OceanCrowd: Vessel Trajectory Data-Based Participant Selection for Mobile Crowd Sensing in Ocean Observation

Abstract

With the in-depth study of the internal process mechanism of the global ocean by oceanographers, traditional ocean observation methods have been unable to meet the new observation requirements. In order to achieve a low-cost ocean observation mechanism with high spatio-temporal resolution, this paper introduces mobile crowd sensing technology into the field of ocean observation. First, a Transformer-based vessel trajectory prediction algorithm is proposed, which can monitor the location and movement trajectory of vessel in real time. Second, the participant selection algorithm in mobile crowd sensing is studied, and based on the trajectory prediction algorithm, a dynamic participant selection algorithm for ocean mobile crowd sensing is proposed by combining it with the discrete particle swarm optimization (DPSO) algorithm. Third, a coverage estimation algorithm is designed to estimate the coverage of the selection scheme. Finally, the spatio-temporal resolution of the vessel's driving trajectory is analyzed through experiments, which verifies the effectiveness of the algorithm and comprehensively confirms the feasibility of mobile crowd sensing in the field of ocean observation.