Energy Efficient Clustering Algorithm Research Articles

무선 센서 네트워크의 수명연장을 위한 클러스터링 알고리즘

Recently, wireless sensor network(WSN) have been used in various fields to implement ubiquitous computing environment. WSN uses small, low cost and low power sensors in order to collect information from the sensor field. This paper proposes a clustering algorithm for energy efficiency of sensor nodes. The proposed algorithm is based on conventional LEACH, the representative clustering protocol for WSN and it prolongs network and nodes life time using sleep technique and changable transmission mode. The nodes of the proposed algorithm first calculate their clustering participation value based on the distance to the neighbor nodes. The nodes located in high density area will have clustering participation value and it ∙제1저자 : 김선철 ∙교신저자 : 조용환 ∙투고일 : 2014. 12. 19, 심사일 : 2015. 1. 13, 게재확정일 : 2015. 3. 25. * 충북대학교 컴퓨터공학과(Dept. of Computer Engineering, Chungbuk National University) ※ 이 논문은 2013년도 충북대학교 학술연구지원사업의 연구비 지원에 의하여 연구되었음 (This paper was supported by the research grant of the Chungbuk National University in 2013) 78 Journal of The Korea Society of Computer and Information April 2015 can turn to sleep mode. Besides, proposed algorithm can change transmission method from conventional single-hop transmission to multi-hop transmission according to the energy level of cluster head. Simulation results show that the proposed clustering algorithm outperforms conventional LEACH, especially non-uniformly deployed network. ▸

An Infrequent Route Selection Strategy for Unequally Clustered Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a vital role in the field of automation and control where sensing of information is the first step before any automated task could be performed. In order to facilitate such incessant tasks with less energy consumption ratio, clustering is incorporated at large to enhance the network lifetime. Traditional clustering algorithms become weak as the advancements in wireless technology progresses towards better energy efficient solutions. Unequal Cluster-based Routing (UCR) is one of the most efficient solutions to prolong the network lifetime and to solve the hotspot problem that is usually found in equal clustering mechanisms. In this paper, we present an Infrequent Route (IFR) selection strategy that helps in finding an efficient next hop to send the data aggregated by cluster heads to the base station. Also we provide an enhancement on the Energy-Efficient Unequal Clustering (EEUC) algorithm which is a part of the UCR protocol to facilitate the incorporation of IFR in WSNs in order to prolong the lifetime of the network. Simulation analysis is performed using the network simulator to show the efficiency of the proposed method.

Developed Distributed Energy-Efficient Clustering (DDEEC) Algorithm based on Fuzzy Logic Approach for Optimizing Energy Management in Heterogeneous WSNs

Energy Management can be improved by proficient clustering algorithms in heterogeneous wireless sensor networks. Coordination through cluster head selection provides efficient data aggregation that reduces communication overhead in the network. In this paper, we propose a fuzzy logic approach based DDEEC clustering algorithm which aims to prolong the lifetime of nodes in heterogeneous WSNs. We compare this algorithm with the PSO based DDEEC algorithm and original DDEEC algorithm according to the parameters of first node dies at different rounds and energy-efficiency metrics. The efficiency of proposed optimized fuzzy algorithm is proved by the Matlab experimental results. Simulation results exhibits that the proposed algorithm has higher energy efficiency and can improve life span of a node and data delivery at the base station over its comparatives.

Energy Efficient Clustering and Routing Algorithms for Wireless Sensor Networks: GA Based Approach

Energy efficient clustering and routing are two well known problems in wireless sensor networks. In this paper, we propose genetic algorithm based approaches for clustering and routing in wireless sensor networks. The clustering is based on residual energy of the gateways and distance from sensor nodes to their corresponding cluster head. The routing scheme is also based on the residual energy of the gateways along with a trade-off between transmission distance and number of forwards. We perform extensive simulations of the proposed algorithms and compare the simulation results with that of the existing algorithms. The results demonstrate that the proposed algorithms outperform the existing algorithms in terms of various performance metrics including energy consumption, number of active nodes, first gateway die and number of dead gateway per round.

