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Free Market of Multi-Leader Multi-Follower Mobile Crowdsensing: An Incentive Mechanism Design by Deep Reinforcement Learning

机译:多领导多追随者移动众包的自由市场:深增强学习的激励机制设计

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摘要

The explosive increase of mobile devices with built-in sensors such as GPS, accelerometer, gyroscope and camera has made the design of mobile crowdsensing (MCS) applications possible, which create a new interface between humans and their surroundings. Until now, various MCS applications have been designed, where the task initiators (TIs) recruit mobile users (MUs) to complete the required sensing tasks. In this paper, deep reinforcement learning (DRL) based techniques are investigated to address the problem of assigning satisfactory but profitable amount of incentives to multiple TIs and MUs as a MCS game. Specifically, we first formulate the problem as a multi-leader and multi-follower Stackelberg game, where TIs are the leaders and MUs are the followers. Then, the existence of the Stackelberg Equilibrium (SE) is proved. Considering the challenge to compute the SE, a DRL based Dynamic Incentive Mechanism (DDIM) is proposed. It enables the TIs to learn the optimal pricing strategies directly from game experiences without knowing the private information of MUs. Finally, numerical experiments are provided to illustrate the effectiveness of the proposed incentive mechanism compared with both state-of-the-art and baseline approaches.
机译:具有内置传感器的移动设备的爆炸性增加,如GPS,加速度计,陀螺仪和相机,使得移动人群(MCS)应用成为可能的设计,可以在人类及其周围环境之间创建新的界面。到目前为止,已经设计了各种MCS应用程序,其中任务启动器(TIS)招募移动用户(MUS)以完成所需的传感任务。在本文中,研究了基于深度的加强学习(DRL)技术,解决了为多个TIS和MUS作为MCS游戏的令人满意但有利可图的激励量的问题。具体而言,我们首先将问题制定为一个多领导和多追随者Stackelberg游戏,其中TIS是领导者,Mus是追随者。然后,证明了Stackelberg均衡(SE)的存在。考虑到计算SE的挑战,提出了基于DRL的动态激励机制(DDIM)。它使TIS能够直接从游戏体验中学习最佳定价策略,而不知道Mus的私人信息。最后,提供了数值实验,以说明所提出的激励机制的有效性与最先进的和基线方法相比。

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