Data Acquisition in Social Internet of Things based on Contract Theory

Sangoleye, Fisayo; Irtija, Nafis; Tsiropoulou, Eirini Eleni

doi:10.1109/icc42927.2021.9500254

Cited by 11 publications

(3 citation statements)

References 11 publications

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“…Specifically, an environment affected by high interference and noise will result in a reduction of the successful data transmission probability, and as a consequence the node trustworthiness degree will decrease. It can be observed that the proposed approach is similar to other existing works that exploit the concept of contract theory, mainly for data acquisition and trust management in social IoT [12], [13]. Specifically, in [12] both social and physical features of nodes in SIoT are analyzed to define the nodes' unique types, while in [13] the authors exploit the principles of network economics for trust management based on the efforts of node neighbors.…”

Section: Node Trustworthiness Degreementioning

confidence: 82%

“…Different from [12], [13], in this paper we exploit the successful transmission probability achievable in a vehicular environment, which depends on the outage probability that is affected by the propagation environment, the transmission modes i.e., Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I), and the level of interference. In our network scenario, we assume vehicles communicate to each other via V2V dedicated short range communications.…”

Section: Node Trustworthiness Degreementioning

confidence: 99%

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A Reputation-Based Trustworthiness Concept for Wireless Networking in Vehicular Social Networks

Vegni,

Leoni,

Loscrí

et al. 2024

IEEE Commun. Mag.

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Social features are affecting different application areas, from traditional online social networks to economical networks, as well as the study of epidemical behaviors of pandemics and diseases. From the information point of view, also the Internet paradigm has been revised according to social behaviors of nodes, showing intrinsic properties that have allowed the definition of the Social Internet of Things paradigm. Furthermore, considering vehicular networks, social features have been spreading their influence, and defined the concept of Vehicular Social Networks, where mobile nodes show predictable social behavior. In this context, all the networking concepts should be updated accordingly, taking into account node social aspects and interactions. In this paper, we discuss the open challenge of how we should define a trust vehicle, relying on social networking aspects. We introduce the concept of node trustworthiness in a vehicular environment, based on the reputation degree, computed according to real-time interactions and past data. A node can be defined as trusted if exhibiting an acceptable reputation degree, and being successfully able to transmit data packet in a given environment. The framework of a network architecture for the computation of node trustworthiness degree in a vehicular scenario is also presented.

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Section: Node Trustworthiness Degreementioning

confidence: 82%

Section: Node Trustworthiness Degreementioning

confidence: 99%

A Reputation-Based Trustworthiness Concept for Wireless Networking in Vehicular Social Networks

Vegni,

Leoni,

Loscrí

et al. 2024

IEEE Commun. Mag.

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“…A novel matrix-factorizationbased trust prediction model is presented in [33] to address issues related to the sparse social interaction and low trust utilization in OSNs via integrating the social trust graph and recommendation system data to improve the predictive performance of OSN users. A crisis-assessment-oriented and topic-based influence maximization framework is proposed in [34] to address the risk of negative and untrusted information in OSNs [35]. A qualitative model of swift trust formation in global virtual teams is presented in [36] offering insights into how these teams develop trust behaviors among each other for effective virtual collaboration and international management.…”

Section: Online Advertisementmentioning

confidence: 99%

INFLUTRUST: Trust-Based Influencer Marketing Campaigns in Online Social Networks

Adesokan,

Rahman,

Tsiropoulou

2024

Future Internet

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This paper introduces the INFLUTRUST framework that is designed to address challenges in trust-based influencer marketing campaigns on Online Social Networks (OSNs). The INFLUTRUST framework enables the influencers to autonomously select products across the OSN platforms for advertisement by employing a reinforcement learning algorithm. The Stochastic Learning Automata reinforcement algorithm considers the OSN platforms’ provided monetary rewards, the influencers’ advertising profit, and the influencers’ trust levels towards the OSN platforms to enable the influencers to autonomously select an OSN platform. The trust model for the influencers incorporates direct and indirect trust, which are derived from past interactions and social ties among the influencers and the OSN platforms, respectively. The OSN platforms allocate rewards through a multilateral bargaining model that supports competition among the influencers. Simulation-based results validate the INFLUTRUST framework’s effectiveness across diverse scenarios, with the scalability analysis demonstrating its robustness. Comparative evaluations highlight the INFLUTRUST framework’s superiority in considering trust levels and reward allocation fairness, benefiting both the influencers and the OSN platforms.

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