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.     

Performance Optimization Based on Compressive Sensing for Wireless Sensor Networks

A routing algorithm, based on a dual cluster head redundant mechanism combined with compressive sensing data fusion algorithm, is proposed to improve reliability and reduce data redundancy of the industrial wireless sensor networks. The Dual cluster head alternation mechanism is adopted to balance the energy consumption of cluster head nodes. Through the compressive sensing data fusion technology to eliminate redundancy, effectively improve the network throughput of the sensor network. The simulation results show that the proposed algorithm is able to enhance the networks performance, significantly reduces the number of lost packets and extend the network’s lifetime.     

Joint Routing and Resource Allocation for Cluster Based Isolated Nodes in Cognitive Radio Wireless Sensor Networks

Clustering is an effective way to increase network lifetime but it leads to formation of isolated nodes in the wireless sensor network. These isolated sensor nodes forward data directly to sink and consume more energy which significantly reduces the network lifetime. In this article, we present how to maximize the network lifetime through joint routing and resource allocation with isolated nodes technique (JR-IN) between cluster head and isolated nodes in a cognitive based wireless sensor networks. In JR-IN technique the network area is divided into different layers and cluster size is formulated in each layer such that the size of the cluster remains unequal when it moves towards sink. Hence the cluster size is lager in the outermost layer compared to the cluster size in the inner most layer. To avoid inter cluster collision, we proposed different fixed channel to all the cluster heads in the network. For the intra cluster communication, the cluster member (sensor nodes) will lease the spectrum from the cluster head and forward data to their respective cluster head using TDMA technique. The periodical data gathering of cluster heads and forwarding the data to one hop cluster head may tend to lose energy faster and dies out quickly. We also propose in the JR-IN technique, the isolated nodes in the layer will take charge as a cluster head node and utilizes the resource allocated to the respective cluster head and forward the data to next hop cluster head. Simulation result shows that JR-IN outperforms the existing techniques, maximizes network lifetime and throughput and reduces the end to end delay.

     

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.     

一种无线传感器网络异构分簇模型的簇头调度方案

将无线传感器网络划分成簇会有效利用系统资源,近来提出的基于异构分簇模型的无线传感器网络,是指网络中存在多种不同能力的节点,能力强的节点自动成为簇头,这种网络避免了复杂的簇头选举过程并有效降低了普通节点的硬件复杂性和成本。但是,固定簇头的方法会削弱系统的负载均衡以及健壮性。为了解决这个问题,提出了一种基于自适应退避策略的簇头调度方案,该方案通过适当增加冗余度实现传感节点的k覆盖,增强了网络的健壮性。同时,依赖于地理信息和剩余电池能量信息,簇头节点通过自主周期性睡眠来保证系统负载的均衡分配,延长网络生存期。     

基于冗余节点的LEACH协议的改进

无线传感器网络中LEACH协议是一种典型的能有效延长网络生命周期的节能通信协议。因为其优秀的节能效果和其简单的规程得到了广泛的认可。但是LEACH簇头算法存在簇头开销大、簇头没有确定的数量和位置等不足。而在成簇后的稳定阶段,节点通过一跳通信将数据传送给簇头,簇头也通过一跳通信将聚合后的数据传送给基站,这样会造成簇头节点...     

An availability predictive trust factor‐based semi‐Markov mechanism for effective cluster head selection in wireless sensor networks

The longevity of the sensor networks purely depends on the effectiveness of a cluster head selection process that attributes towards effective network management in wireless sensor networks. However, the majority of the cluster head selection schemes are considered an unrealistic condition which ponders the sensor nodes that have the possibility of being selected as cluster heads are highly energy competitive and trustworthy. In this paper, an availability predictive trust factor‐based semi‐Markov mechanism (APTFSMM) was proposed for facilitating effective cluster head selection with the view to enhance its degree of longevity degree in wireless sensor networks. This proposed APTFSMM inherited the merits of semi‐Markov process for estimating availability predictive trust factor that quantifies the maximum likelihood probability under which it is selected as the cluster head through maximized exploration of multiple transition states of the sensors in the networks. This proposed APTFSMM is determined to be predominant in enhancing the lifetime of the sensor network by 31% with a significantly reduced energy consumption rate of 38% compared with the benchmarked cluster head selection approaches considered for investigation.     

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.     

A Centralized Balance Clustering Routing Protocol for Wireless Sensor Network

Routing protocol plays a role of great importance in the performance of wireless sensor networks (WSNs). A centralized balance clustering routing protocol based on location is proposed for WSN with random distribution in this paper. In order to keep clustering balanced through the whole lifetime of the network and adapt to the non-uniform distribution of sensor nodes, we design a systemic algorithm for clustering. First, the algorithm determines the cluster number according to condition of the network, and adjusts the hexagonal clustering results to balance the number of nodes of each cluster. Second, it selects cluster heads in each cluster base on the energy and distribution of nodes, and optimizes the clustering results to minimize energy consumption. Finally, it allocates suitable time slots for transmission to avoid collision. Simulation results demonstrate that the proposed protocol can balance the energy consumption and improve the network throughput and lifetime significantly.     

