Work

When Computing Follows Vehicles: Decentralized Mobility-Aware Resource Allocation for Edge-to-Cloud Continuum

Year: 2025

Type: article

Abstract: The transformation of smart mobility is unprecedented—Autonomous, shared and electric-connected vehicles, along with the urgent need to meet ambitious net-zero targets by shifting to low-carbon transport modalities result in new traffic patterns and requirements for real-time computation at large-scale, for instance, self-driving cars and augmented reality applications. The cloud computing paradigm can neither respond to such low-latency requirements nor adapt resource allocation to such dynamic spatiotemporal service requests. This article addresses this grand challenge by introducing a novel decentralized optimization framework for mobility-aware edge-to-cloud resource allocation, service offloading, provisioning, and load-balancing. In contrast to related work, this framework comes with superior efficiency and cost-effectiveness under evaluation in real-world traffic settings and mobility datasets. This breakthrough capability of “computing follows vehicles” proves to be superior to balance resource utilization in a highly cost-effective way, while preventing service deadline violations by 14%–34%.

Source: IEEE Internet of Things Journal

DOI: 10.1109/jiot.2025.3552504

Authors: Zeinab Nezami, Emmanouil Manos Chaniotakis, Evangelos Pournaras

Institutions: University of Leeds, Energy Institute, University College London

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: green

Funder: UK Research and Innovation