ASC: Actuation system for city-wide crowdsensing with ride-sharing vehicular platform

Abstract

Vehicular mobile crowdsensing (MCS) enables a lot of smart city applications, such as smart transportation, environmental monitoring etc. Taxis provide a good platform for MCS due to their long operational time and city-scale coverage. However, taxis, as a non-dedicated sensing platform, does not guarantee high sensing coverage quality (large and balanced). This paper presents ASC, a system that actuates vehicular taxis fleets for optimal sensing coverage quality while matching ride requests with taxis. We propose a near-optimal algorithm that integrates 1) a mobility prediction model that guides the selection of taxis to actuate and 2) a ride request prediction model to help match ride request with taxis, lower incentive cost and improve taxi drivers' motivation. Extensive simulation and real-world experiments in a testbed with 230 actuated taxis show that our ASC can achieve up to 40% improvement in sensing coverage quality improvement and up to 20% better ride request matching rate than baselines approaches. In addition, to achieve a similar level of sensing coverage quality, our ASC only requires 10% of the baseline budget.