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Intelligent and Sustainable Aerial-Terrestrial IoT Networks (INITIATE)

The ongoing concern of climate change presents challenges for the future. Environmental disasters are expected to endanger millions of lives, causing significant economic damage. To address these challenges, reliable environmental monitoring is required to reduce the effects of these disasters. Aerial network and the Internet-of-Things (IoT) are promising technologies for environmental monitoring, but both present limitations. The INITIATE project will develop a new generation of intelligent, sustainable and ultrareliable air-to-ground IoT networks to address climate change and environmental disasters, through research, innovation, and knowledge sharing on cutting-edge technologies to shape future IoT networks.

Project Aim

The INITIATE project aims to create and proliferate a long-term international, multidisciplinary and cross-sector platform to research and develop a new generation of intelligent, sustainable and ultra-reliable aerial-terrestrial IoT networks. Through strengthened collaborative research, the INITIATE project will significantly contribute to the European competitiveness and leadership in the key sectors such as Information and Communication Technologies (ICT), remote sensing, and environment monitoring with scientific breakthroughs on IoT, AI, aerial networks, wireless power transfer, software-defined networking, and network resource optimisation. The staff members will be exposed to internationally leading research environments, and trained by participating in extensive R&I research and well-planned networking activities at both Europe and global levels, which will enrich their skill sets and have their career perspectives enhanced for improved employability.

Specific Research Objectives

WP No. WP Title Start Month End Month
1 Project Planning and Management, Knowledge Sharing, and Impact Activities 1 48
2 Requirement Analysis and System Architecture Design 1 24
3 Simultaneous and Efficient Wireless Power and Information Transfer 6 42
4 Light-Weight AI Models and Online Learning Approaches 1 30
5 Ultra-Reliable and AI-Driven Autonomous Network Management 6 42
6 System Integration, Test, Demonstration, and Evaluation 24 48