TRACKING OF MOVING OBJECT IN WIRELESS SENSOR NETWORK

A Wireless Sensor Network is a collection of sensor nodes distributed into a network to monitor the environmental conditions and send the sensed data to the Base Station. Wireless Sensor Network is one of the rapidly developing area in which energy consumption is the most important aspect to be considered while tracking, monitoring, reporting and visualization of data. An Energy Efficient Prediction-based Clustering algorithm is proposed to track the moving object in wireless sensor network. This algorithm reduces the number of hops between transmitter and receiver nodes and also the number of transmitted packets. In this method, the sensor nodes are statically placed and clustered using LEACH-R algorithm. The Prediction based clustering algorithm is applied where few nodes are selected for tracking which uses the prediction mechanism to predict the next location of the moving object. The Current Location of the target is found using Trilateration algorithm. The Current Location or Predicted Location is sent to active Cluster Head from the leader node or the other node. Based on which node send the message to the Cluster Head, the Predicted or Current Location will be sent to the base station. In real time, the proposed work is applicable in traffic tracking and vehicle tracking. The experiment is carried out using Network Stimulator-2 environment. Simulation result shows that the proposed algorithm gives a better performance and reduces the energy consumption.

Heap and parameter-based load balanced clustering algorithms for wireless sensor networks

Clustering is an efficient technique to improve scalability and life time of wireless sensor networks WSNs. However, energy efficiency of the sensor nodes and load balancing of the cluster heads CHs are the two most important issues that must be addressed in designing clustering algorithm for WSNs. In this paper, we first present a clustering algorithm called energy efficient load-balanced clustering algorithm EELBCA that addresses energy efficiency of the sensor nodes and load balancing of the CHs in terms of cardinality of the clusters. EELBCA is a min-heap-based clustering algorithm which is shown to run in On log m time for n sensor nodes and m CHs. We next propose a parameter-based clustering algorithm PBCA that considers the energy efficiency of the sensor nodes and also the load balancing of the CHs in terms of overall energy consumption by incorporating communication load of the CHs with respect to the base station. The algorithm requires On log n time. The experimental results show the efficiency of the proposed algorithms in terms of execution time, load balancing, energy consumption, number of live sensor nodes and the number of live CHs.

An Energy Efficient Clustering Algorithm for Maximizing the Lifetime of Wireless Sensor Network

In this paper we propose an energy efficient clustering algorithm for maximizing the lifetime of WSNs. The proposed scheme decides the cluster head based on the energy level of the nodes, while tree topology is adopted to connect the nodes inside each cluster. A new model for deciding an optimal number of clusters is also derived. Computer simulation reveals that the proposed scheme significantly extends the network lifetime and message delivery ratio compared to the existing schemes.

Survey of different Clustering Algorithms used to Increase the Lifetime of Wireless Sensor Networks

Sensor Networks (WSNs) have gained increasing attention from both the research community and actual user. WSN consists of a large number of sensor nodes which are deployed over an area to perform local computations based on information gathered from surroundings and send this information to the main location. Each sensor node is equipped with a limited battery- power. The energy, the storage capacity and communication capability of sensor nodes are very limited. It is almost difficult to change or replace the battery. One of the fundamental design challenges in designing a Wireless Sensor Network is to maximize the network lifetime. To overcome this challenge, different methods were developed in last few years. The main objective in designing of WSN is to maximize the lifetime of WSN through minimizing the energy. There are different energy-efficient Clustering algorithms which prolongs the efficiency of WSN. Low-Energy Adaptive Clustering Hierarchy (LEACH) Protocol, Particle swarm optimization (PSO), Artificial Bee Colony (ABC) algorithm are different clustering mechanisms used to improve the efficiency of WSN. This paper discusses these three algorithms and compares this three with each other. KeywordsLEACH, Lifetime, PSO, ABC, Cluster-Head

A Proposal for Improve the Life-Time of Wireless Sensor Network

In the Wireless Sensor Network (WSN), sensor nodes are connected together through radio frequency (RF). Routing protocol is used to transmit data among sensor nodes. In the paper, we proposed a new routing protocol based on LEACH protocol. This is energy-efficient clustering algorithm. The proposed protocol enlarges WSN life-time by considering remaining energy and distance from nodes to BS in the election of cluster head. Comparing the result simulation between LEACH and proposed protocol showed that proposed protocol will prolong the network life-time.

Energy efficient fault tolerant clustering and routing algorithms for wireless sensor networks

Conservation of energy and fault tolerance are two major issues in the deployment of a wireless sensor network (WSN). Design of clustering and routing algorithms for a large scale WSN should incorporate both these issues for the long run operation of the network. In this paper, we propose distributed clustering and routing algorithms jointly referred as DFCR. The algorithm is shown to be energy efficient and fault tolerant. The DFCR uses a distributed run time recovery of the sensor nodes due to sudden failure of the cluster heads (CHs). It takes care of the sensor nodes which have no CH within their communication range. We perform extensive experiments on the proposed algorithm using various network scenarios. The experimental results are compared with the existing algorithms to demonstrate the strength of the algorithm in terms of various performance metrics.

