Green, sustainable, and energy-efficient system for transportation applications in IoT Edge-cloud networks
File(s)
Published version (article in press)
Author(s)
Khuwuthyakorn, Pattaraporn
Lakhan, Abdullah
Majumdar, Arnab
Thinnukool, Orawit
Type
Journal Article
Abstract
In recent years, the concepts of sustainability and green computing have gained significant attention, particularly in the context of smart cities and their various transportation applications. The primary goal is to shift transportation from fuel-based systems to electric alternatives, reducing overall CO2 emissions. Motivated by this objective, this paper proposes a Green, Sustainable, and Energy-Efficient System for Transportation Applications in IoT Edge Cloud Networks. The focus is on designing an IoT edge cloud infrastructure to support sustainable and green transportation within smart cities. The system addresses various transportation-related tasks, including energy consumption monitoring, traffic and object detection, and optimal route planning, all while leveraging green edge cloud networks. To optimize performance, we propose a workload partitioning method based on a min-cut scheme that categorizes tasks into IoT-local, edge, and cloud-based workloads. This partitioning aims to reduce computational energy consumption and lower CO2 emissions, fostering a more eco-friendly environment. Additionally, we introduce the Energy-Efficient Application Partitioning and Task Scheduling (EAPTS) scheme, which efficiently divides and schedules tasks across different nodes. To validate the system, we implemented testbeds based on Oslo’s public transport scenario, used training data from the given dataset, and developed a simulator for a green, sustainable transport environment. Simulation results demonstrate that the proposed system effectively reduces CO2 emissions, energy consumption, and execution time for all operational tasks.
Date Issued
2026-07-23
Date Acceptance
2026-05-11
Citation
Scientific Reports, 2026
ISSN
2045-2322
Publisher
Nature Portfolio
Journal / Book Title
Scientific Reports
Copyright Statement
© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Publication Status
Published online
Date Publish Online
2026-07-23