Determining Task Assignments for Candidate Workers Based on Trajectory Prediction

With the rise of sensor-equipped mobile devices, Mobile Crowd Sensing (MCS) has emerged as an efficient method for information gathering. In smart city environmental sensing, workers can acquire data by merely being within the sensing area. Currently, most studies select opportunistic workers based on the workers’ prior preferences and ignore the effect of movement trajectories on potential opportunistic workers. This may result in the selected opportunistic workers being less-than-ideal, or even ignoring the failure of some tasks to be accomplished, thus resulting in a waste of resources. Therefore, this paper proposes a Recruitment Framework for judging Opportunistic Workers based on Movement Trajectories (RFOW-MT), a two-phase framework for worker recruitment. In the offline phase, combining the neural network model Long Short-Term Memory (LSTM) and Geohash algorithm, an algorithm to detect the set of candidate opportunistic workers is proposed, solving the problems of location privacy and search efficiency. In the online phase, in order to maximize the task spatial coverage under the task budget constraint, a task allocation algorithm based on geographic location packed grouping is proposed. Finally, RFOW-MT outperforms other methods in terms of task spatial coverage and runtime as verified by experiments on real datasets.