LA-EEHSC: Learning automata-based energy efficient heterogeneous selective clustering for wireless sensor networks

Wireless sensor networks (WSNs) consist of many sensor nodes (SNs) which may be deployed at different geographical locations to perform multiple tasks such as monitoring, data aggregation, and data processing. During all these operations, energy of the SNs continuously depleted which results in the creation of energy holes in some regions. As SNs are battery operated and it is difficult to replace the battery of the SNs each time, so energy conservation is a paramount concern to increase the lifetime of the WSNs. It has been proved in the literature that clustering of SNs can be used for energy saving during various operations in WSNs. Keeping in view of the above issues, in this paper, we propose a new learning automata-based energy efficient heterogeneous selective clustering (LA-EEHSC) scheme for WSNs. Automaton is assumed to be located on each SN with two types of SNs, namely, normal and advanced are considered in the proposed scheme. Based upon the weighted election probability (WEP) of each group of SNs, Cluster Heads (CHs) are selected among the group of SNs by the automaton. Automaton at each SN receives reward or penalty from the environment based upon WEP of different SNs. An efficient learning automata-based energy efficient clustering algorithm is also proposed. Finally, first node die (FND) and last node alive (LNA) are selected as the key parameters for the measurement of lifetime of network field. Using these parameters, we have evaluated the performance of the proposed scheme in different network scenarios in comparison with the well-known existing protocols such as LEACH, LEACH-SC and SEP. The results obtained show that proposed scheme yields 5.89% improvement in lifetime and 21.14% improvement in stability in comparison to LEACH, LEACH-SC, and SEP.

Distance Based Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks

In wireless sensor networks Energy-efficient routing is an important issue due to the limited battery power within the network, Energy consumption is one of the important performance factors. Specifically for the election of cluster head selection and distance between the cluster head node and base station. The main objective of this proposed system is to reduce the energy consumption and prolong the network lifetime. This paper introduces a new clustering algorithm for energy efficient routing based on a cluster head selection

An Energy Efficient Clustering Algorithm based on Boundary Nodes Allocation for WSN

Instead of various advantages of wireless sensor networks, there is always a constraint related to energy consumption resulting in reduced lifetime of a wireless sensor network. In this paper, an energy efficient algorithm is proposed considering allocation of boundary nodes, removing clashes among clusters while distributing nodes thereby reducing overall energy consumption in the network as compared to LEACH (Low energy adaptive clustering hierarchy)and EELEACH-MIMO (Energy Efficient LEACH multiple-inputmulti-output) schemes. The selection of the cluster head is done on the basis of residual energy of nodes in a cluster as done in LEACH. The simulation results shows that the proposed algorithm is more energy efficient and works for longer time period. General Terms Sensor Nodes, Clustering, Wireless Sensor Networks.

무선 센서 네트워크에서 효율적인 에너지 사용을 위한 클러스터링 알고리즘

최근 무선 센서 네트워크(WSN)이 침입 탐지와 생태, 환경, 대기, 산업, 교통, 화재 감시 등에 다양하게 적용되어 사용되고 있다. 본 논문에서는 에너지 효율적인 클러스터링 알고리즘을 제안한다. 제안 알고리즘은 수신전력을 바탕으로 하여 최적위치의 클러스터헤드를 선출하고 이를 통해 균일한 클러스터사이즈를 가지도록 한다. 또한 최대 깊이가 제한된 MST를 이용한 라우팅 트리를 구성하고 멀티 홉 전송을 통해 센서 노드의 위치에 관계없이 균일한 에너지 소모를 유도하도록 하였다. 이를 통해 제안 알고리즘은 기존의 LEACH나 HEED에 비해 노드의 에너지 소모를 감소시키고 균등한 에너지 소모를 유도하여 네트워크 수명을 증대시킬 수 있다. 시뮬레이션 결과 제안 알고리즘이 공평한 에너지 소모를 통해 네트워크 수명을 증대시킬 수 있음을 확인하였다. Recently, wireless sensor networks(WSNs) are widely used for intrusion detection and ecology, environment, atmosphere, industry, traffic, fire monitoring. In this paper, an energy efficient clustering algorithm is proposed. The proposed algorithm forms clusters uniformly by selecting cluster head that optimally located based on receiving power. Besides, proposed algorithm can induce uniform energy consumption regardless of location of nodes by multi-hop transmission and MST formation with limited maximum depth. Through the above, proposed algorithm elongates network life time, reduces energy consumption of nodes and induces fair energy consumption compared to conventional LEACH and HEED. The results of simulation show that the proposed clustering algorithm elongates network life time through fair energy consumption.

