An Artificial Intelligence Approach for Enhancing Trust Between Social IoT Devices in a Network

Kumar, J. Senthil; Sivasankar, G.; Nidhyananthan, S. Selva

doi:10.1007/978-3-030-24513-9_11

Cited by 14 publications

(4 citation statements)

References 17 publications

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“…Social media data may be used to monitor issues related to public health, spot new health trends, and make context‐sensitive health forecasts. attitude analysis aids in comprehending public attitudes about health‐related events, while advanced natural language processing (NLP) techniques are utilized to extract pertinent health information from social media posts 123 . Personalized health monitoring systems become more accurate and comprehensive when this data is integrated with other health sources.…”

Section: Discussionmentioning

confidence: 99%

Deep learning for personalized health monitoring and prediction: A review

Damaševičius,

Jagatheesaperumal,

Kandala

et al. 2024

Computational Intelligence

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Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL‐based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain.

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Section: Discussionmentioning

confidence: 99%

Deep learning for personalized health monitoring and prediction: A review

Damaševičius,

Jagatheesaperumal,

Kandala

et al. 2024

Computational Intelligence

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“…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%

“…protocol is effective against BMA and BSA because it considers trustor-recommender relationships. [93] proposes assessing trustworthiness on both sides of the SIoT to protect the trust relationship between the trustor and trustee. SIoT devices accept task delegation based on requests, ensuring resources aren't misused.…”

Section: G Mechanisms To Provide Resiliency Against Intrinsic and Ext...mentioning

confidence: 99%

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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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