Performance Comparison of Optimization Algorithms for Clustering in Wireless Sensor Networks (original) (raw)
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Evolving a clustering algorithm for wireless sensor network using particle swarm optimisation
International Journal of Swarm Intelligence, 2016
Energy consumption is a vital problem that faces wireless sensor network (WSN) because sensor nodes are always equipped with batteries that cannot be recharged or replaced. Thus, maximising the lifetime of WSN by means of minimising the energy dissipation is an essential aspect in WSN deployment. In this paper, we propose a novel algorithm to cluster the WSN using particle swarm optimisation, named PSO-VC. The proposed algorithm is designed to obtain the optimal number of clusters, optimum cluster heads and optimum clusters layout. The proposed PSO-VC aimed to maximise the number of transmissions which a CH can perform before the node depletes its energy. Our proposed algorithm was evaluated and compared with traditional Low-Energy Adaptive Clustering Hierarchy (LEACH) clustering protocol. Moreover, the same algorithm is re-implemented using genetic algorithm, named GA-VC. It was found that the proposed PSO-VC preserves more energy and considerably prolongs the network lifetime compared to the GA-VC and LEACH algorithms.
Energy-Aware Clustering for Wireless Sensor Networks using Particle Swarm Optimization
2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, 2007
Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the need for energy efficient infrastructure is becoming increasingly more important since it impacts upon the network operational lifetime. Sensor node clustering is one of the techniques that can expand the lifespan of the whole network through data aggregation at the cluster head. In this paper, we present an energy-aware clustering for wireless sensor networks using Particle Swarm Optimization (PSO) algorithm which is implemented at the base station. We define a new cost function, with the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. The performance of our protocol is compared with the well known cluster-based protocol developed for WSNs, LEACH (Low-Energy Adaptive Clustering Hierarchy) and LEACH-C, the later being an improved version of LEACH. Simulation results demonstrate that our proposed protocol can achieve better network lifetime and data delivery at the base station over its comparatives.
Particle Swarm Optimisers for Cluster formation in Wireless Sensor Networks
2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2005
Hierarchical based clustering protocol for wireless sensor network is suitable to use in energy efficient environmental monitoring. In clustering protocol, sensor nodes that are cluster heads (CHs) have to collect information from cluster member and transmit to the base station. Strategic CHs location can significantly affect the network overall energy consumption. Therefore, selecting suitable CHs location becomes a challenging task. In this work, CHs distribution using adaptive particle swarm optimisation (APSO) is proposed. Particle swarm optimization (PSO) is one of the swarm intelligence methods that is designed to search for optimum solution by mimicking the behavior of bird flocking and fish schooling. Introduction of adaptive cognitive and social learning factor can achieve better convergence speed and particles reselection mechanism to reduce the chances of getting trapped at local maximum. The performance of the proposed method is compared with the low energy adaptive cluster hierarchical (LEACH) protocol. Simulation results show that the proposed method outperforms LEACH in terms of first node dies (FND) round, total data received at the base station and energy consumed per round.
An Optimal Clusters in Wireless Sensor Networks by Using Particle Swarm Optimization
EECCMC, 2018
The restricted power of the sensor nodes in wireless sensor networks (WSNs) delegates the design of energy-efficient communication protocol. As a result, many algorithms were proposed to balance the spent on energy in sensor nodes. In order to reduce the energy consumption, minimize the data transmission, and maximize the lifespan of the network, the clustering algorithm is the most efficient technique, the selecting of the cluster head and forming the clusters are the key issues in this technique. In this paper, a proposed scheme OCPSO is suggested by extending the centralized algorithm along with the particle swarm optimization (PSO) with the aim of selecting an optimal cluster head among the eligible cluster heads and forming the appropriate clusters. The performance of our proposed scheme is evaluated through analysis, comparison, and implementation. The results show that the proposed scheme is highly efficient, and it has a better performance in term of energy saving, increasing the number of alive sensor nodes, and prolonging the lifespan of the network.
Engineering Applications of Artificial Intelligence, 2014
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.
Energy-Efficient Link-aware PSO-Based Clustering Algorithm in Wireless Sensor Networks (WSNs
Recently Wireless Sensor Networks (WSNs) has turned into a popular matter of research area, because of its flexibility and dynamic nature. It is proven that the clustering technique, as a multi objective optimization, is the most effective solution to have minimum energy consumption. The goal of the clustering technique is to divide network sensors into clusters each of which has a cluster-head (CH) responsible to collect, aggregate and send sensed data to the base station (BS). The recent researches shown that such multi objective optimization in WSNs can be solved through well adapted an evolutionary algorithm. In this paper an improved k-mean clustering model powered by Particle Swarm Optimization (PSO) algorithm is presented. This is called Link-aware PSO, LSPO. The proposed model utilizes two-phase optimization by applying different fitness function. At the first phase, it selects Primary Cluster-heads based on improved Intra-Cluster Distance metric as fitness function in PSO algorithm to give primary CHs. In the second phase each primary CHs selected are evaluated by link quality and energy metrics to select the best ones as final CHs. Simulation results showed that the proposed algorithm outperforms LEACH and PSO-C algorithms in term of performance, prolonging network lifetime and energy saving.
