Energy Efficient Clustering Algorithm Research Articles

Split and Merge LEACH Based Routing Algorithm for Wireless Sensor Networks

Hierarchical routing and clustering mechanisms in Wireless Sensor Networks (WSNs) help to reduce both the energy consumption, and the overhead that is created when all the sensor nodes in the network are sending information to the central data collection point or base station. LEACH (Low Energy Adaptive Clustering Hierarchy) is one of the most well-known energy efficient clustering algorithms for WSNs. In this paper, we extend the LEACH protocol to (LEACH-SM) protocol by introducing a Split and Merge stage to improve the performance and robustness. We consider the following design aspects: non-uniform distribution of sensors, cluster re-formation by splitting or merging clusters conditionally, routes maintenance, and nodes mobility. OPNET, a well-known simulator tool, is used to simulate LEACH-SM in order to evaluate the performance of the proposed protocol. Simulation results and comparisons with existing protocols show the effectiveness and strength of the proposed protocol in terms of enhancing the lifetime of the whole sensor network, where sensors are either static or mobile with low speed.

ENERGY EFFICIENT DISTRIBUTED CLUSTERING AND SCHEDULING ALGORITHM FOR WIRELESS SENSOR NETWORKS WITH NON-UNIFORM NODE DISTRIBUTION

Abstract- The lifetime of Randomly Distributed wireless sensor networks gets effected do to the imbalanced energy consumption in sensor nodes. In order to balance the energy consumption among sensor nodes an energy efficient clustering algorithm is proposed in this paper. EECS is divided into two cluster setup phase and steady state phase. The cluster head is selected by new cluster election algorithm which uses local information of sensor nodes. The allocation of time slots for member nodes is done as per data available in sensor nodes in steady state phase. The simulation results of EECS algorithm is better than EADC and SA-EADC algorithm in terms of network lifetime and energy consumption.

CEERA: Improved Clustring Based Energy Efficient Routing Algorithm for Mobile Ad Hoc Networks

Ubiquitous smart devices with embedded sensors are paving the way for mobile ad hoc networks (MANETs) that enable users to communicate directly, thereby playing a key role in Smart City and Internet of Things applications. In such smart environments, people with smart devices (nodes) can freely self-organize and form self-configuring MANETs to send and forward data packets to a destination over multiple hops via intermediate nodes. However, the energy consumption during routing remains a challenge in such ensemble mobile environments due to the limited battery capacity of mobile devices. Thus the effectiveness of MANET not only depends on control protocols but also on management of network topology and energy administration. Clustering in MANET is useful to make network more manageable. Many clustering protocols and algorithms are proposed to make network more stable and trusted. In this paper we depict the most prominent factor related to the MANET. The motive of this paper is to perform investigational study including: routing structure, storage method, overhead, cryptographic authentication and misbehavior of nodes to clearly address relevant problem in cluster based routing protocol and provide a suitable solution by proposing a energy efficient clustering algorithm named as clustering based energy efficient routing algorithm (CEERA).

Distributed Clustering Algorithm for Energy Efficiency and Load-Balance in Large-Scale Multi-Agent Systems

To improve the energy efficiency and load-balance in large-scale multi-agent systems, a large-scale distributed cluster algorithm is proposed. At first, a parameter describing the spatial distribution of agents is designed to assess the information spreading capability of an agent. Besides, a competition resolution mechanism is proposed to tackle the competition problem in large-scale multiagent systems. Thus, the proposed algorithm can balance the load, adjust the system network locally and dynamically, reduce system energy consumption. Finally, simulations are presented to demonstrate the superiority of the proposed algorithm.

Distributed Energy Efficient Clustering Algorithm to Optimal Cluster Head by Using Biogeography Based Optimization

Wireless sensor network consist of hundred to thousand of sensor nodes with limited energy capacity. It is generally difficult to recharge or replace these sensor nodes. Energy efficiency is thus a primary issue to maintain a wireless network. The problem of energy depletion of nodes is common for all data collection scenarios in which cluster head have a heavy burden of gathering and relaying information. In this paper we propose an energy efficient clustering algorithm “Distributed energy efficient clustering biogeography based optimization algorithm” to elect optimal cluster head based on highest residual energy and appropriate packet forwarding to the sink with respect to sensor nodes. This algorithm gives the better simulation results in comparison to DEEC algorithm.

