Friendliness Based Trustworthy Relationship Management (F-TRM) in Social Internet of Things

Rajendran, Subash; Jebakumar, R.

doi:10.1007/s11277-021-09256-8

Cited by 9 publications

(9 citation statements)

References 25 publications

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“…To calculate the overall trust of one node as perceived by another node while taking into account the TAs [100], [107], [109], [119], [122], [132], [133].…”

Section: Scenariosmentioning

confidence: 99%

“…Time-Driven [63], [80], [81], [82], [84], [85], [87], [7], [89], [90], [95], [96], [97], [102], [103], [106], [109], [110], [78], [112], [114], [115] [132], [133] [79], [86], [88], [98], [100], [101],…”

Section: Classification Of Studies On the Basis Of Trust Update Schem...unclassified

“…The studies are represented by their reference numbers. Hybrid/ Semi-Centralized [81], [82], [84], [46], [88], [95], [112], [114], [115], [119] [79], [63], [85], [86], [87], [7], [89], [90], [91], [92], [93], [94], [95], [96], [98], [99], [100], [101], [58], [102], [103], [104], [77], [105], [106], [107], [108], [109], [110], [111], [78], [113], [114] [132], [133] [71], [80], [83], [97]…”

Section: ) Trust Values Propagation Schemementioning

confidence: 99%

“…In the research [132], the authors present a Trustworthy Relationship Management (F-TRM) framework based on the principle of friendliness as a means of delivering stable services in the SIoT. For regulating the device's friendliness, a technique called Dynamic Friendship Supervision (DFS) is suggested.…”

Section: B Brief Description Of the Relevant Studiesmentioning

confidence: 99%

“…[79], [63], [80], [81], [83], [84], [46], [85], [86], [87], [7], [88], [89], [90], [91], [92], [94], [95], [96], [97], [98], [99], [100], [58], [102], [103], [104], [77], [107], [108], [109], [110], [111], [78], [112], [113], [71], [114], [115]…”

Section: Number Of Research Studiesunclassified

See 4 more Smart Citations

Trust Management in Social Internet of Things (SIoT): A Survey

et al. 2022

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A survey on trust management in the Social Internet of Things (SIoT) is provided, beginning with a discussion of SIoT architectures and relationships. Using a variety of publication databases, we describe efforts that focus on various trust management aspects of SIoT . Trust management models comprise three themes: trust computation, aggregation, and updates. Our study presents a detailed discussion of all three steps. Trust computation and trust aggregation depend upon Trust Attributes (TAs) for the calculation of local and global trust values. Our paper discusses many strategies for aggregating trust, but "Weighted Sum" is the most frequently used in the relevant studies. Our paper addresses trust computation and aggregation scenarios. Our work classifies research by TAs (Social Trust, Quality of Service). We've categorized the research (reputation-based, recommendation-based, knowledge-based) depending on the types of feedback/opinions used to calculate trust values (global feedback/opinion, feedback from a friend, trustor's own opinion considering the trustee's information). Our work classifies studies (policy-based, prediction-based, weighted sum-based/weighted linear combination-based) by trust computation/aggregation approach. Two trust-update schemes are discussed: time-driven and event-driven schemes, while most trust management models utilize an event-driven scheme. Both trust computation and aggregation need propagating trust values in a centralized, decentralized, or semi-centralized way. Our study covers classifying research by trust updates and propagation techniques. Trust models should provide resiliency to SIoT attacks. This analysis classifies SIoT attacks as collaborative or individual. We also discuss scenarios depicted in the relevant studies to incorporate resistance against trust-related attacks in SIoT. Studies suggest context-based or context-free trust management strategies. Our study categorizes studies based on context-based or contextfree approaches. To gain the benefits of an immutable, privacy-preserving approach, a future trust management system should utilize Blockchain technology to support non-repudiation and tracking of trust relationships.

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“…To calculate the overall trust of one node as perceived by another node while taking into account the TAs [100], [107], [109], [119], [122], [132], [133].…”

Section: Scenariosmentioning

confidence: 99%

Section: Classification Of Studies On the Basis Of Trust Update Schem...unclassified

Section: ) Trust Values Propagation Schemementioning

confidence: 99%

Section: B Brief Description Of the Relevant Studiesmentioning

confidence: 99%

Section: Number Of Research Studiesunclassified

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Trust Management in Social Internet of Things (SIoT): A Survey

et al. 2022

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Understanding the trustworthiness management in the social Internet of Things: A survey

Sagar,

Mahmood,

Sheng

et al. 2024

Computer Networks

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MarketTrust: blockchain-based trust evaluation model for SIoT-based smart marketplaces

Latif

Yakubu

Saba

2023

Sci Rep

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Due to the significance of trust in Social Internet of Things (SIoT)-based smart marketplaces, several research have focused on trust-related challenges. Trust is necessary for a smooth connection, secure systems, and dependable services during trade operations. Recent SIoT-based trust assessment approaches attempt to solve smart marketplace trust evaluation difficulties by using a variety of direct and indirect trust evaluation techniques and other local trust rating procedures. Nevertheless, these methodologies render trust assessment very sensitive to seller dishonesty, and a dishonest seller may influence local trust scores and at the same time pose a significant trust related threats in the system. In this article, a MarketTrust model is introduced, which is a blockchain-based method for assessing trust in an IoT-based smart marketplace. It has three parts: familiarity, personal interactions, and public perception. A conceptual model, assessment technique, and a global trust evaluation system for merging the three components of a trust value are presented and discussed. Several experiments were conducted to assess the model's security, viability, and efficacy. According to results, the MarketTrust model scored a 21.99% higher trust score and a 47.698% lower average latency than both benchmark models. Therefore, this illustrates that using the proposed framework, a potential buyer can efficiently choose a competent and trustworthy resource seller in a smart marketplace and significantly reduce malicious behavior.

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Friendliness Based Trustworthy Relationship Management (F-TRM) in Social Internet of Things

Cited by 9 publications

References 25 publications

Trust Management in Social Internet of Things (SIoT): A Survey

Trust Management in Social Internet of Things (SIoT): A Survey

Understanding the trustworthiness management in the social Internet of Things: A survey

MarketTrust: blockchain-based trust evaluation model for SIoT-based smart marketplaces

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