Dynamic localization based-utility decision approach under type-2 Pythagorean fuzzy set for developing internet of modular self-reconfiguration robot things

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Engineering Applications of Artificial Intelligence Pub Date : 2024-11-21 DOI:10.1016/j.engappai.2024.109671
Nahia Mourad , A.A. Zaidan , Hassan A. Alsattar , Sarah Qahtan , B.B. Zaidan , Muhammet Deveci , Dragan Pamucar , Witold Pedrycz
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Abstract

The Internet of Modular Robot Things (IoMRT) has emerged through the integration of robotic systems into the Internet of Things (IoT), offering a wide range of solutions to meet continuously growing demands. Six self-reconfiguration functionalities/criteria have been proposed for developing IoMRT. However, no study has fully developed an IoMRT that satisfies all the necessary functionalities. Additionally, there is a lack of scholarly research proposing a decision-based approach for evaluating and ranking IoMRT, which highlights a significant research gap. A complex multiple-criteria decision-making (MCDM) problem has arisen in evaluating and ranking IoMRT due to the diversity of functionalities, the need to prioritize these functionalities based on their importance, and data variability. To address this issue, the study proposes a novel decision-based approach for evaluating and ranking IoMRT, which consists of three phases: (i) Developing a novel weighting method called T2PFS-FWZICbIP (Type-2 Pythagorean Fuzzy Set–Fuzzy Weighted Zero Inconsistency based on Interrelationship Process) to measure the importance of the identified functionalities; (ii) Formulating a decision matrix by cross-referencing potential IoMRT developments with the six self-reconfiguration functionalities resulted in the selection of a random sample of 50 IoMRTs as proof of concept. Following this, the DLbU (Dynamic Localization-based Utility) method was proposed, integrating dynamic localization and utility procedures to manage binary data within the decision matrix; (iii) Developing a novel ranking method, T2PFS-DNMA (Type-2 Pythagorean Fuzzy Set–Double Normalization-based Multiple Aggregation), to address the diversity of functionalities and concerns regarding data variance. The results revealed that the Distributed functionality (C1) received the highest weight value of 0.4060 according to T2PFS-FWZICbIP, indicating its high importance in the ranking of IoMRT. In contrast, the High-Fidelity functionality (C5) received a weight value of 0.0733, indicating its very low importance in the ranking. IoMRT2 and IoMRT35 were identified as the most and least favored, respectively, according to T2PFS-DNMA. The robustness of the proposed approach was assessed through sensitivity analysis and comparative studies.
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基于 2 型毕达哥拉斯模糊集的动态定位-效用决策方法,用于开发模块化自重构机器人物联网
模块化机器人物联网(IoMRT)是通过将机器人系统集成到物联网(IoT)中而出现的,它提供了广泛的解决方案,以满足不断增长的需求。为开发 IoMRT,已经提出了六种自我重新配置功能/标准。然而,还没有一项研究能完全开发出满足所有必要功能的物联网远程监控技术。此外,缺乏学术研究提出一种基于决策的方法来评估物联网实时路况跟踪系统并对其进行排序,这凸显了一个重大的研究空白。由于功能的多样性、根据重要性对这些功能进行优先排序的必要性以及数据的多变性,在对物联网实时通信技术进行评估和排序时出现了一个复杂的多重标准决策(MCDM)问题。为解决这一问题,本研究提出了一种新颖的基于决策的方法,用于对物联网实时交通进行评估和排序,该方法包括三个阶段:(i) 开发一种名为 T2PFS-FWZICbIP(基于相互关系过程的第 2 类毕达哥拉斯模糊集-模糊加权零不一致性)的新型加权方法,以衡量已识别功能的重要性;(ii) 通过将潜在的物联网实时路由器开发与六种自重新配置功能相互参照,形成一个决策矩阵,最终随机选择 50 个物联网实时路由器样本作为概念验证。随后,提出了 DLbU(基于动态定位的效用)方法,整合了动态定位和效用程序,以管理决策矩阵中的二进制数据;(iii) 开发了一种新的排序方法 T2PFS-DNMA(Type-2 Pythagorean Fuzzy Set-Double Normalization-based Multiple Aggregation),以解决功能的多样性和数据差异问题。结果显示,根据 T2PFS-FWZICbIP 方法,分布式功能(C1)的权重值最高,为 0.4060,表明其在 IoMRT 排序中的重要性很高。相比之下,高保真功能(C5)的权重值为 0.0733,表明其在排序中的重要性非常低。根据 T2PFS-DNMA 方法,IoMRT2 和 IoMRT35 分别被认为是最受欢迎和最不受欢迎的。通过敏感性分析和比较研究评估了所建议方法的稳健性。
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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