A Novel Energy-Efficient Clustering Protocol Using Two-Stage Genetic Algorithm for Improving the Lifetime of Wireless Sensor Networks

2020 ◽  
Vol 19 (03) ◽  
pp. 2050019
Author(s):  
Ali Mahani ◽  
Ebrahim Farahmand ◽  
Saeide Sheikhpour ◽  
Nooshin Taheri-Chatrudi
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Clustering Protocol ◽  
New Energy ◽  
Energy Efficient Clustering

Wireless sensor networks (WSNs) are beginning to be deployed at an accelerated pace, and they have attracted significant attention in a broad spectrum of applications. WSNs encompass a large number of sensor nodes enabling a base station (BS) to sense and transmit data over the area where WSN is spread. As most sensor nodes have a limited energy capacity and at the same time transmit critical information, enhancing the lifetime and the reliability of WSNs are essential factors in designing these networks. Among many approaches, clustering of sensor nodes has proved to be an effective method of reducing energy consumption and increasing lifetime of WSNs. In this paper, a new energy-efficient clustering protocol is implemented using a two-step Genetic Algorithm (GA). In the first step of GA, cluster heads (CHs) are selected, and in the second step, cluster members are chosen based on their distance to the selected CHs. Compared to other clustering protocols, the lifetime of WSNs in the proposed clustering is improved. This improvement is the consequence of the fact that this clustering considers energy efficient parameters in clustering protocol.

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.

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.

An Energy-Balanced Cluster-Based Protocol for Wireless Sensor Networks

2013 ◽  
Vol 8 (3) ◽  
pp. 50-63 ◽  
Author(s):  
Eyad Taqieddin ◽  
Moad Mowafi ◽  
Fahed Awad ◽  
Omar Banimelhem ◽  
Hani Maher
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Clustering Protocol ◽  
Virtual Forces ◽  
Virtual Field ◽  
Energy Efficient Clustering

This paper proposes a novel energy-efficient clustering protocol for wireless sensor networks. It combines the benefits of using the k-means clustering algorithm with the, recently developed, LEACH with virtual forces (LEACH-VF) protocol. In this work, the k-means algorithm is employed to determine k centroids around which the clusters will be formed. After that, the virtual field force method is applied to these clusters to determine the most suitable positions for each node. The main target of such an approach is to improve the energy balance in the network and to extend the network lifetime. Simulation results show that the proposed protocol extends the time before the first node death, minimizes the variance of the average node energy, and reduces the distance that the sensor nodes travel within their respective clusters.

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