Mobile Crowdsensing for Data Freshness: A Deep Reinforcement Learning Approach

Data collection by mobile crowdsensing (MCS) is emerging as data sources for smart city applications, however how to ensure data freshness has sparse research exposure but quite important in practice. In this paper, we consider to use a group of mobile agents (MAs) like UAVs and driverless cars which are equipped with multiple antennas to move around in the task area to collect data from deployed sensor nodes (SNs). Our goal is to minimize the age of information (AoI) of all SNs and energy consumption of MAs during movement and data upload. To this end, we propose a centralized deep reinforcement learning (DRL)-based solution called "DRL-freshMCS" for controlling MA trajectory planning and SN scheduling. We further utilize implicit quantile networks to maintain the accurate value estimation and steady policies for MAs. Then, we design an exploration and exploitation mechanism by dynamic distributed prioritized experience replay. We also derive the theoretical lower bound for episodic AoI. Extensive simulation results show that DRL-freshMCS significantly reduces the episodic AoI per remaining energy, compared to five baselines when varying different number of antennas and data upload thresholds, and number of SNs. We also visualize their trajectories and AoI update process for clear illustrations.