Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes.
翻译:然而,对于常规监控监,大量消费通信资源用于原始数据传输,对数据储存和计算能力的要求很高,这些都阻碍了使用监控监的潜在请求人。 为便利广泛应用监控监,我们提议一个全新的监控监学习框架,利用连锁技术和基于联邦学习(FL)的边际情报新概念,这涉及四个主要实体,包括请求者、链路、边缘服务器和移动设备,以更具灵活性和较低成本完成各种遥感任务。尽管对基于链路的监控监和基于链路的FL进行了一些研究,但它们无法解决监控监在接纳资源紧张的请求人或处理由于请求者和工人参与学习过程而产生的隐私问题方面所面临的基本挑战。 为填补空白,四个主要程序,即任务发布、数据检测和提交、学习最终结果回归、支付结算和分配等,旨在应对内部和外部威胁带来的重大挑战,例如恶意边缘服务器和基于链路路路的Frichal Servical Servoral Services, 拟议的基于基于准确的保密服务器规则设计数据设计流程,这是基于基于基于保密交易链路段规则进行的安全数据配置,一个基于数据保存机制。