scholarly journals Energy-Efficient Clustering and Localization Technique Using Genetic Algorithm in Wireless Sensor Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Junfeng Chen ◽  
Samson Hansen Sackey ◽  
Joseph Henry Anajemba ◽  
Xuewu Zhang ◽  
Yurun He
Keyword(s):  
Genetic Algorithm ◽  
Energy Efficient ◽  
Fitness Function ◽  
Distance Estimation ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Energy Distance ◽  
Node Location ◽  
Unknown Node ◽  
Energy Efficient Clustering

Localization is recognized among the topmost vital features in numerous wireless sensor network (WSN) applications. This paper puts forward energy-efficient clustering and localization centered on genetic algorithm (ECGAL), in which the residual energy, distance estimation, and coverage connection are developed to form the fitness function. This function is certainly fast to run. The proposed ECGAL exhausts a lesser amount of energy and extends wireless network existence. Finally, the simulations are carried out to assess the performance of the proposed algorithm. Experimental results show that the proposed algorithm approximates the unknown node location and provides minimum localization error.

scholarly journals GAECH: Genetic Algorithm Based Energy Efficient Clustering Hierarchy in Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
B. Baranidharan ◽  
B. Santhi
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Fitness Function ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
The Stability ◽  
Energy Efficient Clustering

Clustering the Wireless Sensor Networks (WSNs) is the major issue which determines the lifetime of the network. The parameters chosen for clustering should be appropriate to form the clusters according to the need of the applications. Some of the well-known clustering techniques in WSN are designed only to reduce overall energy consumption in the network and increase the network lifetime. These algorithms achieve increased lifetime, but at the cost of overloading individual sensor nodes. Load balancing among the nodes in the network is also equally important in achieving increased lifetime. First Node Die (FND), Half Node Die (HND), and Last Node Die (LND) are the different metrics for analysing lifetime of the network. In this paper, a new clustering algorithm, Genetic Algorithm based Energy efficient Clustering Hierarchy (GAECH) algorithm, is proposed to increase FND, HND, and LND with a novel fitness function. The fitness function in GAECH forms well-balanced clusters considering the core parameters of a cluster, which again increases both the stability period and lifetime of the network. The experimental results also clearly indicate better performance of GAECH over other algorithms in all the necessary aspects.

Quasi Oppositional Glowworm Swarm Optimization Algorithm for Energy Efficient Clustering in Wireless Sensor Networks

2020 ◽  
Vol 17 (12) ◽  
pp. 5447-5456
Author(s):  
R. M. Alamelu ◽  
K. Prabu
Keyword(s):  
Energy Efficient ◽  
Cluster Head ◽  
Residual Energy ◽  
Link Quality ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Node Degree ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Energy Efficient Clustering

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.

ECRPW: Energy-Efficient Clustering Routing Protocol Based on Weight for WSNs

2012 ◽  
Vol 182-183 ◽  
pp. 823-828
Author(s):  
Xiang Ping Gu ◽  
Rong Lin Hu
Keyword(s):  
Routing Protocol ◽  
Energy Efficient ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Distance Threshold ◽  
Cluster Heads ◽  
Cluster Scheme ◽  
Energy Efficient Clustering ◽  
Routing Tree ◽  
Better Than

ECRPW (energy-efficient clustering routing protocol based on weight) routing protocol is presented to avoid the characteristic of limited energy for wireless sensor networks. It takes nodes’ residual energy into consideration during the process of cluster heads being elected. The constraint of distance threshold is used to optimize cluster scheme. Furthermore, it also sets up the routing tree based on cluster heads’ weight. We simulate and analyze LEACH and ECRPW in NS2. The results show that the performance of ECRPW is better than LEACH.

scholarly journals Cooperative Game Theory Approach for Energy-Efficient Node Clustering in Wireless Sensor Network

2020 ◽  
Vol 20 (2) ◽  
pp. 76
Author(s):  
Chaeriah Bin Ali Wael ◽  
Nasrullah Armi ◽  
Arumjeni Mitayani ◽  
Suyoto Suyoto ◽  
Salita Ulitia Prini ◽  
...  
Keyword(s):  
Game Theory ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Cooperative Game ◽  
Energy Efficient ◽  
Cooperative Game Theory ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Theory Approach ◽  
Energy Efficient Clustering

Energy consumption is one of the critical challenges in designing wireless sensor network (WSN) since it is typically composed of resource-constrained devices. Many studies have been proposed clustering to deal with energy conservation in WSN. Due to its predominance in coordinating the behaviors of many players, game theory has been considered for improving energy efficiency in WSN. In this paper, we evaluate the performance of cooperative game theoretic clustering (CGC) algorithm which employs cooperative game theory in a form of 3-agent cost sharing game for energy-efficient clustering in WSN. Furthermore, we compared its performance to a well-known traditional clustering method, low-energy adaptive clustering hierarchy (LEACH), in terms of network lifetime and stability, and total residual energy. The simulation results show that CGC has better performance compared to LEACH due to the cooperation among cluster heads in coalition. CGC has higher alive nodes with stability improvement of first node dies (FND) by 65%, and the improvement by 52.4% for half node dies (HND). However, with the increasing of the number of nodes, the performance of LEACH is getting better compared to CGC.

scholarly journals Energy-efficient genetic algorithm variants of PEGASIS for 3D Wireless Sensor Networks

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aaqil Somauroo ◽  
Vandana Bassoo
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Residual Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Routing ◽  
Delay Performance

Due to its boundless potential applications, Wireless Sensor Networks have been subject to much research in the last two decades. WSNs are often deployed in remote environments making replacement of batteries not feasible. Low energy consumption being of prime requisite led to the development of energy-efficient routing protocols. The proposed routing algorithms seek to prolong the lifetime of sensor nodes in the relatively unexplored area of 3D WSNs. The schemes use chain-based routing technique PEGASIS as basis and employ genetic algorithm to build the chain instead of the greedy algorithm. Proposed schemes will incorporate an energy and distance aware CH selection technique to improve load balancing. Clustering of the network is also implemented to reduce number of nodes in a chain and hence reduce delay. Simulation of our proposed protocols is carried out for homogeneous networks considering separately cases for a static base-station inside and outside the network. Results indicate considerable improvement in lifetime over PEGASIS of 817% and 420% for base station inside and outside the network respectively. Residual energy and delay performance are also considered.

Sign in / Sign up

Export Citation Format

Share Document