EEA: Clustering Algorithm for Energy-Efficient Adaptive in Wireless Sensor Networks

EEA: Clustering Algorithm for Energy-Efficient Adaptive in Wireless Sensor Networks

Hassan El Alami, Abdellah Najid
DOI: 10.4018/IJWNBT.2018070102
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Abstract

The data communication task, in wireless sensor networks (WSNs), is a major issue of high energy consumption. A hierarchical design based on a clustering algorithm is one of the approaches to manage the data communication and save energy in WSNs. However, most of the previous approaches based on clustering algorithms have not considered the length of the data communication path, which is a direct relation to energy consumption in WSNs. In this article, a novel scheme of a clustering algorithm has been proposed for reducing the data communication distance in WSNs. Hierarchical routing protocols were implemented for homogeneous and heterogeneous networks. The results show that the proposed scheme is more efficient than other protocols.
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2. Literature Review

In terms of energy, the WSNs are divided into homogeneous and heterogeneous networks. In homogeneous networks, all the sensor nodes are initially powered by same energy levels, whereas, different initial energy is supplied to sensor nodes in the case of heterogeneous networks. The process of CHs selection and clusters formation has been shown in many previous works (Vlajic and Xia, 2006, Karaboga, 2012, and El Alami and Najid, 2017). However, due to space restriction, only some of these works are represented in this chapter.

Low-energy adaptive clustering hierarchy (LEACH) (Heinzelman et al., 2000) is one of the famous hierarchical routing approaches based on distributed algorithm for homogeneous WSNs. In LEACH protocol, the network area is divided into clusters to achieve energy-efficient in WSNs. In each cluster, the selected CH is responsible to gather the data and transmit it to the base station (BS). LEACH protocol uses the probability model to select CHs and alternates the CHs periodically to equalize the energy consumption. A given node generates a random number, between 0 and 1, and compares it with the following threshold:

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