Load Balanced Clustering Approach in Wireless Sensor Network using Genetic Algorithm (original) (raw)
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An Optimal Energy-Efficient Clustering Protocol in Wireless Sensor Networks Using Genetic Algorithm
A sensor network has many sensor nodes with limited energy. One of the important issues in these networks is the increase of the life time of the network. This paper proposes a hybrid algorithm which, acts on the network and using genetic algorithm at first stage to choose the best sensors as a cluster head and using sleep/wake up mechanism for redundant sensors in the second stage. This algorithm will balance the energy consumption in the network and improve the network life time and coverage preservation. https://sites.google.com/site/ijcsis/
International Journal of Energy, Information and Communications, 2014
This paper presents a new Genetic Algorithm-based Energy-Efficient adaptive clustering hierarchy Protocol (GAEEP) to efficiently maximize the lifetime and to improve the stable period of Wireless Sensor Networks (WSNs). The new protocol is aimed at prolonging the lifetime of WSNs by finding the optimum number of cluster heads (CHs) and their locations based on minimizing the energy consumption of the sensor nodes using genetic algorithm. The operation of the GAEEP is broken up into rounds, where each round begins with a setup phase, when the base station finds the optimum number of CHs and assigns members nodes of each CH, followed by a steady-state phase, when the sensed data are transferred to CHs and collected in frames; then these frames are transferred to the base station. The performance of the GAEEP is compared with previous protocols using Matlab simulation. Simulation results show that GAEEP protocol improves the network lifetime and stability period over previous protocols in both homogeneous and heterogeneous cases. Moreover, GAEEP protocol increases the reliability of clustering process because it expands the stability period and compresses the instability period.
Procedia Computer Science, 2015
Wireless sensor networks gain ample interest because of their wide range of applications. Efficient energy consumption of nodes is the prime design issue for these networks. Clustering approaches prolong the network lifetime with the load balanced network. To achieve load balancing clustering algorithm rotate the role of cluster head among the nodes so, cluster head selection process is pivotal for clustering algorithms. Work of this paper presents a genetic algorithm based cluster head selection for centralized clustering algorithms to have a better load balanced network than the traditional clustering algorithm. Simulation shows that the proposed solution finds the optimal cluster heads and has prolonged network lifetime than the traditional clustering algorithms.
A novel evolutionary approach for load balanced clustering problem for wireless sensor networks
Swarm and Evolutionary Computation, 2013
Clustering sensor nodes is an effective topology control method to reduce energy consumption of the sensor nodes for maximizing lifetime of Wireless Sensor Networks (WSNs). However, in a cluster based WSN, the leaders (cluster heads) bear some extra load for various activities such as data collection, data aggregation and communication of the aggregated data to the base station. Therefore, balancing the load of the cluster heads is a challenging issue for the long run operation of the WSNs. Load balanced clustering is known to be an NP-hard problem for a WSN with unequal load of the sensor nodes. Genetic Algorithm (GA) is one of the most popular evolutionary approach that can be applied for finding the fast and efficient solution of such problem. In this paper, we propose a novel GA based load balanced clustering algorithm for WSN. The proposed algorithm is shown to perform well for both equal as well as unequal load of the sensor nodes. We perform extensive simulation of the proposed method and compare the results with some evolutionary based approaches and other related clustering algorithms. The results demonstrate that the proposed algorithm performs better than all such algorithms in terms of various performance metrics such as load balancing, execution time, energy consumption, number of active sensor nodes, number of active cluster heads and the rate of convergence.
2014 11th International Symposium on Wireless Communications Systems, 2014
Node clustering in wireless sensor networks helps in extending the network life time by reducing the nodes' communication energy and balancing their remaining energy. This paper presents a new genetic-based approach that improves the performance of the LEACH clustering protocol used in wireless sensor networks. The proposed approach utilizes the mobility feature of sensor nodes in order to reduce the communication distances between the cluster heads and the base station. In each round, new locations of the cluster heads are determined using a genetic algorithm. The simulation results demonstrate that the proposed approach outperforms LEACH in terms of network lifetime and average remaining energy.
