节点成簇入簇,以防止近基站节点成簇入簇的节能收益无法补偿成簇入簇能耗;利用基站广播公共信息和基于时间机制成簇,以减少节点基本信息交换能耗;通过候选簇头中继来平衡簇头负载。候选簇头的评价函数综合考虑了剩余能量和最优跳数的理想路径,以期在保持中继负载平衡的基础上尽量降低多跳能耗。仿真结果显示,该算法较LEACH和DEBUC算法延长了以30%节点死亡为网络失效的网络生存周期,表明该算法在降低节点能耗和平衡负载方面是有效的。     

A Novel QoS Based Secure Unequal Clustering Protocol with Intrusion Detection System in Wireless Sensor Networks

Wireless sensor network (WSN) becomes a hot research topic owing to its application in different fields. Minimizing the energy dissipation, maximizing the network lifetime, and security are considered as the major quality of service (QoS) factors in the design of WSN. Clustering is a commonly employed energy-efficient technique; however, it results in a hot spot issue. This paper develops a novel secure unequal clustering protocol with intrusion detection technique to achieve QoS parameters like energy, lifetime, and security. Initially, the proposed model uses adaptive neuro fuzzy based clustering technique to select the tentative cluster heads (TCHs) using three input parameters such as residual energy, distance to base station (BS), and distance to neighbors. Then, the TCHs compete for final CHs and the optimal CHs are selected using the deer hunting optimization (DHO) algorithm. The DHO based clustering technique derives a fitness function using residual energy, distance to BS, node degree, node centrality, and link quality. To further improve the performance of the proposed method, the cluster maintenance phase is utilized for load balancing. Finally, to achieve security in cluster based WSN, an effective intrusion detection system using a deep belief network is executed on the CHs to identify the presence of intruders in the network. An extensive set of experiments were performed to ensure the superior performance of the proposed method interms of energy efficiency, network lifetime, packet delivery ratio, average delay, and intrusion detection rate.

     

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.     

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%.