Edge computing is a transformative technology that brings computational power closer to the network’s edge, enabling low-latency data processing for end-user scenarios like Smart Mobility, Smart Homes, and Smart Cities. With billions of distributed, network-enabled smart objects—such as IoT devices, smart cars, and mobile phones—generating massive data streams, the traditional cloud computing paradigm struggles to handle this efficiently due to long latency between data providers, analysis processes, and service consumers. For instance, smart video analytics, one of edge computing’s most impactful applications, enables real-time processing of video streams from IoT cameras, which is crucial for services like traffic monitoring. However, when environmental changes affect service quality, quick detection and reconfiguration are essential—something cloud-based analysis cannot provide without introducing significant delays. As the proliferation of embedded devices and smartphones continues, these challenges become even more pressing. Our research addresses these issues by developing innovative, edge-based solutions that ensure responsive, context-aware services with enhanced performance and reliability.
Context-Aware & Intelligent Systems For Edge Computing
What are we researching?
Why is this research meaningful?
By focusing on the realization of complicated distributed large scale systems in practice, this research is inherently important for many applications in our lives. More and more digital services are incorporated into our everyday lives:
- Autonomous Cars or Intelligent Mobility Systems
- Health Monitoring and Healthcare Applications at Home
- Chat-Bots and Smart Assistants
With Edge Computing we can preserve privacy and security of sensitive data, reduce the latency through real-time processing, and improve the bandwidth efficiency.
