Fu Yanming, Shen Yuming, Tang Liang
School of Computer and Electronic Information, Guangxi University, No. 100, University East Road, Nanning 530004, China.
Sensors (Basel). 2023 Jul 1;23(13):6088. doi: 10.3390/s23136088.
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.
随着富含传感器的智能设备（智能手机、iPad等）的普及，结合收集大量数据的需求，移动人群感知（MCS）近年来逐渐引起了学术界的关注。MCS是一种用于大规模感知和计算数据收集的新型且有前景的模型。其主要功能是招募大量拥有移动设备的参与者在给定区域执行感知任务。任务分配是MCS系统中的一个重要研究课题，旨在将感知任务高效地分配给招募的工人。先前的研究主要集中在贪婪或启发式方法上，而由于各种资源和质量约束，MCS任务分配问题通常是一个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只考虑平台的利益，而忽略其他参与者的感受，这对平台的发展不利。因此，在本文中，我们使用深度强化学习方法来寻找更高效的任务分配解决方案，并采用加权方法来优化多个目标。具体来说，我们使用基于对决架构的双深度Q网络（D3QN）来解决任务分配问题。由于考虑了工人的最大行进距离、奖励值以及感知任务的随机到达和时间敏感性，这是一个多约束下的动态任务分配问题。对于动态问题，传统启发式方法（如粒子群优化算法、遗传算法）从建模和实际角度往往难以解决。强化学习可以通过顺序决策在有限时间内获得次优或最优解。最后，我们将基于D3QN提出的解决方案与标准基线解决方案进行比较，实验表明，在平台利润、任务完成率等方面，该方案优于基线解决方案，提升了平台的效用和吸引力。
