QUAD：一个面向策略环境下IoT-Based移动人群感知的质量感知多单元双向拍卖框架
QUAD: A Quality Aware Multi-Unit Double Auction Framework for IoT-Based Mobile Crowdsensing in Strategic Setting
摘要：
近年来，使用携带智能设备的智能代理的众包变得越来越流行。它已经打开了一个广泛的现实生活应用列表，如测量空气污染水平，道路交通信息等。在文献中，这被称为移动众包或移动众感。在本文中，所讨论的设置由多个任务请求者（或任务提供者）和多个物联网设备（作为任务执行器）组成，其中每个任务提供者都具有多个同构的感测任务。每个任务请求者向平台报告出价以及同类感测任务的数量。另一方面，我们有多个物联网设备，它们报告请求（提供服务的费用）和它可以执行的传感任务的数量。任务请求者和物联网设备的估值是私人信息，两者都可能采取战略行动。本文中的一个假设是，代理（任务提供者和物联网设备）的出价和要价遵循边际收益递减准则。在本文中，提出了一种真实的机制来分配物联网设备的任务请求者所携带的传感任务，这也考虑到物联网设备的质量。该机制是真实的，预算平衡，个人理性，计算效率，和先验自由。进行了模拟，以衡量所提出的机制对基准机制的性能。代码和合成数据可在\textbf{https://github.com/Samhitha-Jasti/QUAD-Implementation}上获得。
英文摘要：
Crowdsourcing with the intelligent agents carrying smart devices is becoming increasingly popular in recent years. It has opened up meeting an extensive list of real life applications such as measuring air pollution level, road traffic information, and so on. In literature this is known as mobile crowdsourcing or mobile crowdsensing. In this paper, the discussed set-up consists of multiple task requesters (or task providers) and multiple IoT devices (as task executors), where each of the task providers is having multiple homogeneous sensing tasks. Each of the task requesters report bid along with the number of homogeneous sensing tasks to the platform. On the other side, we have multiple IoT devices that reports the ask (charge for imparting its services) and the number of sensing tasks that it can execute. The valuations of task requesters and IoT devices are private information, and both might act strategically. One assumption that is made in this paper is that the bids and asks of the agents (task providers and IoT devices) follow decreasing marginal returns criteria. In this paper, a truthful mechanism is proposed for allocating the IoT devices to the sensing tasks carried by task requesters, that also keeps into account the quality of IoT devices. The mechanism is truthful, budget balance, individual rational, computationally efficient, and prior-free. The simulations are carried out to measure the performance of the proposed mechanism against the benchmark mechanisms. The code and the synthetic data are available at \textbf{https://github.com/Samhitha-Jasti/QUAD-Implementation}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
