Incentivizing Large-scale Vehicular Crowdsensing System For　Smart City Applications

摘要

Mobile crowd sensing (MCS) enables many smart city applications (e.g., transportation monitoring/management,environmental monitoring, etc.). Recently, MCS systems built on non-dedicated vehicular platforms like taxishave become popular due to their large-scale coverage and low-cost deployment and maintenance. However,the goal of MCS may be inconsistent with the goal of vehicles. For example, MCS expects to get large andbalanced sensing coverage over the city, while the taxis gather in busy areas to search for new ride requests. Thisinconsistency between the goals of MCS and vehicles results in a low sensing coverage and decreases the qualityof the collected information.To address this inconsistency and optimize the sensing coverage, this paper presents an incentivizing systemto optimize the sensing coverage of the sampled data. Key challenges to resolving this inconsistency includelimited budget constraining the ability to incentivize more vehicles and complicate vehicle and trajectory selec-tion problem making it diu000ecult to obtain the incentivizing strategy. To address these challenges, we design acustomized incentive by combining monetary incentives and potential ride request at the destination to reducethe cost of incentivizing vehicles and utilize the budget eu000eciently. Meanwhile, we formulate the problem of incen-tivizing trajectory planning as a non-linear multiple-choice knapsack problem, and propose a heuristic algorithmto approximate the optimal incentivizing strategy. The experiments based on the real-world data show that oursystem achieves up to 26.99% improvement in the sensing coverage compared to benchmark methods.