Energy Efficient Clustering-Based Mobile Routing Algorithm on WSNs

Muhammed Ali Aydin,Baybars Karabekir,Abdul Halim Zaim

doi:10.1109/access.2021.3090979

Muhammed Ali Aydin, Baybars Karabekir + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3090979

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Abstract

In this paper, we propose and discuss two types of algorithms to improve energy efficiency in Wireless Sensor Networks. An efficient approach for extending the life of a network is known as “sensor clustering” in wireless sensor networks. In proposed algorithms, the study area where sensor nodes are randomly distributed is divided into clusters. In each cluster, the sensor that is the closest to the cluster center and has the highest residual energy is chosen as the cluster head. To make this choice, a greedy approach and artificial neural network methods are applied. In addition, to reduce the energy consumption of cluster heads, a mobile sink is used. The list of routes to be used by the mobile sink is calculated with the genetic algorithm. According to the route information, the mobile sink moves to the clusters and initiates the data collection process for each cluster. We compared our models according to the round value at which all sensor nodes run out of energy and the energy consumption by the network per round. Simulation results show that the proposed models increase the energy efficiency and extend the network lifespan.

Highlights

Today, the development of sensor technologies allows the measured data to be transferred to the target area via wireless communications
We separate clusters from each other according to an optimal Bluetooth Low Energy (BLE) connection distance in constant intervals, and we model a scalable Wireless Sensor Network (WSN)
As a result of the clusters formed at 30 m2 intervals, the selection of cluster head (CH) depending on the residual energy and distance from cluster center and using mobile sink, simulation results clearly show that the energy consumed by the network is reduced and extending the network lifespan much more than R-low-energy adaptive clustering hierarchy (LEACH) with using Greedy&genetic algorithm (GA) and ANN&GA models

Summary

INTRODUCTION

The development of sensor technologies allows the measured data to be transferred to the target area via wireless communications. To collect the data for a long time and transmit data within the network [7], it is very important to solve the energy efficiency problem in WSNs [8]. The clustering strategy for sensors is a method used to improve the energy efficiency of WSNs by balancing the consumption of energy [9]. In this configuration, the sensors are split into clusters according to the parameters defined [10], and one of the nodes in the cluster is selected as the cluster head (CH).

RELATED WORKS

MODELS AND CLUSTER HEAD

PROPOSED MODEL

CLUSTERING WITH ARTIFICIAL NEURAL

RESULT

CONCLUSION AND FUTURE WORKS