In the Internet of Vehicles, vehicular crowdsensing is crucial for alleviating traffic congestion and ensuring the safety of autonomous driving. However, practical vehicular crowdsensing processes face dual challenges of skewed spatial distributions of vehicles and inadequate data quality guidance. These issues cause sensing redundancy in high-participation areas (HPAs) and coverage deficits in low-participation areas (LPAs), while also leading to unstable data quality. Given that participants’ decisions are profoundly influenced by bounded rationality and psychological preferences, this paper proposes a collaborative incentive mechanism integrating behavioral economics and psychology (BEP-IM) to drive sustained spatial coverage and proactive sensing shaping. First, to mitigate coverage deficits in LPA, a reference-dependent two-sided selection and bidding strategy (RD-TSB) is designed to guide participants toward LPA via a reference-driven utility evaluation. Concurrently, a loss-aversion-based sustained incentive strategy (LA-RPI) is introduced to enhance their sustained participation within LPAs by amplifying loss perception. Furthermore, to overcome weak data quality constraints, an operant conditioning-based proactive sensing shaping strategy (OC-SFQ) is constructed, utilizing a closed-loop mechanism of relative improvement, variable-ratio reinforcement, and association updating to drive participants to output high-quality data. Simulation results demonstrate that the proposed mechanism effectively increases participation frequency in LPAs and optimizes sensing data quality.
Zhang et al. (Tue,) studied this question.
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