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.     

Increasing network lifetime by balancing node energy consumption in heterogeneous sensor networks

Sensor nodes are powered by battery and have severe energy constraints. The typical many‐to‐one traffic pattern causes uneven energy consumption among sensor nodes, that is, sensor nodes near the base station or a cluster head have much heavier traffic burden and run out of power much faster than other nodes. The uneven node energy dissipation dramatically reduces sensor network lifetime. In a previous work, we presented the chessboard clustering scheme to increase network lifetime by balancing node energy consumption. To achieve good performance and scalability, we propose to form a heterogeneous sensor network by deploying a few powerful high‐end sensors in addition to a large number of low‐end sensors. In this paper, we design an efficient routing protocol based on the chessboard clustering scheme, and we compute the minimum node density for satisfying a given lifetime constraint. Simulation experiments show that the chessboard clustering‐based routing protocol balances node energy consumption very well and dramatically increases network lifetime, and it performs much better than two other clustering‐based schemes. Copyright © 2006 John Wiley & Sons, Ltd.     

移动无线传感网能量时延约束的自适应路由及性能评估

 针对间歇性连通的移动无线传感网提出一种能量时延约束的自适应路由协议(EDCA),EDCA由初始化阶段、转发决策阶段、转发阶段和等待阶段组成,传感器节点根据目标时延实时判断是否转发副本,并选择剩余能量多的节点进行副本转发.EDCA对平均时延和网络负载具有控制力,对网络环境变化具有自适应能力,能够有效延长网络生命周期.     

Efficient Energy Utilization Through Optimum Number of Sensor Node Distribution in Engineered Corona-Based (ONSD-EC) Wireless Sensor Network

An optimum sensor node deployment in wireless sensor network can sense the event precisely in many real time scenarios for example forests, habitat, battlefields, and precision agriculture. Due to these applications, it is necessary to distribute the sensor node in an efficient way to monitor the event precisely and to utilize maximum energy during network lifetime. In this paper, we consider the energy hole formation due to the unbalanced energy consumption in many-to-one wireless sensor network. We propose a novel method using the optimum number of sensor node Distribution in Engineered Corona-based wireless sensor network, in which the interested area is divided into a number of coronas. A mathematical models is proposed to find out the energy consumption rate and to distribute the optimum number of sensor node in each corona according to energy consumption rate. An algorithm is proposed to distribute the optimum number of sensor nodes in corona-based networks. Simulation result shows that the proposed technique utilized 95 % of the total energy of the network during network lifetime. The proposed technique also maximizes the network lifetime, data delivery and reduce the residual energy ratio during 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.     

A new load balancing and data collection algorithm for energy saving in wireless sensor networks

Data gathering is a major function of many applications in wireless sensor networks. The most important issue in designing a data gathering algorithm is how to save energy of sensor nodes while meeting the requirements of special applications or users. Wireless sensor networks are characterized by centralized data gathering, multi-hop communication and many to one traffic pattern. These three characteristics can lead to severe packet collision, network congestion and packet loss, and even result in hot-spots of energy consumption thus causing premature death of sensor nodes and entire network. In this paper, we propose a load balance data gathering algorithm that classifies sensor nodes into different layers according to their distance to sink node and furthermore, divides the sense zone into several clusters. Routing trees are established between sensor node and sink depending on the energy metric and communication cost. For saving energy consumption, the target of data aggregation scheme is adopted as well. Analysis and simulation results show that the algorithm we proposed provides more uniform energy consumption among sensor nodes and can prolong the lifetime of sensor networks.     

无线传感器网络自适应目标跟踪节点调度算法

在无线传感器网络中,设计合理的节点调度算法是提高网络感知能力、降低系统能耗的关键。在分析节点能耗模型的基础上,针对移动目标跟踪型网络应用,提出一种高能效的无线传感器网络自适应节点调度算法ANSTT。该算法根据节点对移动目标的感知能力,以及节点的相对剩余能量水平,自动调整节点工作模式。仿真实验表明,ANSTT算法在维持低感知延时、高目标感知率的同时,可有效降低系统能耗,延长网络寿命。     

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.     

An Energy-Efficient Routing Protocol for Event-Driven Dense Wireless Sensor Networks

The routing energy efficiency of a wireless sensor network is a crucial issue for the network lifetime. In this article, we propose MICRO (MInimum Cost Routing with Optimized data fusion), an energy-efficient routing protocol for event-driven dense wireless sensor networks. The proposed routing protocol is an improvement over the formerly proposed LEACH and PEGASIS protocol, which is designed to be implemented mainly with node computations rather than mainly with node communications. Moreover, in the routing computation the proposed scheme exploits a new cost function for energy balancing among sensor nodes, and uses an iterative scheme with optimized data fusions to compute the minimum-cost route for each event-detecting sensor node. Compared to the PEGASIS routing protocol, MICRO substantially improves the energy-efficiency of each route, by optimizing the trade-off between minimization of the total energy consumption of each route and the balancing of the energy state of each sensor node. It is demonstrated that the proposed protocol is able to outperform the LEACH and the PEGASIS protocols with respect to network lifetime by 100–300% and 10–100%, respectively.     

