New Conference Paper Published

We are excited to announce the publication of our latest conference paper, titled “Controllers for Edge-Cloud Cyber-Physical Systems”, in the proceedings of the 2025 International Conference on Communication Systems and Networks (COMSNETS).

Abstract

Deep Neural Networks (DNNs) are now widely used in Cyber-Physical Systems (CPSs), both for sensor data or perception processing and also as neural network controllers. However, resource constraints often prevent a full local deployment of the DNNs, e.g., on edge devices. Implementing them on the cloud is associated with large delays and large volumes of data transfers. This has resulted in the emergence of Split Computing (SC), where a part of the DNN is implemented on an edge device and the rest in the cloud.

However, how to design control strategies with such a setup has not been sufficiently investigated in the past. In this paper, we study controller design strategies where state estimates from sensor data processed on an edge device are combined with estimates obtained from the cloud. While the former is associated with low delays, the state estimates have higher errors or uncertainties. The estimates from the cloud, obtained with larger DNNs, are, however, delayed.

We show that the problem of sizing the DNNs on the edge and the cloud can be formulated as an optimization problem with the goal of maximizing system safety.

Details

• Title: Controllers for Edge-Cloud Cyber-Physical Systems
• Authors: Tingan Zhu, Prateek Ganguli, Arkaprava Gupta, Shengjie Xu, Luigi Capogrosso, Enrico Fraccaroli, Marco Cristani, Franco Fummi, Samarjit Chakraborty
• Conference: 17th International Conference on Communication Systems and Networks (COMSNETS)
• Year: 2025
• Pages: 198-206
• Keywords: Hands, Cloud computing, Uncertainty, Process control, Artificial neural networks, Cyber-physical systems, Data transfer, Delays, Safety, Optimization, Split Computing, Early Exit, Deep Neural Networks

Links

• DOI: 10.1109/COMSNETS63942.2025.10885702
• Open Access Version: Read Here

This paper explores controller design strategies for edge-cloud Cyber-Physical Systems (CPSs) by leveraging Split Computing (SC) to balance computation between edge devices and the cloud. The research investigates trade-offs between low-latency but noisy state estimates from edge devices and delayed but more accurate estimates from cloud-based models. The proposed optimization framework aims to maximize system safety while efficiently allocating computational resources.

We extend our gratitude to all collaborators and contributors for their efforts. For further details, please explore the links provided above.