Genetic Algorithm Based Energy Efficient Clusters (GABEEC) in Wireless Sensor Networks
Procedia Computer Science, 2012
In this paper, a genetic algorithm based method (GABEEC) is proposed to optimize the lifetime of wireless sensor networks. The proposed method is a cluster based approach like LEACH. Genetic algorithm is used to maximize the lifetime of the network by means of rounds. The method has 2 phases which are Setup and Steady-state phase. In the setup phase, the clusters are created and are not changed throughout the network. The clusters are not recreated for each round. In each round, there are static clusters with dynamically changing clusterheads. A simulator is developed in MS Visual C# 2010 development environment to validate the proposed method. In the simulation, 100 nodes are randomly distributed in 50x50 square meters area. The results show that the proposed method is found to be more efficient than LEACH.
Bulletin of Electrical Engineering and Informatics, 2023
One of the most pressing issues in wireless sensor networks (WSNs) is energy efficiency. Sensor nodes (SNs) are used by WSNs to gather and send data. The techniques of cluster-based hierarchical routing significantly considered for lowering WSN's energy consumption. Because SNs are battery-powered, face significant energy constraints, and face problems in an energy-efficient protocol designing. Clustering algorithms drastically reduce each SNs energy consumption. A low-energy adaptive clustering hierarchy (LEACH) considered promising for application-specifically protocol architecture for WSNs. To extend the network's lifetime, the SNs must save energy as much as feasible. The proposed developed cluster-based loadbalanced protocol (DCLP) considers for the number of ideal cluster heads (CHs) and prevents nodes nearer base stations (BSs) from joining the cluster realization for accomplishing sufficient performances regarding the reduction of sensor consumed energy. The analysis and comparison in MATLAB to LEACH, a well-known cluster-based protocol, and its modified variant distributed energy efficient clustering (DEEC). The simulation results demonstrate that network performance, energy usage, and network longevity have all improved significantly. It also demonstrates that employing cluster-based routing protocols may successfully reduce sensor network energy consumption while increasing the quantity of network data transfer, hence achieving the goal of extending network lifetime.
GENETIC ALGORITHM FOR ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORK
This study proposes a genetic algorithm-based (GA-based) adaptive clustering protocol with an optimal probability prediction to achieve good performance in terms of lifetime of network in wireless sensor networks. The proposed GA-based protocol is based on LEACH, called LEACH-GA herein, which basically has set-up and steady-state phases for each round in the protocol and an additional preparation phase before the beginning of the first round. In the period of preparation phase, all nodes initially perform cluster head selection process and then send their messages with statuses of being a candidate cluster head or not, node IDs, and geographical positions to the base station. As the base station received the messages from all nodes, it then searches for an optimal probability of nodes being cluster heads via a genetic algorithm by minimizing the total energy consumption required for completing one round in the sensor field. Thereafter, the base station broadcasts an advertisement message with the optimal value of probability to the all nodes in order to form clusters in the following set-up phase. The preparation phase is performed only once before the set-up phase of the first round. The processes of following set-up and steady-state phases in every round are the same as LEACH. Simulation results show that the proposed genetic-algorithm-based adaptive clustering protocol effectively produces optimal energy consumption for the wireless sensor network.
wireless sensor networking is an emerging technology that promises a wide range of potential applications in both civilian and military areas. A wireless sensor network (WSN) typically consist of a large number of low cost, low power and multi-functional sensor nodes that are deployed in a region of interest. Wireless sensor networks face many challenges caused by communication failures, storage and computational constraints and limited power supply. In WSN, the nodes are battery driven and hence energy saving of sensor nodes is a major design issue. Energy efficient algorithms must be implemented so that network lifetime should be prolonged. Lifetime of a network can be maximized through clustering algorithms, where cluster is responsible for sending the data to the base station and not all the nodes are involved in data transmission .various clustering algorithms are deployed in past few years. In this paper we are proposing an algorithm which is a combination of Bacterial foraging optimization algorithm (BFO) which is a Bio-Inspired algorithm and LEACH and HEED protocols which enhances the lifetime of a network by dissipating minimum amount of energy.