Poster Abstract: Vehicle Dispatching for Sensing Coverage Optimization in Mobile Crowdsensing Systems

Mobile crowd sensing (MCS) collects city-scale sensing data with low cost and high efficiency. One important goal of MCS is to ensure high quality of sensing coverage to provide sufficient information to data analysis end. However, the goal of the MCS may be inconsistent with the goal of vehicles. This inconsistency between goals results in a bad sensing coverage and decreases the quality of the collected information. Key challenges to resolve this inconsistency include the heterogeneous target desired spatio-temporal distributions, limited budget constraining the ability to incentivize more taxis, and high computational complexity. This work shows a vehicle dispatching system to optimize the sensing coverage of the sampled data with a limited budget and taxis. Our system models the objective function using the similarity between the collected data distribution and target distribution, and introduces an algorithm combining vehicle mobility prediction and ride request prediction to obtain an approximate-optimal vehicle dispatching strategy in an efficient way. The preliminary experiment based on the real-world data shows a significant improvement of the sensing coverage.