Optimizing Multitask Assignment of Internet of Things Devices by Reinforcement Learning in Mobile Crowdsensing Scenes

The objective is to optimize the multitask assignment (MTA) in mobile crowdsensing (MCS) scenarios. From the perspective of reinforcement learning (RL), an Internet of Things (IoT) devices-oriented MTA model is established using MCS, IoT technology, and other related theories. Then, the data collected by the University of Cambridge and the University of St. Andrews are chosen to verify the three MTA algorithms on IoT devices. They are multistage online task assignment (MOTA), average makespan-sensitive online task assignment (AOTA), and water filling (WF). Experiments are designed by considering different algorithms’ MTA time consumption and accuracy in simple and complex task scenarios. The research results manifest that with a constant load or task quantity, the MOTA algorithm takes the shortest time to assign tasks. In simple task scenarios, MOTA is compared with the WF. The MOTA algorithm’s total moving distance is relatively short, and the task completion degree is the highest. AOTA algorithm lends best to complex tasks, with the highest MTA accuracy and the shortest time consumption. Therefore, the research on IoT devices’ MTA optimization based on RL in the MCS scenario provides a certain theoretical basis for subsequent MTA studies.