NCPS > Research

Research

The Institute for Networked Cyber-Physical Systems (NCPS) is part of the School of Electrical Engineering, Computer Science, and Mathematics at the Hamburg University of Technology (TUHH).

Our work is driven by three trends:

sensors are everywhere and give near real-time insights in every aspect of the world,
AI is here to stay,
nearly everything gets programmable, see RISE-Lab.

We do - mainly data-driven - systems research on networked and intelligent systems. We are particularly passionate about the Internet of Things (IoT), Cyber-Physical Systems, Edge & Fog Computing, Edge AI and TinyML. We love to build systems and play with them (= run experiments and write papers about them). We release our results as open source and evaluate our work on large-scale testbeds, often with hundreds of nodes. Software releases of projects in which we were involved are published on GitHub (GitHub TUHH-NCPS, GitHub DS-Kiel, GitHub IoT Chalmers).

Currently, our Institute focuses on the following directions:

Deep Learning

            						Adaptive Machine Learning: Adaptive and flexible Deep Neural Networks 
Edge AI and TinyML: Resource-efficient and embedded ML
Distributed Machine Learning: split computing and federated learning 

        						

Internet of Things

            						Low-Power Wireless Networking: Bluetooth (BLE), ZigBee / 802.15.4, LoRa, UWB
Wireless Networking: 5G, 6G, 802.11
Resilient Internet of Things: Synchronous transmissions for resilient low-latency wireless networking in low-power wireless networks 

        						

Edge Computing

            						Distributed Computing: Distributed computing in dynamic and resource-constrained environments
Swarms of Autonomous Devices: Coordinating maneuvers, positioning and localization in dynamics and mobile environments
Process Mining: Mining of processes on distributed event sources

        						

Current Projects at Kiel University

Intelligent Underwater Monitoring, part of “Helmholtz School for Marine Data Science”

Intelligent underwater monitoring systems combining distributed underwater sensor networks with cloud-based digital twins.

Helmholtz MarData
Role: Co-PI
Year(s): 2025-2028 (3 years)
Volume: 230k Euro

An INnovative, intelligent SYSTem for coastal water monitoring using artificial intelligence (INSYST)

Distributed, locality-aware process mining and process mining on resource-constrained IoT devices.

Role: PI
Year(s): 2023-2026 (3 years)
Volume: 325k Euro

DFG Project EdgeMine, part of Research Unit: "SOURCED – Process Mining on Distributed Event Sources"

Distributed, locality-aware process mining and process mining on resource-constrained IoT devices.

Role: PI
Year(s): 2023-2027 (4 years)
Volume: about 400k Euro

CAPTN Fördeareal II (DTW II)

Developing the infrastructure for real-world autonomous sailing.

Project Website
Role: Co-PI
Year(s): 2023-2024 (2 years)
Volume: 260k Euro

Data Campus

Bringing machine learning to all fields of science.

Project Website (in German)
Role: PI
Year(s): 2021-2024 (3 years)
Volume: ca. 230k Euro (2M Euro total)

MarData Research Project (MarData 5), part of “Helmholtz School for Marine Data Science”

Automated event detection and data-analytics in distributed seafloor sensor networks.

Helmholtz MarData
Role: Co-PI
Year(s): 2021-2024 (3 years)
Volume: 230k Euro

Completed Projects

At TUHH, Kiel University, and Chalmers University of Technology (Sweden)