survey

On Trust Recommendations in the Social Internet of Things – A Survey

Authors:

Marius Becherer,

Omar Khadeer Hussain,

Frank den Hartog,

Elizabeth ChangAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 6

Article No.: 160, Pages 1 - 35

https://doi.org/10.1145/3645100

Published: 13 March 2024 Publication History

Abstract

The novel paradigm Social Internet of Things (SIoT) improves the network navigability, identifies suitable service providers, and addresses scalability concerns. Ensuring trustworthy collaborations among devices is a key aspect in SIoT and can be realized through trust recommendations. However, the outcome of trust recommendations depends on multiple factors related to the context-dependent nature of SIoT and practical constraints brought by the devices and networks embedded in the SIoT. While the existing literature has proposed numerous trust recommendation models to assess the trustworthiness of devices in various scenarios, researchers have not sufficiently examined the required features for trust recommendations in the SIoT. Consequently, trust recommendation models may inaccurately assess the true risk of device interactions. In this literature survey, we investigate the context-dependent features and recommendation methods used for the SIoT using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We propose a novel taxonomy to categorize trust recommendation models according to their input features and design. Our findings reveal limited attention is given to the context-dependent features, constraints of the information environment, and limited inference capabilities that impede more precise trust recommendations. Finally, we present the research gaps and outline future directions to enable trustworthy inter-domain operations within the SIoT.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 6

June 2024

963 pages

EISSN:1557-7341

DOI:10.1145/3613600

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Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 13 March 2024

Online AM: 08 February 2024

Accepted: 31 January 2024

Revised: 20 September 2023

Received: 28 May 2023

Published in CSUR Volume 56, Issue 6

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Lygizou ZKalles D(2024)Using Deep Q-Learning to Dynamically Toggle between Push/Pull Actions in Computational Trust MechanismsMachine Learning and Knowledge Extraction10.3390/make60300676:3(1413-1438)Online publication date: 27-Jun-2024
https://doi.org/10.3390/make6030067
Khelloufi AKhelil ANaouri ASada ANing HAung NDhelim S(2024)A Hybrid Feature and Trust-Aggregation Recommender System in the Social Internet of ThingsIEEE Access10.1109/ACCESS.2024.341188712(126460-126477)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3411887

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