TriCloudEdge: A multi-layer Cloud Continuum

2026-02-02

AI Summary

This work addresses the challenge of simultaneously balancing computation, latency, and privacy in cloud-edge-end协同 systems by proposing a scalable three-tier unified architecture that integrates remote microcontrollers, edge nodes, and the central cloud to enable efficient cross-layer AI task distribution. The architecture optimizes the trade-off between resource utilization and communication efficiency through a comparative evaluation of multiple protocols—including WebSocket, MQTT, HTTP, and Zenoh—and incorporates federated learning, model adaptation, and global identity management. Experimental results demonstrate that the proposed framework achieves on-device model adaptation at the remote tier, significantly reducing both latency and privacy risks, thereby validating its advantages in computational and communication efficiency.

Technology Category

Machine Learning: Learning on the Edge & Model CompressionCognitive Modeling & Cognitive Systems: Agent ArchitecturesMultiagent Systems: Coordination and Collaboration

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processingSecurity and Privacy: Browser and end-point securityUser Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

TriCloudEdge is a scalable three-tier cloud continuum that integrates far-edge devices, intermediate edge nodes, and central cloud services, working in parallel as a unified solution. At the far edge, ultra-low-cost microcontrollers can handle lightweight AI tasks, while intermediate edge devices provide local intelligence, and the cloud tier offers large-scale analytics, federated learning, model adaptation, and global identity management. The proposed architecture enables multi-protocols and technologies (WebSocket, MQTT, HTTP) compared to a versatile protocol (Zenoh) to transfer diverse bidirectional data across the tiers, offering a balance between computational challenges and latency requirements. Comparative implementations between these two architectures demonstrate the trade-offs between resource utilization and communication efficiency. The results show that TriCloudEdge can distribute computational challenges to address latency and privacy concerns. The work also presents tests of AI model adaptation on the far edge and the computational effort challenges under the prism of parallelism. This work offers a perspective on the practical continuum challenges of implementation aligned with recent research advances addressing challenges across the different cloud levels.

Problem

• cloud continuum
• edge computing
• latency
• privacy
• resource utilization

Innovation

• TriCloudEdge
• cloud continuum
• edge computing
• federated learning
• multi-protocol communication