Performance evaluation and comparative analysis of CrowWhale-energy and trust aware multicast routing algorithm

IF 0.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Web Intelligence Pub Date : 2023-01-23 DOI:10.3233/web-220063

Dipali K. Shende, Y. Angal

{"title":"Performance evaluation and comparative analysis of CrowWhale-energy and trust aware multicast routing algorithm","authors":"Dipali K. Shende, Y. Angal","doi":"10.3233/web-220063","DOIUrl":null,"url":null,"abstract":"Multipath routing helps to establish various quality of service parameters, which is significant in helping multimedia broadcasting in the Internet of Things (IoT). Traditional multicast routing in IoT mainly concentrates on ad hoc sensor networking environments, which are not approachable and vigorous enough for assisting multimedia applications in an IoT environment. For resolving the challenging issues of multicast routing in IoT, CrowWhale-energy and trust-aware multicast routing (CrowWhale-ETR) have been devised. In this research, the routing performance of CrowWhale-ETR is analyzed by comparing it with optimization-based routing, routing protocols, and objective functions. Here, the optimization-based algorithm, namely the Spider Monkey Optimization algorithm (SMO), Whale Optimization Algorithm (WOA), Dolphin Echolocation Optimization (DEO) algorithm, Water Wave Optimization (WWO) algorithm, Crow Search Algorithm (CSA), and, routing protocols, like Ad hoc On-Demand Distance Vector (AODV), CTrust-RPL, Energy-Harvesting-Aware Routing Algorithm (EHARA), light-weight trust-based Quality of Service (QoS) routing, and Energy-awareness Load Balancing-Faster Local Repair (ELB-FLR) and the objective functions, such as energy, distance, delay, trust, link lifetime (LLT) and EDDTL (all objectives) are utilized for comparing the performance of CrowWhale-ETR. In addition, the performance of CrowWhale-ETR is analyzed in terms of delay, detection rate, energy, Packet Delivery Ratio (PDR), and throughput, and it achieved better values of 0.539 s, 0.628, 78.42%, 0.871, and 0.759 using EDDTL as fitness.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.2000,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Web Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/web-220063","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

Abstract

Multipath routing helps to establish various quality of service parameters, which is significant in helping multimedia broadcasting in the Internet of Things (IoT). Traditional multicast routing in IoT mainly concentrates on ad hoc sensor networking environments, which are not approachable and vigorous enough for assisting multimedia applications in an IoT environment. For resolving the challenging issues of multicast routing in IoT, CrowWhale-energy and trust-aware multicast routing (CrowWhale-ETR) have been devised. In this research, the routing performance of CrowWhale-ETR is analyzed by comparing it with optimization-based routing, routing protocols, and objective functions. Here, the optimization-based algorithm, namely the Spider Monkey Optimization algorithm (SMO), Whale Optimization Algorithm (WOA), Dolphin Echolocation Optimization (DEO) algorithm, Water Wave Optimization (WWO) algorithm, Crow Search Algorithm (CSA), and, routing protocols, like Ad hoc On-Demand Distance Vector (AODV), CTrust-RPL, Energy-Harvesting-Aware Routing Algorithm (EHARA), light-weight trust-based Quality of Service (QoS) routing, and Energy-awareness Load Balancing-Faster Local Repair (ELB-FLR) and the objective functions, such as energy, distance, delay, trust, link lifetime (LLT) and EDDTL (all objectives) are utilized for comparing the performance of CrowWhale-ETR. In addition, the performance of CrowWhale-ETR is analyzed in terms of delay, detection rate, energy, Packet Delivery Ratio (PDR), and throughput, and it achieved better values of 0.539 s, 0.628, 78.42%, 0.871, and 0.759 using EDDTL as fitness.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

CrowWhale-energy - trust - aware组播路由算法性能评价与比较分析

多路径路由有助于建立各种服务质量参数，这对于帮助物联网(IoT)中的多媒体广播具有重要意义。物联网中传统的组播路由主要集中在ad hoc传感器网络环境中，对于辅助物联网环境中的多媒体应用来说，其易用性和生命力不够强。为了解决物联网中具有挑战性的组播路由问题，设计了CrowWhale-energy和trust-aware组播路由(CrowWhale-ETR)。本研究通过与基于优化的路由、路由协议和目标函数的比较，分析了CrowWhale-ETR的路由性能。在这里，基于优化的算法，即蜘蛛猴优化算法(SMO)，鲸鱼优化算法(WOA)，海豚回声定位优化算法(DEO)，水波优化算法(wo)，乌鸦搜索算法(CSA)，以及路由协议，如Ad hoc按需距离矢量(AODV)， CTrust-RPL，能量收集感知路由算法(EHARA)，轻量级基于信任的服务质量(QoS)路由，利用能量感知负载均衡-快速局部修复(ELB-FLR)和能量、距离、延迟、信任、链路寿命(LLT)和EDDTL(所有目标)等目标函数对CrowWhale-ETR的性能进行了比较。此外，从延迟、检测率、能量、包投递率(Packet Delivery Ratio, PDR)和吞吐量等方面分析了CrowWhale-ETR算法的性能，以EDDTL作为适应度，CrowWhale-ETR算法的适应度分别为0.539 s、0.628 s、78.42% s、0.871 s和0.759 s。

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

Web Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

0.90

自引率

0.00%

发文量

期刊介绍： Web Intelligence (WI) is an official journal of the Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web intelligence. WI seeks to collaborate with major societies and international conferences in the field. WI is a peer-reviewed journal, which publishes four issues a year, in both online and print form. WI aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with Collective Intelligence, Data Science, Human-Centric Computing, Knowledge Management, and Network Science. It is committed to publishing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of technologies based on Web intelligence. The journal features high-quality, original research papers (including state-of-the-art reviews), brief papers, and letters in all theoretical and technology areas that make up the field of WI. The papers should clearly focus on some of the following areas of interest: a. Collective Intelligence[...] b. Data Science[...] c. Human-Centric Computing[...] d. Knowledge Management[...] e. Network Science[...]