Rural Environmental Monitoring via ultra wide-ARea networKs And distriButed federated Learning

通过超广域网和分布式联合学习进行农村环境监测

• 批准号: EP/X039021/1
• 负责人: Bamidele Adebisi
• 金额: $ 22.73万
• 依托单位: Manchester Metropolitan University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX039021%2F1
• 关键词: Rural; Environmental; Monitoring; via; ultra

Internet of Things (IoT) technology combined with complementary support for data analytics is the corner stone of today's digital transformation. The societal and economic impact of IoT/ML systems in urban and suburban areas significantly outpaces the one in rural areas due to a limited reach of connectivity infrastructure. To reverse further widening of the urban-rural gap, we need to bring efficient and affordable IoT/ML solutions to deep rural areas, reaching out to applications and use cases ranging from wildlife management, rural tourism, livestock monitoring, water and air pollution control, and others. By identifying main gaps in connectivity and affordable data analytics and through interleaved research, development and validation in a real-world setting, REMARKABLE will address the challenge of bringing IoT and data analytics systems a step closer to seamless, energy efficient and secure deployment inrural ares. The consortium composed of six European academic partners, five companies and five associated partners representing leading academic groups from Africa, will focus on research, development, innovation and demonstration across five use cases with six demonstration sites located in rural areas across the European and African continent. The inter-disciplinary nature of the programme provides a unique opportunity for investigation of smart sensing, IoT and data science technology from non-traditional, holistic perspectives leading to new scientific achievements and innovations. Project outputs like smart IoT sensors and devices, rural IoT digital twining platforms, ultra wide-area IoT networks, novel data analytics models and architectures will find their routes to the market via active industrial partners. The project will impact EU workforce market via new interdisciplinary skills for young contributors and form new long-lasting networks of European and African institutions in the area of sensing, IoT, big data analytics and rural entrepreneurship.

物联网 (IoT) 技术与数据分析的互补支持相结合，是当今数字化转型的基石。由于连接基础设施的覆盖范围有限，物联网/机器学习系统在城市和郊区的社会和经济影响明显超过农村地区。为了扭转城乡差距进一步扩大的趋势，我们需要为深入农村地区带来高效且负担得起的物联网/机器学习解决方案，涵盖野生动物管理、乡村旅游、牲畜监测、水和空气污染控制等应用和用例，以及其他。通过确定连接性和负担得起的数据分析方面的主要差距，并通过在现实环境中进行交叉研究、开发和验证，REMARKABLE 将解决使物联网和数据分析系统更接近农村地区无缝、节能和安全部署的挑战。该联盟由六个欧洲学术合作伙伴、五家公司和五个代表非洲领先学术团体的合作伙伴组成，将重点关注五个用例的研究、开发、创新和示范，六个示范点位于欧洲和非洲大陆的农村地区。该项目的跨学科性质为从非传统的整体角度研究智能传感、物联网和数据科学技术提供了独特的机会，从而产生新的科学成就和创新。智能物联网传感器和设备、农村物联网数字孪生平台、超广域物联网网络、新颖的数据分析模型和架构等项目成果将通过活跃的工业合作伙伴找到进入市场的途径。该项目将通过为年轻贡献者提供新的跨学科技能来影响欧盟劳动力市场，并在传感、物联网、大数据分析和农村创业领域形成欧洲和非洲机构的新的持久网络。

项目成果

Federated Deep Learning for Intrusion Detection in Consumer-Centric Internet of Things

用于以消费者为中心的物联网中的入侵检测的联合深度学习

• DOI: 10.1109/tce.2023.3347170
• 发表时间: 2024-02-01
• 期刊: IEEE Transactions on Consumer Electronics
• 影响因子: 4.3
• 作者: S. Popoola;A. Imoize;M. Hammoudeh;B. Adebisi;Olamide Jogunola;A. Aibinu
• 通讯作者: A. Aibinu