无线传感网络中基于蚁群的路由算法

In the wireless sensor networks, high efficient data routing for the limited energy resource networks is an important issue. By introducing Ant-colony algorithm, this paper proposes the wireless sensor network routing algorithm based on LEACH. During the construction of sensor network clusters, to avoid the node premature death because of the energy consumption, only the nodes whose residual energy is higher than the average energy can be chosen as the cluster heads. The method of repeated division is used to divide the clusters in sensor networks so that the numbers of the nodes in each cluster are balanced. The basic thought of ant-colony algorithm is adopted to realize the data routing between the cluster heads and sink nodes, and the maintenance of routing. The analysis and simulation showed that the proposed routing protocol not only can reduce the energy consumption, balance the energy consumption between nodes, but also prolong the network lifetime.     

基于繁忙因子的VVSN自适应双簇头分簇算法研究

在WSN中,节点之间不平衡通信消耗大量能量,因此网络生存时间较短。为改善网络整体生存时间,提出一种基于网络繁忙因子的簇头自适应切换算法。首先,算法一次性选举双簇头,再根据网络实际情况自适应切换簇头。然后,在簇头选举完成后,节点通过当前簇头与基站通信,若当前簇头的能量低于门限值,则网络中的当前簇头将被切换到次级簇头,此时节点通过次级簇头与基站通信,从而降低节点能耗,减少节点的死亡率。双簇头切换机制缩短了整体通信距离,缓解簇头节点过早死亡,使网络生存时间增长。仿真结果表明,该算法通过缩短整体通信距离可显著降低整体网络的能量消耗,增加网络的生存时间。     

基于生命期划分的无线传感器网络节能策略

提出一种基于生命期划分的节能数据传输策略.利用能耗判定建立多层簇,并在保证能耗均衡的基础上划分节点生命期,同时利用节点工作状态轮转,进一步延长了网络生存时间.仿真结果表明,本方法相比于其他一些能量有效的网络协议,拥有更长的网络生存时间及更均衡的网络能耗.     

Data Aggregation using Difference transfer for Load Reduction in Periodic Sensor Networks

One of the basic challenges in wireless sensor networks is energy conservation. Sensor nodes are energy constrained and prudent energy usage is of utmost importance. Data aggregation aims to reduce amount of data communicated across the network without loss in information, thereby reducing the energy costs, and increasing network lifetime. In this paper, we propose a novel, simple and easy to implement method to reduce the amount of periodic data transferred from the sensor nodes to the sink. Instead of sending a set of measures at the end of every time period, we propose sending the first measure, and for every subsequent measure in that time period, we send the difference with respect to first measure. Differences are represented by a group of binary bits. Differences are also chosen in an adaptive manner in order to maintain precision between the data measured at sensor nodes and data reconstructed from binary bit patterns at sink. We evaluated our technique against two real world data-sets with vastly different properties. Results indicate 85–88.5% reduction in amount of data sent and transmission energy.

     

基于离散多目标优化粒子群算法的多移动代理协作规划

无线传感器网络中多移动代理协作能快速高效地完成感知数据汇聚任务,但是随着移动代理访问数据源节点数的增加,移动代理携带的数据分组会逐渐增大,导致传感器节点能量负载不均衡,部分数据源节点能耗过快,网络生存期缩短。目前,针对该问题所设计的能耗均衡算法,多以降低多移动代理总能耗为目标,却未充分考虑部分数据源节点能量消耗过快对网络生存期造成的影响。提出离散多目标优化粒子群算法,以网络的总能耗和移动代理负载均衡作为适应度函数,在多移动代理协作路径规划中寻求近似最优解。通过仿真实验验证,所提出的多移动代理协作路径规划,在网络总能耗和网络生存期方面的性能优于同类其他算法。     

Nature inspired discrete firefly algorithm for optimal mobile data gathering in wireless sensor networks

Nowadays wireless sensor networks enhance the life of human beings by helping them through several applications like precision agriculture, health monitoring, landslide detection, pollution control, etc. The built-in sensors on a sensor node are used to measure the various events like temperature, vibration, gas emission, etc., in the remotely deployed unmanned environment. The limited energy constraint of the sensor node causes a huge impact on the lifetime of the deployed network. The data transmitted by each sensor node cause significant energy consumption and it has to be efficiently used to improve the lifetime of the network. The energy consumption can be reduced significantly by incorporating mobility on a sink node. Thus the mobile data gathering can result in reduced energy consumption among all sensor nodes while transmitting their data. A special mobile sink node named as the mobile data transporter (MDT) is introduced in this paper to collect the information from the sensor nodes by visiting each of them and finally it sends them to the base station. The Data collection by the MDT is formulated as a discrete optimization problem which is termed as a data gathering tour problem. To reduce the distance traveled by the MDT during its tour, a nature-inspired heuristic discrete firefly algorithm is proposed in this paper to optimally collect the data from the sensor nodes. The proposed algorithm computes an optimal order to visit the sensor nodes by the MDT to collect their data with minimal travel distance. The proposed algorithm is compared with tree-based data collection approaches and ant colony optimization approach. The results demonstrate that the proposed algorithm outperform other approaches minimizing the tour length under different scenarios.     

