A Fuzzy-based System for assessment of relational trust in IoT and social networks

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things Pub Date : 2025-02-05 DOI:10.1016/j.iot.2025.101528

Shunya Higashi , Phudit Ampririt , Ermioni Qafzezi , Makoto Ikeda , Keita Matsuo , Leonard Barolli

{"title":"A Fuzzy-based System for assessment of relational trust in IoT and social networks","authors":"Shunya Higashi , Phudit Ampririt , Ermioni Qafzezi , Makoto Ikeda , Keita Matsuo , Leonard Barolli","doi":"10.1016/j.iot.2025.101528","DOIUrl":null,"url":null,"abstract":"<div><div>In modern digital ecosystems, trust plays a critical role in ensuring secure and reliable interactions across various entities, including humans and machines. As technologies such as 5G wireless networks and the Internet of Things (IoT) drive unprecedented complexity in these environments, the demand for flexible and effective trust evaluation systems has grown exponentially. This paper presents a Fuzzy-based System for Assessment of Relational Trust (FSART), which utilizes fuzzy logic to evaluate trust levels between entities. We implemented two models (FSARTM1 and FSARTM2) considering three parameters: Influence (If), Importance (Ip), and Similarity (Sm) for FSARTM1, while for FSARTM2 we considered Reputation (Rp) as an additional parameter. The simulation results indicate that increasing the values of these parameters leads to a corresponding increase in Relational Trust (RT). For FSARTM1, when Ip is 0.9, all RT values exceed 0.5, while in FSARTM2 when If is 0.9 and moderate values of other parameters, RT consistently remains above 0.5. These results suggest that FSARTM2 provides a more accurate assessment by incorporating Rp, making it a more robust tool for trust evaluation in complex digital ecosystems, including social media, collaborative environments, and IoT systems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101528"},"PeriodicalIF":6.0000,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2542660525000411","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

In modern digital ecosystems, trust plays a critical role in ensuring secure and reliable interactions across various entities, including humans and machines. As technologies such as 5G wireless networks and the Internet of Things (IoT) drive unprecedented complexity in these environments, the demand for flexible and effective trust evaluation systems has grown exponentially. This paper presents a Fuzzy-based System for Assessment of Relational Trust (FSART), which utilizes fuzzy logic to evaluate trust levels between entities. We implemented two models (FSARTM1 and FSARTM2) considering three parameters: Influence (If), Importance (Ip), and Similarity (Sm) for FSARTM1, while for FSARTM2 we considered Reputation (Rp) as an additional parameter. The simulation results indicate that increasing the values of these parameters leads to a corresponding increase in Relational Trust (RT). For FSARTM1, when Ip is 0.9, all RT values exceed 0.5, while in FSARTM2 when If is 0.9 and moderate values of other parameters, RT consistently remains above 0.5. These results suggest that FSARTM2 provides a more accurate assessment by incorporating Rp, making it a more robust tool for trust evaluation in complex digital ecosystems, including social media, collaborative environments, and IoT systems.

查看原文本刊更多论文

基于模糊的物联网和社交网络关系信任评估系统

在现代数字生态系统中，信任在确保各种实体（包括人和机器）之间安全可靠的交互方面发挥着关键作用。随着5G无线网络和物联网（IoT）等技术在这些环境中带来前所未有的复杂性，对灵活有效的信任评估系统的需求呈指数级增长。本文提出了一种基于模糊的关系信任评估系统（FSART），该系统利用模糊逻辑来评估实体之间的信任水平。我们实现了两个模型（FSARTM1和FSARTM2），考虑了三个参数：FSARTM1的影响力（If）、重要性（Ip）和相似性（Sm），而对于FSARTM2，我们将声誉（Rp）作为附加参数。仿真结果表明，增加这些参数的值会导致相应的关系信任（RT）增加。对于FSARTM2，当Ip为0.9时，所有RT值均超过0.5，而在FSARTM2中，当If为0.9及其他参数值适中时，RT始终保持在0.5以上。这些结果表明，FSARTM2通过纳入Rp提供了更准确的评估，使其成为复杂数字生态系统（包括社交媒体、协作环境和物联网系统）中更强大的信任评估工具。

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分钟内获得全文求助全文

来源期刊

Internet of Things Multiple-

CiteScore

3.60

自引率

5.10%

发文量

115

审稿时长

37 days

期刊介绍： Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.