Simulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems WHERE DO YOU CLASS THIS

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dc.contributor.authorDOROTHY MWONGELI KALUI, DEZHENG ZHANG, GEOFFREY MUCHIRI MUKETHA, AND JARED OKOYO ONSOMU
dc.date.accessioned2026-04-01T08:07:50Z
dc.date.issued2020-01-31
dc.description.abstractWith the rapid development of mobile technology and subsequent mass adoption of mobile devices, mobile crowdsensing (MCS) has gained a lot of research attention. In MCS systems, trust is a key focus in the overall improvement in the participant uptake of the sensing tasks. The trust-based scheme of MCS is studied to predict the damage level, the scores of quality-of-service (QoS), and the levels of qualityof-data (QoD) of MCS systems. Users can participate in MCS sensing based on trustworthy indicators that are related to user experience and system reputation, as well as the knowledge obtained about the MCS systems. This paper illustrates the establishment of user confidence during recruitment in MCS as it is very critical for the success of MCS systems and proposes a simulation trust-based mechanism (SiTBaM) approach. The level of MCS security is enhanced to protect the privacy of participants, so that participants can be assured that the MCS system they are working with during sensing moment is trustworthy. The application of SiTBaM in MCS is verified to yield better results as the simulations show that it offers higher QoS levels, QoD scores, as well as low damage levels in the presence of any task or many malicious users. These results were validated through comparisons with other schemes.
dc.description.sponsorshipNRF
dc.identifier.uriDigital Object Identifier 10.1109/ACCESS.2020.2968797
dc.identifier.urihttps://repository.nrf.go.ke/handle/123456789/1591
dc.language.isoen
dc.publisherIEEE ACCESS
dc.subjectINDEX TERMS Mobile crowdsensing
dc.subjectquality-of-data
dc.subjectquality-of-service
dc.subjectsecurity
dc.subjecttrust-based scheme
dc.titleSimulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems WHERE DO YOU CLASS THIS
dc.typeArticle

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