IAEME, 2019
Wireless Sensor Networks (WSNs) are utilized for a plethora of applications such as weather forecasting, monitoring systems, surveillance, and so on. The critical issues of the WSN are energy constraints, limited memory, and computation time. This spectrum of criticality takes a deep dive with large-scale WSNs. In such scenario, the network lifetime has to be efficiently utilized with the available resources by organizing into clusters. Even though the technique of clustering has proven to be highly effective in minimizing the energy, the tradition cluster based WSNs, the protocol overhead is high for Cluster Heads (CHs) as it receives and aggregates the data from its cluster members. Therefore, efficient management of CH along with routing behavior is vital in prolonging the network lifetime. In this paper, an enhanced CH-Management technique is proposed which efficiently elects its CH using Particle Swarm Optimization (PSO), hereafter referred to as PSO_DDE. The PSO_DDE approach considers various parameters such as within-cluster distance between nodes (intra-cluster distance), neighbor density, and residual energy of nodes for the best candidate selection of CH. Also, the cluster formation is defined by the k-means based on the Euclidian distance. The PSO_DDE approach is integrated with the Dynamic Source Routing (DSR) for efficiently traversing the data packet to the sink node. The performance metrics are compared with the existing approaches using NS-2 simulator, and the proposed approach shows superiority of results.
APPLICATION OF PARTICLE SWARM OPTIMIZATION FOR ENERGY EFFICIENT WIRELESS SENSOR NETWORK: A SURVEY
In this paper we are going to survey the application of particle swarm optimization (PSO) in WSN over different type of clustering based algorithm techniques like LEACH,LEACH-C, PEGASIS, etc In WSN sensors are randomly deployed in the sensor field which brings the coverage problem. Hence energy and coverage problem are very scarce resources for such sensor systems and has to be managed wisely in order to extend the life of the sensors and maximizing coverage for the duration of a particular mission. In past a lot of cluster based algorithm and techniques were used. In this paper we also find out all type of PSO based algorithm, their application and limitation over present techniques to overcome the problems of low energy and coverage of sensor range.
Dutse Journal of Pure and Applied Sciences
In a wide range of applications, such as the military, healthcare, and environmental monitoring, wireless sensor networks (WSNs) have emerged as a key player. Cluster-based WSNs are a viable method for enhancing the life of the sensor network. Choosing the proper cluster head for wireless sensor networks (WSNs) is a key undertaking that affects the network's performance. Current approaches for selecting the cluster head have a number of drawbacks, such as nodes dying too quickly, uneven energy utilization, and shorter network lifespan. Additionally, conventional techniques like fixed Cluster Head and randomized Clustering are ineffective at extending the network lifetime. In the proposed method, Particle swarm optimization was used to create an optimal cluster head selection that addresses the problem of intra-cluster communication and lowers SN energy consumption. The simulation result shows that the performance improvement of the developed algorithm PSO in terms of network lif...
Energy optimization in wireless sensor networks using a hybrid K-means PSO clustering algorithm
Turkish Journal of Electrical Engineering and Computer Sciences, 2015
Energy saving in wireless sensor networks (WSNs) is a critical problem for diversity of applications. Data aggregation between sensor nodes is huge unless a suitable sensor data flow management is adopted. Clustering the sensor nodes is considered an effective solution to this problem. Each cluster should have a controller denoted as a cluster head (CH) and a number of nodes located within its supervision area. Clustering demonstrated an effective result in forming the network into a linked hierarchy. Thus, balancing the load distribution in WSNs to make efficient use of the available energy sources and reducing the traffic transmission can be achieved. In solving this problem we need to find the optimal distribution of sensors and CHs; thus, we can increase the network lifetime while minimizing the energy consumption. In this paper, we propose our initial idea on providing a hybrid clustering algorithm based on K-means clustering and particle swarm optimization (PSO); named KPSO to achieve efficient energy management of WSNs. Our KPSO algorithm is compared with traditional clustering techniques such as the low energy adaptive clustering hierarchy (LEACH) protocol and K-means clustering separately.