Labs
EDGE has several labs in which we explore interdisciplinary projects that address broader challenges at the intersection of edge computing, AI, IoT, and sustainability. The EDGE Center for networked systems and intelligence is using these labs to get practical insight in the technology of the EDGE centre, while considering environmental impact and security, making it a valuable hub for cutting-edge research and innovation.
- Sustainability Lab - Promoting environmentally responsible practices in ICT, edge computing and IoT to minimize ecological impact and foster sustainable technological growth.
- Green Edge Computing: Researching energy-efficient edge computing architectures and algorithms to reduce carbon footprint.
- IoT for Environmental Monitoring: Using IoT sensors and data analytics for environmental monitoring, such as air quality and water quality assessments.
- Circular Economy: Exploring sustainable practices in electronics manufacturing, including recycling and reuse of components.
- IoT security Lab - Ensuring the security and privacy of IoT ecosystems through research, development, and testing of robust cybersecurity solutions.
- Cybersecurity for IoT Devices: Analyzing and enhancing the security of Internet of Things (IoT) devices and protocols to protect against cyberattacks.
- Secure Communication: Developing secure communication protocols and encryption techniques for IoT networks and edge computing environments.
- IoT Threat Detection: Building intrusion detection systems and anomaly detection algorithms to identify malicious activities within IoT ecosystems.
- Security Testing: Conducting penetration testing and vulnerability assessments of IoT devices and networks.
- Embedded AI Lab - Advancing edge computing by developing hardware and algorithms that enable efficient and intelligent processing on resource-constrained devices.
- AI Hardware Development: Designing and optimizing hardware accelerators for edge devices to improve AI model inference speed and efficiency.
- Embedded AI Applications: Developing AI-powered applications for embedded systems, such as smart cameras, drones, and robotics.
- Edge AI Algorithms: Researching and implementing machine learning and deep learning algorithms tailored for resource-constrained edge devices.
- Low-Power AI: Exploring energy-efficient AI techniques for edge devices to prolong battery life and reduce power consumption.
Teams
- Computer Architecture for Embedded Systems
- Design and Analysis of Communication Systems
- Pervasive Systems
Projects
- 6G FNS
- MISD
Calendar
- 2023, September 6th: EDGE internal kick-off