A Hybrid Optimization Framework for Age of Information Minimization in UAV-Assisted MCS

Abstract

UAVs-enabled Mobile Crowdsensing (UMCS) has gained considerable attention recently, but it is challenging to meet the data collection needs of the entire city using only the UAV with limited energy. Furthermore, how to effectively minimize Age-of-Information (AoI) and ensure data quality has not been well solved in previous studies. Therefore, this paper proposes a hybrid optimization framework for AoI minimization, which recruits massive distributed workers as the main force for data collection, while the UAV acts as a data collection collaborator and is more inclined to fly to the SNs that cannot establish connections with workers, To mitigate the potential security threats incurred by dishonest workers of the MCS system, we first provide a Greedy-based Multi-worker Task Assignment (GMTA) strategy, aiming to assign more urgent data collection tasks to reliable workers under workload constraints. Then, we propose a Deep-Reinforcement-Learning-based Global AoI Minimization (DRL-GAM) strategy for the UAV path planning to find a set of optimal actions to minimize the global AoI. Based on the real dataset, our simulation experiments show that compared with traditional strategies, our DRL-GAM strategy can reduce the global AoI by an average of 6.49%$\sim$68.21% in various network sizes, and is more stable for the average standard deviation is only 51.75% of other strategies.