Energy-efficient clustering algorithm based on game theory for wireless sensor networks

Clustering is a popular method to achieve energy efficiency and scalable performance in large-scale sensor networks. Many clustering algorithms were proposed to use energy efficiently, extend network life span, and improve data transfer. Clustered routing for selfish sensors is a recently proposed algorithm based on game theory. In clustered routing for selfish sensors, the sensor nodes campaign to be cluster heads in order to achieve equilibrium probability. However, this algorithm needs global information for the computation of probability and disregard the uneven energy dissipation from different nodes that serve as cluster heads, thereby causing some nodes to die quickly. Therefore, an energy-efficient clustering algorithm based on game theory is proposed in this study. In the cluster head selection phase, each node competes as potential cluster head by joining a localized clustering game, and a potential cluster head is selected to be a real cluster head through a properly designed probability method. Simulation results show that the life span of wireless sensor networks extended by our algorithm becomes longer than those extended by low-energy adaptive clustering hierarchy and clustered routing for selfish sensors when proper parameters are used.

Firefly Algorithm Solution to Improving Threshold Distributed Energy Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks

Firefly Algorithm Solution to Improving Threshold Distributed Energy Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks

Firefly Algorithm Solution to Improving Threshold Distributed Energy Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks

The improvement of the lifetime of heterogeneous wireless sensor networks is a challenge for many researches. One of the most important protocols to achieve this goal is to divide the network into clusters that run by a single node called cluster head and the others have attached. However, all nodes must form the cluster including the nearest nodes to the base station which should be excluded from the clustering process. Furthermore these nodes consume more energy since each member node communicates directly with their cluster head and not with the base station. To eliminate these notes from cluster process, we need to formulate a new energy total of the network which depends on the number of these nodes. In this paper we propose a new technic to optimize this energy which basing on the firefly algorithm. The developed approach allows the boundary of the excluded nodes efficiently. Computer simulation in MATLAB proves the superiority of this method concerning the increase of the lifetime and the number of the received packet messages compared to the others protocols.

SURVEY ON CLUSTERING ENERGY EFFICIENT IN WIRELESS SENSOR NETWORKS

A Wireless Sensor Network is a consisting of spatially distributed autonomous systems with a communication for monitoring and recording conditions at diverse locations. WSN is built of nodes from a few to several hundred or even thousand, where each node is connected to one sensors. WSN is an emerging and attractive research area in which several applications such as health care, smart home, agriculture, and military are making use of it. Energy efficiency is one of the most important issues in wireless sensor network. A cluster is a group of servers and other machines act like a single system. Clusters enable high availability, load balancing and parallel processing in certain situation. Clustering algorithm is used to reduce the energy consumption and improves the lifetime of the network. Clustering reduces traffic flow, minimizes energy consumption, increase residual energy and extend the lifetime of the sensor network. This paper includes study of various energy efficient clustering algorithms in Wireless Sensor Networks.

An optimal clustering mechanism based on Fuzzy-C means for wireless sensor networks

In order to balance the node’s energy consumption and extend the lifetime under energy-constrained wireless sensor networks, an energy-efficient clustering algorithm based on Fuzzy-C means for wireless sensor networks is proposed. Taking into account the uneven distribution of the sensor nodes and the uncertainty of the radio channel, the cluster formation process of nodes is modeled as a fuzzy partition of sample space in this paper. Firstly, the overall energy consumption of the networks is analyzed, and the optimal number of cluster heads is estimated based on node’s density. Secondly, in the design of the objective function, the distance from the node to the cluster head and the weight of the membership values are considered. Then, the improved Fuzzy-C means clustering algorithm is proposed to divide the sensor nodes into a specified number of clusters. Finally, a single hop communication mode is used for intra cluster communication, and inter cluster communication adopts a multi-hop communication mode. The simulation results show that the proposed algorithm can obtain uniform spatial distribution of cluster heads and balance the energy consumption of network effectively.

