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Computational resources distributed at the edge of the network are the fundamental infrastructural component of edge computing. The operational scale of edge computing introduces new challenges for building and operating suitable computation platforms. Many application scenarios require edge computing resources to provide reliable response times while operating in dynamic and resource-constrained environments. In the course of the CPS/IoT Ecosystem, we have built several testbeds and demonstrators with this type of post-cloud-computing infrastructure. Here are four selected testbeds:
* Serverless Edge Computing: to investigate how well the serverless computing model works for edge computing infrastructure, we have built several cohesive edge infrastructure units ("multi-purpose edge computers") with various computing capabilities. These units are networked within our Lab, and Kubernetes with a custom scheduler is deployed.
* Cognitive assistance applications using wearable AR devices: multi-user cognitive assistance applications require (1) real-time sensor data from the environment, such as objects moving through the space, and (2) computing resources for low-latency AI-based video processing. We built a testbed using depth cameras, a Microsoft HoloLens 2, and several edge computers with AI accelerators.
* Privacy mechanisms for AI-based video-analytics: The widespread deployment of cameras and AI-based video analytics in public areas are a cause for growing privacy and security concerns. We have built a testebd for evaluating a system for privacy-preserving AI-assisted video analytics, that extracts relevant information from video data and governs the secure access to that information.
* Indoor spatial localization and positioning: A low-cost, medium-precision indoor positioning system that does not require line-of-sight was built using the Decawave platform. We acquired 8 x DWM1001-DEV anchors and 35 x DWM1001 tags, and several Raspberry PIs and Arduinos for building a testbed. Moreover, we developed a Unity application that models the environment and can track objects in the cyber-physical space.
Methods & Expertise for Research Infrastructure
* Design of distributed systems
* Integration of Internet of Things with edge computing infrastructure
* Systems engineering
* Performing empirical experiments (e.g., profiling or benchmarks)
* Rausch, T., Avasalcai, C., & Dustdar, S. (2018). Portable energy-aware cluster-based edge computers. In 2018 IEEE/ACM Symposium on Edge Computing (SEC) (pp. 260-272). IEEE.
* Rausch, T., Raith, P., Pillai, P., & Dustdar, S. (2019). A system for operating energy-aware cloudlets. In Proceedings of the 4th ACM/IEEE Symposium on Edge Computing (pp. 307-309).
* Rausch, T., Rashed, A., & Dustdar, S. (2021). Optimized container scheduling for data-intensive serverless edge computing. Future Generation Computer Systems, 114, 259-271.
* Lachner, C., Rausch, T., & Dustdar, S. (2021). A Privacy Preserving System for AI-assisted Video Analytics. In 2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC).
* Rausch, T., Hummer, W., Stippel, C., Vasiljevic, S., Elvezio, C., Dustdar, S., & Krösl, K. (2021). Towards a Platform for Smart City-Scale Cognitive Assistance Applications. In 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) (pp. 330-335). IEEE.