An energy-efficient and novel populated cluster aware routing protocol (PCRP) for wireless sensor networks (WSN)

Author:

Martinaa M.1,Santhi B.1,Raghunathan A.2

Affiliation:

1. SASTRA Deemed to be University, Tamilnadu, India

2. Retired AGM, BHEL, Trichy, Tamilnadu, India

Abstract

Wireless Sensor Networks (WSNs) is created, stemming from their applications in distinct areas. Huge sensor nodes are deployed in geographically isolated regions in WSN. As a result of uninterrupted transmission, the energy level of the nodes gets rapidly depleted. Sensor node batteries cannot be replaced or recharged often and maintaining the energy level is a crucial issue. Thus energy efficiency is the significant factor to be consider in WSN. This paper focuses to implement an efficient clustering and routing protocols for maximized network lifetime. Clustering has been confirmed as a successful approach in network organization. The fundamental responsibilities of the clustering mechanism include improved energy efficiency and extended network lifespan. In this work, energy efficiency is improved to maximize lifespan of the WSN by proposing a novel method known as the Populated Cluster aware Routing Protocol (PCRP). The proposed method comprises three different steps: cluster formation, cluster head selection, and multi-hop data transmission. All sensor nodes are joined to a Cluster Head in a single hop in the cluster formation phase. Node distance is calculated and from which cluster head is selected. Then, cluster head aggregates the data from sensor nodes and transfer to the Base Station (BS). The shortest pathway is estimated by the Energy Route Request Adhoc On-demand Distance Vector (ERRAODV) algorithm. The proposed method considers the residual energy involved to attain high energy efficiency and network stability. The experimental analysis is demonstrated to validate the proposed method with existing, which improves the network lifespan. Vital parameters are validated using Network Simulator (NS2).

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3