A Game Theoretical Model addressing Misbehavior in Crowdsourcing IoT - Inria - Institut national de recherche en sciences et technologies du numérique
Conference Papers Year : 2023

A Game Theoretical Model addressing Misbehavior in Crowdsourcing IoT

Runbo Su
Connectez-vous pour contacter l'auteur
Arbia Riahi Sfar
Enrico Natalizio
Ye-Qiong Song

Abstract

Crowdsourcing technology enables complex tasks to be solved with the aid of a group of workers in the Internet of Things (IoT). On the one hand, crucial sensing data can be collected and processed to enhance smart IoT applications. On the other hand, crowdsourcing IoT (Crowd-IoT) is still facing threats due to the diverse quality of crowdsourced data, and especially the misbehavior of malicious workers. In this paper, we propose a Stochastic Bayesian Game (SBG) to address the Byzantine Altruistic Rational (BAR) based misbehavior, where workers' behavioral types can be deduced reasonably and the requestor can perform optimal actions accordingly by taking the long-term gain into consideration. To validate and evaluate the performance of the proposed model, we simulate various scenarios and conduct a comparison with other solutions. The numerical results show the effectiveness and feasibility of our proposed solution.
Fichier principal
Vignette du fichier
SECON_Runbo_Su.pdf (1.59 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04205286 , version 1 (13-09-2023)

Licence

Identifiers

Cite

Runbo Su, Arbia Riahi Sfar, Enrico Natalizio, Pascal Moyal, Ye-Qiong Song. A Game Theoretical Model addressing Misbehavior in Crowdsourcing IoT. 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON 2023), IEEE, Sep 2023, Madrid, Spain. ⟨10.1109/SECON58729.2023.10287527⟩. ⟨hal-04205286⟩
173 View
163 Download

Altmetric

Share

More