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.     

Looking for network functionalities’ extension by avoiding energy-compromised hotspots in wireless sensor networks

The vast literature on the wireless sensor research community contains many valuable proposals for managing energy consumption, the most important factor that determines sensors’ lifetime. Interesting researches have been facing this requirement by focusing on the extension of the entire network lifetime: either by switching between node states (active, sleep) or by using energy-efficient routing. We argue that a better extension of the network lifetime can be obtained if an efficient combination of management mechanisms can be performed at the energy of each single sensor and at the load distribution over the network. Considering these two accuracy levels (i.e., node and network), this paper presents a new approach that uses cost functions to choose energy-efficient routes. In particular, by making different energy considerations at a node level, our approach distributes routing load, avoiding, thus, energy-compromised hotspots that may cause network disconnections. The proposed cost functions have completely decentralized and adaptive behavior and take into consideration the end-to-end energy consumption, the remaining energy of nodes, and the number of transmissions a node can make before its energy depletion. Our simulation results show that, though slightly increasing path lengths from sensor to sink nodes, some proposed cost functions (1) improve significantly the network lifetime for different neighborhood density degrees, while (2) preserving network connectivity for a longer period of time.     

Maximum Lifetime Routing Problem in Duty-Cycling Sensor Networks

In order to extend the lifetime of a wireless sensor network, the energy consumption of individual sensor nodes need to be minimized. This can be achieved by minimizing the idle listening time with duty cycling mechanism and/or minimizing the number of communications per node. The nodes will have different relay loads for different routing strategies: therefore, the routing problem is important factor in minimization of the number of communications per node. In this paper, we investigate achievable network lifetime with a routing mechanism on top of an existing duty-cycling scheme. To this end, we formulated the routing problem for duty-cycling sensor network as a linear programming problem with the objective of maximizing the network lifetime. Using the developed linear programming formulation, we investigate the relationship between network lifetime and duty-cycling parameter for different data generation rates and determine the minimum duty-cycling parameter that meets the application requirements. To the best of our knowledge, this is the first mathematical programming formulation which addresses the maximum lifetime routing problem in duty-cycling sensor network. In order to illustrate the application of the analytical model, we solved the problem for different parameter settings.     

A fuzzy decision scheme for relay selection in cooperative wireless sensor network

In energy‐constrained military wireless sensor networks, minimizing the bit error rate (BER) with little compromise on network lifetime is one of the most challenging issues. This paper presents a new relay selection based on fuzzy logic (RSFL) scheme which provides balance between these parameters. The proposed scheme considers node's residual energy and path loss of the relay‐destination link as the input parameters for the selection of the relay node. The relay node selection by fuzzy logic is based on prioritizing higher residual energy and minimum path loss. To evaluate the performance on wireless sensor network, we compare the proposed scheme with the three existing relay selection strategies, ie, random, maximum residual energy based relay selection (MaxRes), and minimum energy consumption based relay selection (MinEnCon). The simulation results of the proposed scheme in terms of network lifetime, BER, Network Survivability Index (NSI), and average energy of network nodes have been presented and compared with different relay selection schemes. The simulation results show that the proposed RSFL scheme has the lowest BER, moderate network lifetime, average energy, and NSI.     

Sleep scheduling with expected common coverage in wireless sensor networks

Sleep scheduling, which is putting some sensor nodes into sleep mode without harming network functionality, is a common method to reduce energy consumption in dense wireless sensor networks. This paper proposes a distributed and energy efficient sleep scheduling and routing scheme that can be used to extend the lifetime of a sensor network while maintaining a user defined coverage and connectivity. The scheme can activate and deactivate the three basic units of a sensor node (sensing, processing, and communication units) independently. The paper also provides a probabilistic method to estimate how much the sensing area of a node is covered by other active nodes in its neighborhood. The method is utilized by the proposed scheduling and routing scheme to reduce the control message overhead while deciding the next modes (full-active, semi-active, inactive/sleeping) of sensor nodes. We evaluated our estimation method and scheduling scheme via simulation experiments and compared our scheme also with another scheme. The results validate our probabilistic method for coverage estimation and show that our sleep scheduling and routing scheme can significantly increase the network lifetime while keeping the message complexity low and preserving both connectivity and coverage.