Internet-of-vehicles network for CO₂ emission estimation and reinforcement learning-based emission reduction
Devi, Archana Sulekha and Britto, Milagres Mary John and Fang, Zian and Gopan, Renjith and Jassal, Pawan Singh and Qazzaz, Mohammed M. H. and Rajbhandari, Sujan and Al-Sallami, Farah Mahdi (2024) Internet-of-vehicles network for CO₂ emission estimation and reinforcement learning-based emission reduction. IEEE Access, 12. pp. 110681-110690. ISSN 2169-3536 (https://doi.org/10.1109/ACCESS.2024.3441949)
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Abstract
The escalating impact of vehicular Carbon Dioxide (CO2) emissions on air pollution, global warming, and climate change necessitates innovative solutions. This paper proposes a comprehensive Internet-of-Vehicles (IoV) network for real-time CO2 emissions estimation and reduction. We implemented and tested an on-board device that estimates the vehicle’s emissions and transmits the data to the network. The estimated CO2 emissions values are close to the standard emissions values of petrol and diesel vehicles, accounting for expected discrepancies due to vehicles’ age and loading. The network uses the aggregate emissions readings to inform the Reinforcement Learning (RL) algorithm, enabling the prediction of optimal speed limits to minimize vehicular emissions. The results demonstrate that employing the RL algorithm can achieve an average CO2 emissions reduction of 11 kg/h to 150 kg/h.
ORCID iDs
Devi, Archana Sulekha, Britto, Milagres Mary John, Fang, Zian, Gopan, Renjith, Jassal, Pawan Singh, Qazzaz, Mohammed M. H., Rajbhandari, Sujan ORCID: https://orcid.org/0000-0001-8742-118X and Al-Sallami, Farah Mahdi;-
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Item type: Article ID code: 90319 Dates: DateEvent20 August 2024Published4 August 2024AcceptedSubjects: Technology > Motor vehicles. Aeronautics. Astronautics
Technology > Engineering (General). Civil engineering (General) > Environmental engineering
Science > Mathematics > Electronic computers. Computer scienceDepartment: Faculty of Science > Physics > Institute of Photonics Depositing user: Pure Administrator Date deposited: 22 Aug 2024 11:42 Last modified: 01 Oct 2024 14:26 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/90319