Edge-Oriented Orchestration of Energy Services Using Graph-Driven Swarm Intelligence

2026-04-06

AI Summary

This work addresses the challenges of decentralized, low-latency service orchestration in smart grids under the integration of IoT and distributed energy management. To this end, the authors propose a unified task orchestration framework tailored for edge–fog–cloud collaborative architectures. The framework combines graph-driven modeling with swarm intelligence–based optimization to enable resource-aware, low-latency task offloading. System interoperability is ensured through adherence to Energy Data Space standards, while blockchain technology guarantees traceability of workloads. Real-world deployment experiments based on KubeEdge demonstrate that the proposed approach achieves zero-downtime service migration and sustained service availability under dynamic workloads, significantly enhancing both system responsiveness and reliability.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

As smart grids increasingly depend on IoT devices and distributed energy management, they require decentralized, low latency orchestration of energy services. We address this with a unified framework for edge fog cloud infrastructures tailored to smart energy systems. It features a graph based data model that captures infrastructure and workload, enabling efficient topology exploration and task placement. Leveraging this model, a swarm-based heuristic algorithm handles task offloading in a resource-aware, latency sensitive manner. Our framework ensures data interoperability via energy data space compliance and guarantees traceability using blockchain based workload notarization. We validate our approach with a real-world KubeEdge deployment, demonstrating zero downtime service migration under dynamic workloads while maintaining service continuity.

Problem

• smart grids
• energy services orchestration
• edge computing
• low latency
• decentralized coordination

Innovation

• graph-driven swarm intelligence
• edge-fog-cloud orchestration
• energy data space
• blockchain notarization
• zero-downtime migration