Simulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems

Kalui, Dorothy; Zhang, Dezheng; Muketha, Geoffrey Muchiri; Onsomu, Jared Okoyo

doi:10.1109/access.2020.2968797

Cited by 3 publications

(1 citation statement)

References 52 publications

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“…In this model, participants apply both active and passive sensing modes in the platform. In this way, the accuracy of the collected data is improved as certain information that users might be unable to share can be effectively collected opportunistically [45].…”

Section: Task Executionmentioning

confidence: 99%

Data Quality in Human-Centric Sensing-Based Next-Generation IoT Systems: A Comprehensive Survey of Models, Issues, and Challenges

Banti,

Louta,

Baziana

2023

IEEE Open J. Commun. Soc.

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Human-Centric Sensing (HCS), a novel approach in the evolution of the Next Generation Internet of Things (NG-IoT), exploits the ubiquity of diverse smart devices, including smartphones or wearable devices, in conjunction with their enhanced sensing capabilities to collect information, leveraging human intelligence for the common benefit of the crowd. The main feature of HCS is the involvement of mobile users in data collection, processing, analysis and sharing. Thus, the main challenge in HCS systems is to ensure users' participation and trustworthiness as well as data quality. The aim of this work is, as a first step, to identify and discuss the factors that affect data quality in HCS-based NG-IoT systems, as well as elaborate on their interrelation. Furthermore, potential solutions that could be adopted to ensure the highest possible degree of data quality are highlighted, in conjunction with critical aspects that should be considered, proposing a novel classification with three major categories: task assignment, reputation mechanisms and blockchain technology. Finally, a trust-aware task assignment model is proposed to effectively address the data quality challenge in HCS-based IoT systems, reflecting users' trustworthiness, willingness, experience, and ability to collect and share high-quality data contributions. The proposed trust-aware task assignment model exploits a reputation mechanism and is designed using blockchain and smart contract technologies to enable the decentralized provision of trustworthy services among entities and preserve users' privacy, harnessing the decentralization, transparency and immutability offered by blockchain. Trust-based task assignment offers an effective solution for trustworthy users' selection while ensuring high-quality contributions and users' privacy.

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Section: Task Executionmentioning

confidence: 99%

Data Quality in Human-Centric Sensing-Based Next-Generation IoT Systems: A Comprehensive Survey of Models, Issues, and Challenges

Banti,

Louta,

Baziana

2023

IEEE Open J. Commun. Soc.

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A multi-dimensional framework for improving data reliability in mobile crowd sensing

Wu,

Song,

Lai

2024

Egyptian Informatics Journal

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Nondeterministic Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing

Xie

Liu

Oaidat

et al. 2023

ACM Trans. Sen. Netw.

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Based on the Internet of Behavior (IoB), mobile crowd-sensing (MCS) utilizes the Internet of Things (IoT) to recruit users by analyzing behavioral patterns. MCS is widely used in numerous large-scale and complex monitoring services, but it cannot provide stable and high-quality services due to nondeterministic user mobility and behaviors , which has a vital impact on recruiting high-quality users. In this paper, a stochastic semi-algebraic hybrid system (SSAHS) model is constructed to characterize the user mobility and behaviors of the MCS systems. Based on the definition of probabilistic path and task execution rate, a nondeterministic evaluation mechanism is proposed to measure the nondeterministic user mobility and behaviors, and to give the probability of the user completing the MCS task under the specified time bound and space conditions. The greater the probability is , the higher the quality of the user. Furthermore, a user recruitment scheme based on a nondeterministic evaluation mechanism (NUR) is developed. The NUR uses historical user data to predict user mobility and behaviors ; therefore, high-quality users are recruited to quickly upload reliable sensing data. We conduct simulation experiments based on a real-world user traces dataset , Geolife. The results show that compared with competing recruitment strategies, NUR achieves a higher QoS for the same MCS sensing tasks.

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Simulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems

Cited by 3 publications

References 52 publications

Data Quality in Human-Centric Sensing-Based Next-Generation IoT Systems: A Comprehensive Survey of Models, Issues, and Challenges

Data Quality in Human-Centric Sensing-Based Next-Generation IoT Systems: A Comprehensive Survey of Models, Issues, and Challenges

A multi-dimensional framework for improving data reliability in mobile crowd sensing

Nondeterministic Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing

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