Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jasleen Kaur ◽  
Punam Rani ◽  
Brahm Prakash Dahiya

Purpose This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency. Design/methodology/approach The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB. Findings The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly Optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay. Originality/value This research work is original.

2020 ◽  
Vol 17 (12) ◽  
pp. 5447-5456
Author(s):  
R. M. Alamelu ◽  
K. Prabu

Wireless sensor network (WSN) becomes popular due to its applicability in distinct application areas like healthcare, military, search and rescue operations, etc. In WSN, the sensor nodes undergo deployment in massive number which operates autonomously in harsh environment. Because of limited resources and battery operated sensor nodes, energy efficiency is considered as a main design issue. To achieve, clustering is one of the effective technique which organizes the set of nodes into clusters and cluster head (CH) selection takes place. This paper presents a new Quasi Oppositional Glowworm Swarm Optimization (QOGSO) algorithm for energy efficient clustering in WSN. The proposed QOGSO algorithm is intended to elect the CHs among the sensor nodes using a set of parameters namely residual energy, communication cost, link quality, node degree and node marginality. The QOGSO algorithm incorporates quasi oppositional based learning (QOBL) concept to improvise the convergence rate of GSO technique. The QOGSO algorithm effectively selects the CHs and organizes clusters for minimized energy dissipation and maximum network lifetime. The performance of the QOGSO algorithm has been evaluated and the results are assessed interms of distinct evaluation parameters.


Author(s):  
Zahra Eskandari ◽  
Seyed Amin Hosseini Seno ◽  
Muhamed Shenify ◽  
Rahmat Budiarto

Wireless sensor network (WSN) consists of sensor nodes which are deployed in the environment densely and randomly. The main constraint of these nodes is limited energy resources, so, the operations which are performed in the network, must be energy efficient. For this reason, routing and data transmission in these networks perform hierarchically and in multi hop manner. One of these hierarchical architectures which have a considerable positive effect on energy consumption is clustering algorithm. But what is important is that the cluster heads election should be done efficiently. Recently some works have focused on optimal cluster head election using Integer Linear Programming techniques. In this paper, Integer Linear Programming techniques are used to formulate the clustering problem.  At first, by using Integer Linear Programming techniques, a scalable and multi objective model for optimal cluster head selection is presented and then the distributed clustering algorithm is proposed. As shown in simulation results, the proposed clustering algorithm is more efficient in terms of energy vs. LEACH algorithm.


Nowadays, Wireless Sensor Network is the promising and booming technology used in a variety of applications like disaster monitoring, health care, environmental monitoring, agriculture, industrial automation, etc. However the main drawback of the wireless sensor network is the limited energy source of the sensor nodes. Consequently, efficient utilization of the energy becomes essential for increasing the lifetime of network. Clustering protocol is one of the best energy efficient approach for saving the energy and maximizing the network lifetime. But the improper selection of cluster heads (CHs) may lead to the death of the CHs which deteriorate the performance of the network. Therefore the proper selection of cluster head becomes important for the energy conservation of sensor nodes and to maximize the lifetime of network. In this paper, we have presented PSO based optimal cluster head selection algorithm, in which the best possible CHs are chosen on the basis of parameters like residual energy, intra-cluster distance, and inter-cluster distance of the sensor node. With the effective scheme of particle encoding and fitness function, the proposed PSO algorithm is implemented for reducing the energy consumption and improving lifetime of network. The proposed algorithm also ensures the uniform distribution of the energy over network, by changing the role of CHs after each round. We extend our research to cluster formation approach where the sensor nodes are joined to the CH on the basis distance and energy of cluster head. The proposed algorithm is simulated extensively under various conditions like number of sensor nodes in the field, number of CHs, the position of the base station, constant energy and random energy, etc. and the simulation results are analyzed with the extant algorithms. Under all the circumstances the proposed algorithm outperforms the existing LEACH and SEP protocols in terms of average residual energy, the network lifetime and number of data packets received by the base station. Because of the improvement in the lifetime of the network, the proposed algorithm can be used in the applications like environmental monitoring, agriculture etc.


Sensor Review ◽  
2018 ◽  
Vol 38 (4) ◽  
pp. 534-541
Author(s):  
Sangeetha M. ◽  
Sabari A.

Purpose This paper aims to provide prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). Forming clusters of mobile nodes is a great task owing to their dynamic nature. Such clustering has to be performed with a higher consumption of energy. Perhaps sensor nodes might be supplied with batteries that cannot be recharged or replaced while in the field of operation. One optimistic approach to handle the issue of energy consumption is an efficient way of cluster organization using the particle swarm optimization (PSO) technique. Design/methodology/approach In this paper two improved versions of centralized PSO, namely, unequal clustering PSO (UC-PSO) and hybrid K-means clustering PSO (KC-PSO), are proposed, with a focus of achieving various aspects of clustering parameters such as energy consumption, network lifetime and packet delivery ratio to achieve energy-efficient and reliable communication in MWSNs. Findings Theoretical analysis and simulation results show that improved PSO algorithms provide a balanced energy consumption among the cluster heads and increase the network lifetime effectively. Research limitations/implications In this work, each sensor node transmits and receives packets at same energy level only. In this work, focus was on centralized clustering only. Practical implications To validate the proposed swarm optimization algorithm, a simulation-based performance analysis has been carried out using NS-2. In each scenario, a given number of sensors are randomly deployed and performed in a monitored area. In this work, simulations were carried out in a 100 × 100 m2 network consisting 200 nodes by using a network simulator under various parameters. The coordinate of base station is assumed to be 50 × 175. The energy consumption due to communication is calculated using the first-order radio model. It is considered that all nodes have batteries with initial energy of 2 J, and the sensing range is fixed at 20 m. The transmission range of each node is up to 25 m and node mobility is set to 10 m/s. Practical implications This proposed work utilizes the swarm behaviors and targets the improvement of mobile nodes’ lifetime and energy consumption. Originality/value PSO algorithms have been implemented for dynamic sensor nodes, which optimize the clustering and CH selection in MWSNs. A new fitness function is evaluated to improve the network lifetime, energy consumption, cluster formation, packet transmissions and cluster head selection.


Author(s):  
Vrajesh Kumar Chawra ◽  
Govind P. Gupta

The formation of the unequal clusters of the sensor nodes is a burning research issue in wireless sensor networks (WSN). Energy-hole and non-uniform load assignment are two major issues in most of the existing node clustering schemes. This affects the network lifetime of WSN. Salp optimization-based algorithm is used to solve these problems. The proposed algorithm is used for cluster head selection. The performance of the proposed scheme is compared with the two-node clustering scheme in the term of residual energy, energy consumption, and network lifetime. The results show the proposed scheme outperforms the existing protocols in term of network lifetime under different network configurations.


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