Research Projects
NTNU
PI: Xiang Su (Associate Professor, Department of Computer Science)
- 2024 – 2028 Nordic University Cooperation on Edge Intelligence, Nordforsk, PI. (with University of Helsinki, KTH Royal Institute of Technology, and Aarhus University)
- 2024 – 2027 Enhanced Industrial IoT Connectivity with Edge Intelligence, NTNU internal grant, PI.
- 2023 – 2026 Socially-Aware Artificial Intelligence for Future Transportation, DTU-NTNU double degree position under Nordic Five Tech program, Co-PI. (with Francisco C. Pereira, Rico Krueger, and Yu Xiao)
KTH
PI: Prof. James Gross and Asst. Prof. Jaya Prakash Champati
- Project: ‘Hierarchical Inference’ and “Hierarchical Inference (HI) refers to a method within edge computing systems that conducts inference across multiple levels of a processing chain with varying cost metrics such as latency, accuracy and energy use. The research on HI is primarily carried out by researchers from KTH Royal Institute of Technology and IMDEA Networks Institute
Aarhus University
PI: Qi Zhang (Associate Professor, Department of Electrical and Computer Engineering - Communication, Control and Automation)
- TOAST: Touch-enabled Tactile Internet Training Network and Open Source Testbed (GA No:101073465)
Funding source: Horizon Europe Marie Sklodowska-Curie Doctoral Network
Start/End Dates: 3.2023 – 2.2027
TOAST project aims to train the next generation of doctoral candidates to tackle the critical challenges of Tactile Internet through interdisciplinary research. The research activities at AU are mainly to design Edge Intelligence Engine to enhance reliability and agility for time-critical Tactile Internet services. - PANDORA: A Comprehensive Framework enabling the Delivery of Trustworthy Datasets for Efficient AIoT Operation (GA No: 101135775)
Funding source: HORIZON Europe-RIA
Start/End Dates: 4.2024 – 3.2027
PANDORA project aims at designing techniques to promote robustness, efficiency and continual operation of Artificial Intelligence of Things systems in IoT-Edge-Cloud continuum that requires realistic and trustworthy data at scale. - AgilE-IoT – Agile Edge Intelligence for Delay Sensitive IoT (Grant No. 9131-00119B)
Funding source: Danmarks Frie Forskningsfond (DFF) – thematic call “Digital technologies”
Start/End Dates: 6.2020 -10.2024
Project Agile-IoT aims to develop an agile MEC framework integrating ML models of adjustable inference time by co-design of communication, computing and ML model. It enables collaborative inference of IoT devices and edge servers to maximize the inference accuracy within a delay constraint. - Light-IoT – Analytics Straight on Compressed IoT Data (Grant No. 0136-00376B)
Funding source: Danmarks Frie Forskningsfond (DFF)
Start/End Dates: 12.2020 – 6.2025
Light-IoT project aims to develop an end-to-end IoT framework that can perform a wide range of data analytics on compressed data by relying on a new compression technique pioneered by our team at AU. We will jointly design data compression, store and analytics to optimize the overall cost of IoT ecosystem.