Energy-Efficient Routing Algorithm Based on Unequal Clustering and Connected Graph in Wireless Sensor Networks

Clustering and multi-hop routing algorithms substantially prolong the lifetime of wireless sensor networks (WSNs). However, they also result in the energy hole and network partition problems. In order to balance the load between multiple cluster heads, save the energy consumption of the inter-cluster routing, in this paper, we propose an energy-efficient routing algorithm based on Unequal Clustering Theory and Connected Graph Theory for WSN. The new algorithm optimizes and innovates in two aspects: cluster head election and clusters routing. In cluster head election, we take into consideration the vote-based measure and the transmission power of sensor nodes when to sectionalize these nodes into different unequal clusters. Then we introduce the connected graph theory for inter-cluster data communication in clusters routing. Eventually, a connected graph is constituted by the based station and all cluster heads. Simulation results show that, this new algorithm balances the energy consumption among sensor nodes, relieves the influence of energy-hole problem, improve the link quality, achieves a substantial improvement on reliability and efficiency of data transmission, and significantly prolongs the network lifetime.     

Target localization based on energy considerations in distributed sensor networks

Wireless distributed sensor networks are important for a number of strategic applications such as coordinated target detection, surveillance, and localization. Energy is a critical resource in wireless sensor networks and system lifetime needs to be prolonged through the use of energy-conscious sensing strategies during system operation. We propose an energy-aware target detection and localization strategy for cluster-based wireless sensor networks. The proposed method is based on an a posteriori algorithm with a two-step communication protocol between the cluster head and the sensors within the cluster. Based on a limited amount of data received from the sensor nodes, the cluster head executes a localization procedure to determine the subset of sensors that must be queried for detailed target information. This approach reduces both energy consumption and communication bandwidth requirements, and prolongs the lifetime of the wireless sensor network. Simulation results show that a large amount of energy is saved during target localization using this strategy.     

Data Aggregation for Increase Performance of Wireless Sensor Networks Using Learning Automata Approach

Wireless sensor networks, a new generation of networks, are composed of a large numbers of nodes and the communication between nodes takes place wirelessly. The main purpose of these networks is collecting information about the environment surrounding the network sensors. The sensors collect and send the required information. There are many challenges and research areas concerned in the literature, one of which is power consumption in network nodes. Nodes in these networks have limited energy sources and generally consume more energy in long communication distances and therefore run out of battery very fast. This results in inefficacy in the whole system. One of the proposed solutions is data aggregation in wireless networks which leads to improved performance. Therefore, in this study an approach based on learning automata is proposed to achieve data aggregation which leads to dynamic network at any hypothetical region. This approach specifies a cluster head in the network and nodes send their data to the cluster head and the cluster head sends the information to the main receiver. Also each node can change its sensing rate using learning automata. Simulation results show that the proposed method increases the lifetime of the network and more nodes will be alive.

     

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。     

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.     

无线传感器网络模糊逻辑分簇路由协议

在无线传感器网络（Wireless Sensor Network,WSN）中,LEACH协议通过概率模型来选举簇头,由于没有考虑到传感器节点的分布情况和能量剩余等信息,可能会使得部分节点过早死亡.针对这一问题,提出基于模糊逻辑的分簇路由协议（DFLCP）.在预选簇头阶段,根据节点剩余能量等信息利用模糊逻辑计算出节点的竞争半径,使得簇头分布相对均匀;在簇头选举阶段,通过模糊逻辑确定节点成为簇头的概率.仿真结果表明：DFLCP协议可有效控制簇头节点的分布密度和簇的半径,均衡网络负载,延长节点平均生存时间.     

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.     

HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks

Topology control in a sensor network balances load on sensor nodes and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. We propose a novel distributed clustering approach for long-lived ad hoc sensor networks. Our proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. We present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED terminates in O(1) iterations, incurs low message overhead, and achieves fairly uniform cluster head distribution across the network. We prove that, with appropriate bounds on node density and intracluster and intercluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks. Simulation results demonstrate that our proposed approach is effective in prolonging the network lifetime and supporting scalable data aggregation.     

基于CMRA路由协议改进算法

为了延长无线传感网络的生存时间,需要设计满足高效率、低功耗的路由算法。一种CMRA（intercluster head multi-hop routing algorithm）算法被提出来,这种算法通过节点通信能量消耗模型建立最小能量路径树,但CMRA对于簇头选择的能量分配不均衡,造成簇头结点负载过重。提出一种新的路由算法CMRA-EE（CMRA-energy efficient）,在簇头选举阶段引入节点能量参数,同时将簇头节点能量与距离作为代价参数,从而平衡了网络节点能耗。通过仿真对CMRA-EE算法进行性能分析与评价,结果显示,CMRA-EE算法在延长无线传感网有效生存时间方面比CMRA算法有了明显的改善。     

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.     

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.