FedCrowdSensing: Incentive Mechanism for Crowdsensing Based on Reputation and Federated Learning

Ouyang, Jianquan; Wang, Wenke

doi:10.1109/iscc58397.2023.10217955

Cited by 1 publication

(4 citation statements)

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“…Ref. [18] proposed a freshnessaware incentive mechanism to recruit suitable mobile users, thereby optimizing the average information freshness and data quality while adhering to budget constraints. Ref.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, few incentive mechanisms that introduce uncertainty take into account user preferences and their associated probabilities. Users are incentivized with deterministic utility or combined with fixed probabilities [7,18,23,25]. For instance, Ref.…”

Section: Related Workmentioning

confidence: 99%

“…Refs. [18,23] both derived probabilities from fixed historical information to recommend tasks to users. However, fixed probabilities cannot reflect the uncertainty caused by user preferences [14].…”

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%