Density-Based Energy-Efficient Unequal Clustering

In this paper, we propose a Density-based Energy-Efficient Unequal Clustering (DEEUC) algorithm to solve the energy consumption imbalance problem of the nodes deployed randomly. The algorithm balances the energy consumption of the cluster by making nodes as cluster head in turn and taking the remaining energy into consideration, which makes the generation of the cluster head more reasonable. It balances the energy consumption of the cluster by generating unequal cluster with different radius based on nodes density and the distance to the sink. In the routing strategy, the cluster head node with high density is chosen as the next hop, which makes the selected node undertake more data forwarding tasks, and balances the regional energy consumption. The simulation experiments show that this algorithm can effectively balance the network energy consumption and prolong the network lifetime.

Study on the Rough-set-based Clustering Algorithm for Sensor Networks

The traditional clustering algorithm is a very typical level routing algorithm in wireless sensor networks (WSN). On the basis of the classical LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm, this paper proposes an energy efficient clustering algorithm in WSN. Through the introduction of rough set, the new algorithm mainly introduces how to confirm an optimized strategy to choose the cluster head effectively by the simplified decision table. That is to say, by discrete normalized data preprocessing of attribute value, getting discretization decision table. Finally, the results from simulated experiments show that the clustering algorithm based on rough set theory can optimize the clustering algorithm in network data. That is to say, the rough-set-based clustering algorithm can effectively choose the cluster head, balance the energy of the nodes in the cluster and prolong the lifetime of sensor networks.

Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach

Energy efficient clustering and routing are two well known optimization problems which have been studied widely to extend lifetime of wireless sensor networks (WSNs). This paper presents Linear/Nonlinear Programming (LP/NLP) formulations of these problems followed by two proposed algorithms for the same based on particle swarm optimization (PSO). The routing algorithm is developed with an efficient particle encoding scheme and multi-objective fitness function. The clustering algorithm is presented by considering energy conservation of the nodes through load balancing. The proposed algorithms are experimented extensively and the results are compared with the existing algorithms to demonstrate their superiority in terms of network life, energy consumption, dead sensor nodes and delivery of total data packets to the base station.

A Stochastic and Optimized Energy Efficient Clustering Protocol for Wireless Sensor Networks

Wireless sensor network consisting of a large number of small sensors of low-power transceiver is effective for gathering data in a variety of environments. Since the network is built with low-cost sensor nodes of limited battery power, it is a challenging task to design an efficient routing scheme maximizing the network lifetime. In this paper, we propose a new clustering protocol reducing the energy taken for routing, and thus extending the network lifetime. The proposed scheme is based on a newly developed model of optimal number of clusters in the network with which the energy consumption can be minimized. It also includes a method of cluster-head rotation inside each cluster, which avoids the clustering operation during normal operation and evenly distributes the load among the nodes. The performance of the proposed scheme is compared with the clustering-based routing protocols, Low-Energy Adaptive Clustering Hierarchy (LEACH), LEACH-Centralized (LEACH-C), and Adaptive and Energy Efficient Clustering (AEEC) algorithm. Simulation results show that it significantly reduces energy consumption and improves network lifetime. It is thus expected to be effective for large-scale semantic sensor web.

Energy-Aware Distributed Clustering Algorithm for Improving Network Performance in WSNs

Wireless sensor networks (WSNs) consist of a large number of sensor nodes equipped with a diverse number of small and low-cost devices with limited resources, such as a short communication range, a low bandwidth, a small memory, and a restricted energy. In particular, among these constraint factors, a sensor node's energy consumption is a very important factor in extending a network's lifetime. Many researchers are focused on the energy efficiency of wireless sensor networks. Many clustering algorithms have been proposed to improve energy efficiency. However, most protocols in previous literature have the problem of not considering the characteristics of real applications, for example, forest fire detection, intruder detection, target tracking, and the like. In this paper, we propose an energy-efficient clustering algorithm that can respond rapidly to unexpected events with increased energy efficiency, because each sensor node detects events individually and creates clusters using a regional competition scheme. Simulation results show improved performance when our algorithm is used.

Cluster based Event Driven Routing Protocol (CERP) for Wireless Sensor Network

Energy efficiency is the main challenge in wireless sensor network. Many routing protocols have been designed to face this challenge. Hierarchical cluster-based routing is one of the efficient techniques to route the sensed data from source node to sink node. The proposed algorithm deals with cluster formation based on different events, cluster head selection, aggregation of the sensed data within a cluster and sending that to the base station. Simulation result proves that the performance of the proposed scheme is better than Energy Efficient Clustering Algorithm for data aggregation in WSNs (EECA) and A Multipath Routing Protocol Based on Clustering and Ant Colony Optimization (MRP) for Wireless Sensor Networks. General Terms Energy Efficient Routing, Event driven cluster formation, Cluster Head selection