Quality-Aware Task Allocation for Mobile Crowd Sensing Based on Edge Computing

In the field of mobile crowd sensing (MCS), traditional client–cloud architecture faces increasing challenges in communication and computation overhead. To address these issues, this paper introduces edge computing into MCS system proposes a two-stage task allocation optimization method under constraint limited resources. The utilizes deep reinforcement learning for selection optimal servers deployment, followed by greedy self-adaptive stochastic algorithm recruitment participants. simulations, proposed demonstrated 20% improvement spatial coverage compared with existing RBR outperformed LCBPA, SMA, MOTA algorithms 41, 42, 48 tasks, respectively. This research contributes to advances integration systems.