SGO A New Approach for Energy Efficient Clustering in WSN

SGO A New Approach for Energy Efficient Clustering in WSN

Pritee Parwekar (Anil Neerukonda Institute of Technology & Sciences, Bheemunipatnam, India)
Copyright: © 2018 |Pages: 19
DOI: 10.4018/IJNCR.2018070104

Abstract

In wireless sensor networks (WSNs), consumption of energy is the major challenging issue. If the data is transmitted directly from the node to the base station, it leads to more transmissions and energy consumed also increases if the communication distance is longer. In such cases, to reduce the longer communication distances and to reduce the number of transmissions, a clustering technique is employed. Another way to reduce the energy consumed is to reduce the transmission from node to CH or from CH to BS. Reducing the transmission distance is a NP-Hard problem. So, optimization techniques can be used effectively to solve such problems. In this article, is the implementation of a social group optimization (SGO) to reduce the transmission distance and to allow the nodes to consume less energy. The performance of SGO is compared with GA and PSO and the results show that SGO outperforms in terms of fitness and energy.
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1. Introduction

Wireless sensor networks (WSNs) have become a powerful technological platform with novel applications that is driving many researches to work for applications like military surveillance, environment monitoring, health monitoring, disaster management, etc., as discussed by author Elhabyan et al. (2015). WSN consists of sensor nodes termed as nodes. The basic component of the node is the micro-sensor which helps to sense the desired event. The node also consists of battery, transmitter, receiver, microprocessor etc. in order to communicate with the network. The main function of the sensor is to sense the physical environment, gather the information from the surroundings, perform basic computation on the data and then transmit it to the base station (BS) discussed by Zangeneh et al. (2017). WSN is an event-based system that consists of low-cost, low-power sensor nodes that are used in many applications like military, civil, health monitoring, environment monitoring etc. Applications like civil and military require the location of the nodes to be known for which best localization techniques must be used as analyzed by Alaybeyoglu et al. (2015). The advancement in the sensor technology have led to the rise of the new application of human detection, tracking and activity recognition discussed by Kamal et al. (2016). For distributed applications, time consistency among the sensors is the major feature that should be addressed as explained by Shi et al. (2015).

WSN consists of thousands of sensors which are randomly deployed and are connected wireless in an ad hoc manner. Zangeneh et al. (2017) stated that these sensors communicate with each other and share the information among them based on the infrastructure and topology of the network. WSN must have the capability to make the sensors operate in harsh and unattended environments where the network is inaccessible and unscheduled. In such environments, it is not possible of replacing or recharging the battery of the sensor if the battery dies. Thus, the energy consumption of the sensor becomes a challenging issue. The issues with the wireless senor networks and its various applications are explained by Satapathy et al. (2016). The sensor must consume less energy for the communication especially, for longer distances in order to increase the network lifetime discussed by Akila et al. (2016).

Many approaches have been developed to reduce energy consumption of the sensor. One possible approach for consuming less energy by the nodes is to introduce the concept of sleep mode and active mode of the node. If the devices or nodes are in active state only for some moment of time, the remaining energy can be used later. This concept can also be applicable to the issue of fault tolerance along with energy consumption explained by Al-Kahtani et al. (2015). Of the many approaches, clustering technique is the best approach through which the energy can be consumed efficiently. Clustering is the technique in which the sensors are organized or grouped together to form clusters. Each cluster will be having a cluster head (CH) and it plays the role of a leader. The sensors sense the data and transmit it to the CH and the CH performs aggregation of data and broadcast it to the BS. The reasons that only clusterhead relay the data out of the cluster is to evade the collisions with the sensors present inside the cluster discussed by Elhabyan et al. (2015). The sensors can share information only within the cluster but not between the clusters.

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