Green, Sustainable, and Energy-Efficient System for Transportation Applications in IoT Edge-Cloud Networks
- 1Innovative Research and Computational Science Lab, College of Arts, Media and Technology, Chiang Mai University
- 2School of Economics, Innovations and Technology, Kristiania University College
- 3Transport Risk Management Centre, Imperial College London
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.
Related
- Route optimization for urban hazardous materials transportation under time-varying road networks
- Persimmon By-Products: Bioactive Phytochemicals, Sustainable Valorization and Potential Food and Health Applications
- Food-Grade By-Products as Sustainable Sources of Phytochemicals: Green Extraction Strategies, Encapsulation Technologies, and Applications in Functional Food Development
- Integrated processing of agro-industrial by-products into functional bioactive ingredients for sustainable food and health applications
- A hybrid predictive and uncertainty-aware framework for renewable hydrogen system efficiency assessment and sustainable energy planning
BibTeX
@article{khuwuthyakorn2026green,
title = {{Green, Sustainable, and Energy-Efficient System for Transportation Applications in IoT Edge-Cloud Networks}},
author = {Pattaraporn Khuwuthyakorn and Abdullah Lakhan and Arnab Majumdar and Orawit Thinnukool},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-53194-z},
url = {https://doi.org/10.1038/s41598-026-53194-z},
note = {CC BY},
}
RIS
TY - JOUR
TI - Green, Sustainable, and Energy-Efficient System for Transportation Applications in IoT Edge-Cloud Networks
AU - Pattaraporn Khuwuthyakorn
AU - Abdullah Lakhan
AU - Arnab Majumdar
AU - Orawit Thinnukool
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-53194-z
UR - https://doi.org/10.1038/s41598-026-53194-z
AB - 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.
SN - 2045-2322
ER -