Toward Incentive-Compatible Vehicular Crowdsensing: An Edge-Assisted Hierarchical Framework

Yang, Xinxin; Gu, Bo; Zheng, Bing-Kun; Ding, Beichen; Han, Yu; Yu, Keping

doi:10.1109/mnet.104.2000773

Cited by 11 publications

(4 citation statements)

References 11 publications

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“…Ref. [19] employed the Strutberg game to analyse the interaction between edge servers and vehicles in vehicle crowd sensing, and derived optimal bidding strategies for vehicles participating in sensing activities through multi-agent actor-critic neural networks to optimize task allocation for overall data quality. Ref.…”

Section: Related Workmentioning

confidence: 99%

“…The platform can only motivate users through higher rewards to obtain higher participation levels and data quality. For instance, the reward distribution mechanism in [10,19,26,32] is also based on absolute values. Refs.…”

Section: Related Workmentioning

confidence: 99%

“…Secondly, users make decisions based on relative changes or differences between the final outcome and a reference point, rather than absolute gains or losses. Therefore, to increase participation levels and data quality on crowdsensing platforms, it is necessary to increase cost investment [15][16][17][18][19]. It is important to note that individual users identify outcomes based on reference points, not the absolute final outcome level [20].…”

Section: Introductionmentioning

confidence: 99%

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TB-HQ: An Incentive Mechanism for High-Quality Cooperation in Crowdsensing

Zhao,

Zeng,

Wang

et al. 2024

Electronics

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Crowdsensing utilizes a range of sensing resources and participants, including mobile device sensors, to achieve collaborative sensing and information fusion. This enables it to handle complex social sensing tasks and provide more intelligent and real-time environment sensing services. Incentive mechanisms in crowdsensing are employed to address issues related to insufficient user participation and low-quality data submission. However, existing mechanisms fail to adequately consider reference points in user decision-making and uncertainty in the decision-making environment. This results in high incentive costs for the platform and limited effectiveness. On the one hand, the probabilities and utilities in the actual decision environment are defined based on user preferences, and uncertainty can lead to unpredictable impacts on users’ future gains or losses. On the other hand, users identify their choices based on certain known values, namely reference points. The factors influencing user decisions are not solely the absolute final result level but rather the relative changes or differences between the final result and the reference point. Therefore, to resolve this problem, we propose TB-HQ, an incentive mechanism for high-quality cooperation in crowdsensing, which simultaneously considers the reference points adopted by users in decision-making and the uncertainty caused by their preferences. This mechanism includes a task bonus-based incentive mechanism (TBIM) and a high quality-driven winner screening mechanism (HQWSM). TBIM motivates users to participate in tasks by offering task bonuses, which alter their reference points. HQWSM enhances data quality by reconstructing utility functions based on user preferences. Simulation results indicate that the proposed incentive mechanism is more effective in improving data quality and platform utility than the comparative incentive mechanisms, with a 32.7% increase in data quality and a 77.3% increase in platform utility.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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TB-HQ: An Incentive Mechanism for High-Quality Cooperation in Crowdsensing

Zhao,

Zeng,

Wang

et al. 2024

Electronics

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“…As science and technology advance, various sensors collect traffic flows (i.e., a kind of traffic statistic) accurately and in real-time [1][2][3][4][5][6]. Real-time traffic data records the time sequence information of the road and can describe the traffic status in more detail.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Real-Time Traffic Data Sharing: A Differential Privacy-Based Scheme with Spatial Correlation

Le,

Xing,

Zhang

et al. 2024

Mathematics

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The real-time sharing of traffic data can offer improved services to users and timely respond to environmental changes. However, this data often involves individuals’ sensitive information, raising substantial privacy concerns. It is imperative to find ways to protect the privacy of the shared traffic data while maintaining its ongoing data utility. In this paper, a Differential Privacy-based scheme with Spatial Correlation for Real-time traffic data (named as DP-SCR) is proposed. DP-SCR not only ensures the high data utility of shared traffic data, but also provides strong privacy protection. Specifically, DP-SCR is designed to adhere to w-event ε-differential privacy, ensuring a high level of privacy protection. Subsequently, a novel adaptive allocation based on spatial correlation prediction is proposed to optimize the privacy budget allocation in differential privacy. In addition, a feasible dynamic clustering algorithm is developed to minimize the relative perturbation error, which further improves the quality of shared data. Finally, the analyses demonstrate that DP-SCR provides w-event privacy for the shared data of each section, and the spatial correlation is a more pronounced characteristic of the traffic data than other characteristics. Meanwhile, experiments conducted on real-world data show that the MAR and MER of the predicted data in DP-SCR are smaller than those in other baseline DP-based schemes. It indicates that the DP-SCR scheme proposed in this paper can provide more accurate shared data.

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A comprehensive survey on communication techniques for the realization of intelligent transportation systems in IoT based smart cities

Rajkumar,

Santhosh Kumar

2024

Peer-to-Peer Netw. Appl.

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Toward Incentive-Compatible Vehicular Crowdsensing: An Edge-Assisted Hierarchical Framework

Cited by 11 publications

References 11 publications

TB-HQ: An Incentive Mechanism for High-Quality Cooperation in Crowdsensing

TB-HQ: An Incentive Mechanism for High-Quality Cooperation in Crowdsensing

Enhancing Real-Time Traffic Data Sharing: A Differential Privacy-Based Scheme with Spatial Correlation

A comprehensive survey on communication techniques for the realization of intelligent transportation systems in IoT based smart cities

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