CRII: CNS: Blockchain-based Distributed Machine Learning for Mobile Crowd Sensing

CRII：CNS：用于移动人群感知的基于区块链的分布式机器学习

• 批准号: 2105004
• 负责人: Qin Hu
• 金额: $ 17.48万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2105004&HistoricalAwards=false
• 关键词: CRII; CNS; Blockchain; based; Distributed

Facilitated by multiple sensors on mobile devices, mobile crowd sensing (MCS) relies on the mobility and characteristics of mobile users to perceive the physical world in real time, inspiring massive innovative services. Despite its prevailing deployment and great potential, conventional MCS devices transmit all sensing data to the requestors, overburdening them with high communication and computation resource consumption. This becomes even worse in practice since redundant workers are recruited for quality consideration, thus offsetting the major advantage of economical monitoring and frustrating resource-constrained requestors. This project seeks to integrate sensing and learning for MCS without the consumption of excessive resources. Specifically, given that sensing data is being collected through dispersed edge servers, blockchain-based federated learning (FL) is introduced to protect data privacy and achieve distributed machine learning (ML) with performance enhancement of trustworthiness and efficiency. The technical contributions of this research include the extension of trust from on-chain to off-chain procedures via incentive mechanism designs for eliciting trustworthy submissions from distributed edge learners. It also aims to establish instantly reliable computing environments in an off-chain manner for guaranteed efficiency of distributed ML in MCS, with both the intra-environment consensus protocol design and inter-environment interaction analysis. The research outcome of this project will contribute to improving the availability and cost-efficiency of MCS, making the perception of the physical world more economical and intelligent. The main technology of blockchain-based distributed ML can benefit society by enhancing applications involving temporal-spatial data collection and calculation. Success of this project will enhance things such as internet of things (IoT), smart cities, wireless networking, and more. The research will be closely integrated into the education and training of students while advancing curriculum development with new theories and methodologies. This project plans to inspire future generations of diverse researchers to join science and engineering. Rapid dissemination of research findings will be realized through publications to top conferences and journals. All designs will be publicly available on the PIs website for broad adoption and future research advances.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

移动的人群感知（MCS）利用移动的设备上的多个传感器，依靠移动的用户的移动性和特性，以真实的时间感知物理世界，激发了大量的创新服务。 尽管其普遍部署和巨大的潜力，常规MCS设备将所有感测数据传输到请求者，使其负担过重，具有高通信和计算资源消耗。这在实践中变得更糟，因为出于质量考虑，招聘了多余的工人，从而抵消了经济监控的主要优势，并使资源有限的请求者感到沮丧。该项目力求在不消耗过多资源的情况下，将监控监的感知和学习结合起来。具体来说，鉴于传感数据是通过分散的边缘服务器收集的，因此引入了基于区块链的联邦学习（FL）来保护数据隐私，并实现分布式机器学习（ML），提高了可信度和效率。这项研究的技术贡献包括通过激励机制设计将信任从链上扩展到链下过程，以从分布式边缘学习器中获得值得信赖的提交。 它还旨在通过环境内共识协议设计和环境间交互分析，以链下方式建立即时可靠的计算环境，以保证MCS中分布式ML的效率。该项目的研究成果将有助于提高MCS的可用性和成本效益，使物理世界的感知更加经济和智能。基于区块链的分布式ML的主要技术可以通过增强涉及时空数据收集和计算的应用程序来造福社会。 该项目的成功将增强物联网（IoT），智能城市，无线网络等。该研究将与学生的教育和培训紧密结合，同时以新的理论和方法推进课程开发。该项目计划激励未来几代不同的研究人员加入科学和工程。研究成果将通过向顶级会议和期刊发表来迅速传播。所有的设计都将在PI网站上公开，以供广泛采用和未来的研究进展。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Blockchain-based Edge Resource Sharing for Metaverse

• DOI: 10.1109/mass56207.2022.00092
• 发表时间: 2022-08
• 期刊: 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
• 作者: Zhilin Wang;Qin Hu;Minghui Xu;Honglu Jiang

Incentive Mechanism Design for Joint Resource Allocation in Blockchain-Based Federated Learning

• DOI: 10.1109/tpds.2023.3253604
• 发表时间: 2022-02
• 期刊: IEEE Transactions on Parallel and Distributed Systems
• 影响因子: 5.3
• 作者: Zhilin Wang;Qin Hu;Ruinian Li;Minghui Xu;Zehui Xiong

Online-Learning-Based Fast-Convergent and Energy-Efficient Device Selection in Federated Edge Learning

• DOI: 10.1109/jiot.2022.3222234
• 发表时间: 2023-03-15
• 期刊: IEEE INTERNET OF THINGS JOURNAL
• 影响因子: 10.6
• 作者: Peng, Cheng;Hu, Qin;Xiong, Zehui
• 通讯作者: Xiong, Zehui

Solving the Federated Edge Learning Participation Dilemma: A Truthful and Correlated Perspective

• DOI: 10.1109/tvt.2022.3161099
• 发表时间: 2022-02
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Qin Hu;Feng Li;X. Zou;Yinhao Xiao

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing

• DOI: 10.1109/jiot.2021.3128155
• 发表时间: 2021-10
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Qin Hu;Zhilin Wang;Minghui Xu;Xiuzhen Cheng