Ant star fuzzy routing for industrial wireless sensor network

2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS) Pub Date : 2017-05-01 DOI:10.1109/SSPS.2017.8071637

M. Kannan, Selvakumar Chinnappan, C. Krishnamoorthy

{"title":"Ant star fuzzy routing for industrial wireless sensor network","authors":"M. Kannan, Selvakumar Chinnappan, C. Krishnamoorthy","doi":"10.1109/SSPS.2017.8071637","DOIUrl":null,"url":null,"abstract":"In the process control field, nodes of WSN are deployed to receive the data. Efficient data collection is difficult and it depends on routing, power supply and remote location of node. To overcome the routing problem a new Bio-Inspired mechanism Ant Star Fuzzy (ASF) is proposed. ASF is a reliable and dynamic protocol, capable to provide energy efficient, data collecting routing structure in WSN. Network traffic and fast draining of energy of individual node is avoided. So this will increase the life of the network. ASF optimizes the routing paths even in the case of node drop of failure. It provides effective multi-path data transmission and reliable communication. The main objective is to increase the life span of network, during effective data communication in an efficient manner. This paper defines implementation of WSN and comparison of its performance with AODV, ACOAODV routing protocol based on ASF algorithm is done in terms of packet delivery ratio, throughput and energy level. Performance of ASF algorithm compare with conventional algorithms gives improved results.","PeriodicalId":382353,"journal":{"name":"2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSPS.2017.8071637","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 5

Abstract

In the process control field, nodes of WSN are deployed to receive the data. Efficient data collection is difficult and it depends on routing, power supply and remote location of node. To overcome the routing problem a new Bio-Inspired mechanism Ant Star Fuzzy (ASF) is proposed. ASF is a reliable and dynamic protocol, capable to provide energy efficient, data collecting routing structure in WSN. Network traffic and fast draining of energy of individual node is avoided. So this will increase the life of the network. ASF optimizes the routing paths even in the case of node drop of failure. It provides effective multi-path data transmission and reliable communication. The main objective is to increase the life span of network, during effective data communication in an efficient manner. This paper defines implementation of WSN and comparison of its performance with AODV, ACOAODV routing protocol based on ASF algorithm is done in terms of packet delivery ratio, throughput and energy level. Performance of ASF algorithm compare with conventional algorithms gives improved results.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

工业无线传感器网络的蚁星模糊路由

在过程控制领域，部署无线传感器网络节点接收数据。有效的数据采集是一个困难的问题，并且依赖于路由、供电和节点的远程位置。为了解决路由问题，提出了一种新的仿生蚁星模糊机制(ASF)。ASF协议是一种可靠的动态协议，能够为无线传感器网络提供节能、数据采集的路由结构。避免了单个节点的网络流量和能量的快速消耗。这将增加网络的寿命。ASF在节点丢失故障的情况下也能优化路由路径。它提供了有效的多路径数据传输和可靠的通信。其主要目的是提高网络的寿命，同时有效地进行数据通信。本文定义了WSN的实现，并将其与基于ASF算法的AODV、ACOAODV路由协议在分组传送率、吞吐量和能级方面进行了性能比较。与传统算法相比，ASF算法的性能得到了改进。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS)

自引率

0.00%

发文量

期刊最新文献

Smart industry pollution monitoring and controlling using LabVIEW based IoT Compact circular ring shaped monopole UWB MIMO antenna Performance analysis of supervised machine learning techniques for sentiment analysis Vehicle network security testing Energy efficient routing in mobile Ad-hoc network