随着传感器丰富的智能设备(智能手机,iPad,等。),结合收集大量数据的需要,近年来,移动人群感知(MCS)逐渐引起了学术界的关注。MCS是一种新的有前途的大规模感知和计算数据收集模型。主要功能是使用移动设备招募一大群参与者,以在给定区域内执行感测任务。任务分配是MCS系统中的一个重要研究课题,旨在有效地将传感任务分配给招募的工人。以前的研究集中在贪婪或启发式方法上,而由于各种资源和质量限制,MCS任务分配问题通常是NP难优化问题,传统的贪婪或启发式方法通常会在一定程度上遭受性能损失。此外,以平台为中心的任务分配模型通常考虑平台的利益,而忽略其他参与者的感受,不利于平台的发展。因此,在本文中,深度强化学习方法用于找到更有效的任务分配解决方案,并采用加权方法对多个目标进行优化。具体来说,我们使用基于决斗架构的双深度Q网络(D3QN)来解决任务分配问题。由于工人的最大旅行距离,奖励价值,并考虑了传感任务的随机到达和时间灵敏度,这是一个多约束下的动态任务分配问题。对于动态问题,传统的启发式(例如,pso,遗传学)通常很难从建模和实践的角度来解决。强化学习可以通过序贯决策的方式在有限的时间内获得次优或最优解。最后,我们将提出的基于D3QN的解决方案与标准基线解决方案进行比较,实验表明,它在平台利润方面优于基准解决方案,任务完成率,等。,平台的实用性和吸引力得到增强。
With the coverage of sensor-rich smart devices (smartphones, iPads, etc.), combined with the need to collect large amounts of data, mobile crowd sensing (MCS) has gradually attracted the attention of academics in recent years. MCS is a new and promising model for mass perception and computational data collection. The main function is to recruit a large group of participants with mobile devices to perform sensing tasks in a given area. Task assignment is an important research topic in MCS systems, which aims to efficiently assign sensing tasks to recruited workers. Previous studies have focused on greedy or heuristic approaches, whereas the MCS task allocation problem is usually an NP-hard optimisation problem due to various resource and quality constraints, and traditional greedy or heuristic approaches usually suffer from performance loss to some extent. In addition, the platform-centric task allocation model usually considers the interests of the platform and ignores the feelings of other participants, to the detriment of the platform\'s development. Therefore, in this paper, deep reinforcement learning methods are used to find more efficient task assignment solutions, and a weighted approach is adopted to optimise multiple objectives. Specifically, we use a double deep Q network (D3QN) based on the dueling architecture to solve the task allocation problem. Since the maximum travel distance of the workers, the reward value, and the random arrival and time sensitivity of the sensing tasks are considered, this is a dynamic task allocation problem under multiple constraints. For dynamic problems, traditional heuristics (eg, pso, genetics) are often difficult to solve from a modeling and practical perspective. Reinforcement learning can obtain sub-optimal or optimal solutions in a limited time by means of sequential decision-making. Finally, we compare the proposed D3QN-based solution with the standard baseline solution, and experiments show that it outperforms the baseline solution in terms of platform profit, task completion rate, etc., the utility and attractiveness of the platform are enhanced.