Context-Aware Worker Recruitment for Mobile Crowd Sensing Based on Mobility Prediction

Opportunistic worker (OW) selection is a challenging problem in mobile crowd sensing (MCS), where tasks are assigned to individuals to be completed seamlessly during their daily routines without any deviation from their usual routes. In this paper, we propose a novel framework named context-aware worker recruitment based on a mobility prediction model (CAMP) to address the OW selection problem in MCS. Unlike previous approaches that relied on worker mobility prediction models with limited accuracy or utility-based selection methods neglecting task distribution differences across locations, CAMP introduces a two-phase strategy for OW selection. In the first phase, we leverage a recurrent neural network-based prediction model specifically designed to forecast volunteer workers' future locations with higher precision. This enhanced mobility prediction ensures more effective task assignments in the MCS system. In the second phase, CAMP employs a weighted-utility algorithm that takes into account the varying task distribution throughout the day across different locations. The key novelty of the CAMP framework lies in its combination of an accurate multi-output RNN model for predicting worker mobility and a unique weighted-utility worker selection algorithm that considers variations in task distribution across different locations and sensing cycles. To validate the effectiveness of the CAMP framework, we extensively evaluate it using real-world GPS data, specifically the Crawdad Roma/Taxi dataset. The results demonstrate that CAMP outperforms existing approaches, delivering a higher number of completed tasks while adhering to the same budget constraints.