论文标题

Quad:在战略环境中,用于基于物联网的移动人群的质量意识到的多单元双拍卖框架

QUAD: A Quality Aware Multi-Unit Double Auction Framework for IoT-Based Mobile Crowdsensing in Strategic Setting

论文作者

Singh, Vikash Kumar, Jasti, Anjani Samhitha, Singh, Sunil Kumar, Mishra, Sanket

论文摘要

近年来,携带智能设备的智能代理商越来越受欢迎。它已经开放了符合现实生活应用的广泛列表,例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包。在本文中,讨论的设置由多个任务请求者(或任务提供程序)和多个IoT设备(作为任务执行者)组成,其中每个任务提供商都有多个均质的传感任务。每个任务请求者都会向平台报告出价以及均质感应任务的数量。另一方面,我们有多个物联网设备报告问答(收取其服务的费用)以及可以执行的传感任务数量。任务请求者和IoT设备的估值是私人信息,两者都可能在战略上采取行动。本文提出的一个假设是,遵循降低边际收益标准的代理商(任务提供者和物联网设备)的出价和询问。在本文中,提出了一种真实的机制,用于将IoT设备分配给任务请求者所承担的传感任务,这也考虑到IoT设备的质量。该机制是真实的,预算的平衡,个人理性的,计算上有效的,并且没有事先。进行模拟以测量针对基准机制的拟议机制的性能。代码和综合数据可在\ textbf {https://github.com/samhitha-jasti/quad-implementation}中获得。

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}.

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