Age-of-Information Driven Mobile Crowdsensing in Wireless Edge Computing

Recently, a novel Mobile Agent-based Mobile Crowdsensing (MA-MCS) paradigm has emerged, which utilizes unmanned aerial vehicles, pilotless automobiles, etc., to implement data sensing from surroundings. Practically, in time-sensitive applications the freshness of collected data is of critical importance. However, restricted by the intermittent bandwidth of wireless channels, the generated data might not be successfully transferred to the application server for further processing. Fortunately, benefit from the hosted edge computing capability, it is feasible to locally process the sensing data and then transfer the concise result. In this paper, we study a novel and practical problem in the Age-of-Information (AoI) driven MA-MCS systems in wireless edge computing. Specifically, restricted by the issues of battery recharging, etc., we strive to schedule the mobile agents and make transmission/computing decisions in a collaborative manner, aiming at minimizing the total AoI threshold violation. To this end, we propose a two-phase deep reinforcement learning-based solution, namely KCDDQN, including spatiotemporal clustering and decision-making learning. Finally, extensive simulations are carried out to demonstrate the effectiveness of our proposed KCDDQN approach.