Our Application in a Box (AnB) project, presented at SMARTCOMP 2023, simplifies the deployment of remote 5G applications. AnB includes pre-configured hardware and software, allowing quick setup without extensive technical knowledge. It features automated resource management for optimized performance, demonstrating real-world applications and significantly reducing deployment time from months to minutes.
Our Elixir is a system that enhances video analytics performance from multiple IoT cameras by adjusting camera settings through Multi-Objective Reinforcement Learning (MORL). In tests, Elixir significantly outperformed default settings and time-sharing methods, improving detection rates for cars, faces, persons, and license plates, addressing challenges in multi-analytical unit settings.
At the 9th International Conference on Internet of Things: Systems, Management and Security (IOTSMS 2022), we presented a novel reinforcement-learning system called APT, which dynamically adjusts camera parameters remotely via 5G networks. Experiments showed a significant accuracy improvement in video analytics, particularly a 42% enhancement in object detection, demonstrating APT's applicability across various tasks.
In The 21st IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021), Melbourne, Victoria, Australia Centralized cloud computing with 100+ milliseconds network latencies cannot meet the tens of milliseconds to sub- millisecond response times required for emerging 5G applications like autonomous driving, smart manufacturing, tactile internet, and augmented or virtual reality. We describe … Continue reading ECO: Edge-Cloud Optimization of 5G applications
Giuseppe Coviello 