HSCA: a novel harmony search based efficient clustering in heterogeneous WSNs

Clustering objective reasons scalability, fault tolerance, data aggregation or fusion, load balancing of cluster heads, stabilized network topology, maximal network lifetime, increased connectivity, reduced routing delay, collision avoidance and utilizing sleeping schemes in wireless sensor networks. Load balanced clustering effectively organize the network into a connected hierarchy. Clustering is a discrete problem that can have more than one solution under different operating constraints. In this scenario, meta-heuristic algorithms are found suitable because they give set of solutions in acceptable time constraints. In the literature, several analytical and meta-heuristic approaches have been developed for load balanced clustering. In this paper, a novel harmony search based energy efficient load balanced clustering algorithm is presented and it is tested on a large sample network. Results demonstrated that the proposed approach has faster convergence and gives reliable and efficient load balanced clustering as compared to conventional harmony search algorithm (HSA) and several other methods in the literature. Moreover, the robustness of the proposed approach is also verified for different cases of fixed and variable parameters of HSA.

Improving Distributed Energy Efficient Clustering Algorithm to Save Lifetime for Heterogeneous WSN

Improving Distributed Energy Efficient Clustering Algorithm to Save Lifetime for Heterogeneous WSN

An Energy-Efficient Layered Clustering Algorithm for Routing in Wireless Sensor Networks

Efficient energy management is a key issue in battery equipped wireless sensor networks (WSNs). The cluster based routing in WSNs is a prominent approach for energy conservation of the network which provides a hierarchical data collection mechanism. In order to maximize the energy conservation of sensor nodes, this paper proposes an Energy-efficient Layered Clustering Algorithm (ELCA) for routing in wireless sensor networks. ELCA constructs two layers of clusters to reduce the transmission rate and to balance the energy consumption of sensors. As early energy depletion of clusterheads (CHs) is a major limitation in clustering, this algorithm provides local remedy for energy suffering CHs through efficient CH substitution scheme. The performance of the proposed algorithm is analysed through extensive simulation experiments and verified by compared results with existing clustering algorithms.

A Cluster-Based Cooperative Spectrum Sensing Strategy to Maximize Achievable Throughput

In this paper, a novel cooperative spectrum sensing (CSS) strategy is proposed for cognitive radio networks (CRN) with imperfect reporting channels. This CSS strategy uses simultaneously four techniques to overcome undesirable effects of reporting channels, which are errors and overhead traffic. First, it uses an energy efficient clustering algorithm to maximize the CRN lifetime. Second, in each cluster, an incremental weighing fusion rule is used to improve the accuracy of local sensing performed by secondary users. Third, it selects more reliable improved decisions for sending to the fusion center, to decrease overhead traffic. Fourth, it employs a space–time block code to reduce the probability of errors in reporting channels. We determine the optimal settings of the proposed strategy, such as number of clusters, and their corresponding members by maximizing the achievable throughput of the CRN. Numerical and simulation results will prove the proposed CSS strategy yields the highest throughput for the CRN, while it guarantees the maximum lifetime of CRN, and maximum protection of primary users.

Modified Distributed Energy-Efficient Cluster for Heterogeneous Wireless Sensor Networks

The existing information collection system is a heterogeneous communication system which includes WSN (wireless sensor network) and wired network. In a heterogeneous information collection system, the survival time of nodes is very important for the stability of the network. How to reduce unnecessary energy consumption and make energy consumption more balanced requires special consideration. In this paper, we firstly explore the necessity of using clustering algorithms, and then analysis the existing heterogeneous wireless sensor network clustering algorithms, finally modify the existing DEEC (distributed energy-efficient clustering algorithm). The modifications of DEEC algorithm aims at the unstable performance. Simulation results show that the MDEEC (modified DEEC) has a greater network lifetime than DEEC.

A Power Controlled Energy-efficient Clustering Scheme for Heterogeneous Wireless Sensor Networks

Topology control is one of the most important technologies in wireless sensor networks. Many applications, combined with the methods of power control and hierarchical topology control strategy, can ultimately achieve the purpose of energy saving and topology optimization. Taken into consideration both power control and average energy of adjacent region, the paper proposes an energy efficient clustering algorithm for heterogeneous WSNs, called Power Controlled Clustering algorithm with Average Energy of Adjacent Region (PCCAEAR). In PCCAEAR, the cluster heads election process is divided into two stages. Firstly, the candidate cluster heads are elected by a probability based on the ratio between residual energy of each node and average energy of adjacent region. Secondly, a regional cluster head can be identified as the node with the highest residual energy within a certain range, while a candidate cluster head is communicating with other reachable candidate cluster heads in a certain transmission power. The cluster heads are elected in a stable number per round and evenly distributed in network, which make the network energy consumption more balanced. Simulation results show that PCCAEAR algorithm has a longer network lifetime, with the first node dead round number increasing by 6%, 14% and 22% respectively compared to DEEC, TCAC and REAC algorithms.

