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)技术与对数据分析的补充支持相结合，是当今数字化转型的基石。由于连接基础设施覆盖范围有限，城市和郊区物联网/ML系统的社会和经济影响远远超过农村地区。为了扭转城乡差距的进一步扩大，我们需要将高效且负担得起的物联网/ML解决方案带到偏远的农村地区，覆盖从野生动物管理、乡村旅游、牲畜监测、水和空气污染控制等在内的各种应用和用例。通过识别连接和负担得起的数据分析方面的主要差距，并通过在现实世界环境中交叉进行研究、开发和验证，非凡将解决将物联网和数据分析系统向无缝、节能和安全的农村地区部署更近一步的挑战。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关联合作伙伴组成，将专注于研究、开发、创新和示范，涉及五个用例，六个示范点位于欧洲和非洲大陆的农村地区。该方案的跨学科性质为从非传统的整体角度调查智能传感、物联网和数据科学技术提供了一个独特的机会，从而导致新的科学成就和创新。智能物联网传感器和设备、农村物联网数字缠绕平台、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行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新的欧洲和非洲机构的长期网络。

项目成果

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

• DOI: 10.1109/tce.2023.3347170
• 发表时间: 2024-02
• 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