Energy Efficient Clustering Algorithm Using RFD Based Multi-hop Communication in Wireless Sensor Networks

In recent years, researchers have focused on many challenging aspects in the area of Wireless Sensor networks (WSNs). Development of energy efficient data collection protocols became the major concern in WSN. Clustering is one of the suitable techniques for energy efficiency. However, in clustering, the cluster are formed with uneven size. This unbalances the energy consumption among clusters which in turn reduce the lifetime of the network. In this paper, in order to balance the energy among clusters, a multi-hop concept is introduced for both intra and inter cluster communication. Firstly, the RFDMRP: River Formation Dynamics based Multi-hop Routing Protocol has been proposed. Later, this protocol is integrated with clustering and a new hybrid technique, named as hybrid clustering communication algorithm using RFDMRP (HCCRFD) is introduced. HCCRFD improves energy conservation by reducing overall packet transmission distance of intra and inter cluster communication, which results in increased network lifetime. Energy consumption of proposed algorithm has been analyzed mathematically. Also, the algorithms were simulated using MATLAB and outputs observed. The observed outputs were compared with existing protocols such as LEACH, DEEC, ERA, and $$ACH^2$$ACH2 in three different scenarios namely homogenous, heterogeneous environment, and node density. The comparison reveals that the proposed algorithm perform better than the existing protocols with respect to energy conservation and network lifetime.

Enhanced three Layer Energy Efficient Clustering (ETLEEC) Algorithm for Wireless Sensor Networks

Providing route stability isconsidered to be crucial task in Wireless Sensor Networks (WSNs). Hierarchical topologyis considered to preserve the energy of the sensor networks in which the nodes with the higher residual energy is used to gather the data. However, most of the existingmethods on clustering have adopted a two-layer hierarchy. This paper proposes an Enhanced Three Layer Energy Efficient Clustering (ETLEEC) algorithm for WSNs. The proposed method uses an extra layer called the lattice head where the cluster head collects the information form sensor nodes and sends it to the lattice head which in turn send to the base station. Simulations are performed in network simulator and the results show that the proposed system is more efficient in terms of energy and quality of service compared to all other existing distributed algorithms.

An Energy-Efficient Clustering Algorithm Using Fuzzy C-Means and Genetic Fuzzy System for Wireless Sensor Network

The energy efficiency in wireless sensor networks (WSNs) is a fundamental challenge. Cluster-based routing is an energy saving method in this type of networks. This paper presents an energy-efficient clustering algorithm based on fuzzy c-means algorithm and genetic fuzzy system (ECAFG). By using FCM algorithm, the clusters are formed, and then cluster heads (CHs) are selected utilizing GFS. The formed clusters will be remaining static but CHs are selected at the beginning of each round. FCM algorithm forms balanced clusters and distributes the consumed energy among them. Using static clusters also reduces the data overhead and consequently the energy consumption. In GFS, nodes energy, the distance from nodes to the base station and the distance from each node to its corresponding cluster center are considered as determining factors in CHs selection. Then, genetic algorithm is also used to obtain fuzzy if–then rules of GFS. Consequently, the system performance is improved and appropriate CHs can be selected, hence energy dissipation is reduced. The simulation results show that ECAFG, compared with the existing methods, significantly reduces the energy consumption of the sensor nodes, and prolongs the network lifetime.

A Survey of Energy Efficient Clustering Algorithms in Wireless Sensor Networks

To make the most of network lifetime in Wireless Sensor Networks (WSNs) the paths for data transfer are chosen in a manner that the total energy utilized along the path is minimized. To support high scalability and better data aggregation, sensor nodes are regularly assembled into disjoint, non overlapping subsets called clusters. Clusters create hierarchical WSNs which incorporate efficient utilization of limited resources of sensor nodes and thus extends network lifetime. The goal of this paper is to show a cutting edge review on clustering algorithms reported in the literature of WSNs. This paper presents various energy efficient clustering algorithms in WSNs.