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Leveraging Green IoT to Enhance Energy-Saving Efficiency in Fairness-Oriented Residential Photovoltaic Charging Stations
Corresponding Author(s) : Syarifah Muthia Putri
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 1, February 2026
Abstract
As electric vehicles (EVs) continue to gain global popularity, residential photovoltaic (PV) charging stations are becoming more common, providing a sustainable way to charge EVs. However, the intermittent nature of solar energy creates challenges in ensuring consistent and fair charging, making fairness-based charging scheduling essential. To automate this process, residential PV charging stations require a customized Internet of Things (IoT) system. A significant concern is the substantial energy consumption due to the high volume of data transmission within the IoT system. This research aims to enhance energy efficiency by leveraging green IoT strategies suitable for such applications. The study proposes the use of edge computing, optimized data transmission scheduling, and delta compression techniques at the edge to minimize energy use. The results demonstrate that these strategies are effective in achieving energy savings. Energy-saving efficiency on the source side ranges from 1.96% to 7.84%, while on the load side, it ranges from 57.5% to 61.3%. These findings highlight the effectiveness of the proposed strategies in reducing energy consumption, providing an efficient solution for optimizing data transmission in residential PV charging stations. Overall, the strategies contribute to the sustainable operation of electric vehicle charging infrastructure by improving energy efficiency and ensuring fair distribution of charging resources.
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- S. M. Putri, M. Ashari, Endroyono, and H. Suryoatmojo, “Design of a Smart Distribution Strategy for a Residential Electric Vehicle Charging System Fully Powered by Photovoltaics Under Intermittent Conditions,” International Review Electrical Engineering, vol. 20, no. 1, pp. 69–79, 2025, doi: 10.15866/iree.v20i1.26264.
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- D. Piątkowski, T. Puślecki, and K. Walkowiak, “Study of the Impact of Data Compression on the Energy Consumption Required for Data Transmission in a Microcontroller-Based System,” Sensors, vol. 24, no. 1, 2024, doi: 10.3390/s24010224.
- W. B. Heinzelman, A. P. Chandrakasan, and S. Member, “An Application-Specific Protocol Architecture for Wireless Microsensor Networks,” IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, vol. 1, no. 4, 2002, doi: 10.1109/TWC.2002.804190.
- M. Eduardo, R. Angeles, I. Yolanda, and O. Flores, “Tools for the selection of the transmission probability in the cluster formation phase for Event-Driven Wireless Sensor Networks,” pp. 101–110, 2014, doi:10.17533/udea.redin.15731.
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References
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M. Harizaj, I. Bisha, and F. Basholli, “IOT integration of electric vehicle charging infrastructure,” 6 th Advanced Engineering Days, vol. 2, no. 2, pp. 136–145, 2023, ISBN: 978-605-72800-2-2.
S. S. S. R.A.Sanadi, Gauri M.Patil, Rutuja M. Patil, Anjali P. Sankpal, “IOT enabled smart charging stations for electric vehicle,” International Journal Scientific Research & Engineering Trends, vol. 119, no. 7, pp. 247–252, 2022, ISSN (Online): 2395-566X
K.S. Phadtare, “A Review on IoT based Electric Vehicle Charging and Parking System,” International Journal of Engineering Research and Technology, vol. V9, no. 08, pp. 831–835, 2020, doi: 10.17577/ijertv9is080361.
H. M. Al-Alwash, E. Borcoci, M. C. Vochin, I. A. M. Balapuwaduge, and F. Y. Li, “Optimization Schedule Schemes for Charging Electric Vehicles: Overview, Challenges, and Solutions,” IEEE Access, vol. 12, no. January, pp. 32801–32818, 2024, doi: 10.1109/ACCESS.2024.3371890.
Y. Cao, “Online Routing and Charging Schedule of Electric Vehicles with Uninterrupted Charging Rates,” IEEE Access, vol. 10, no. August, pp. 98572–98583, 2022, doi: 10.1109/ACCESS.2022.3206789.
M. U. Saleem, M. R. Usman, M. A. Usman, and C. Politis, “Design, Deployment and Performance Evaluation of an IoT Based Smart Energy Management System for Demand Side Management in Smart Grid,” IEEE Access, vol. 10, pp. 15261–15278, 2022, doi: 10.1109/ACCESS.2022.3147484.
M. M. Alenazi, “IoT and Energy,” Internet Things - New Insights, 2024, doi: 10.5772/intechopen.113173.
E. F. Orumwense and K. Abo-Al-Ez, “Internet of Things for smart energy systems: A review on its applications, challenges and future trends,” AIMS Electronics and Electrical Engineering, vol. 7, no. 1, pp. 50–74, 2022, doi: 10.3934/electreng.2023004.
G. Bedi, G. K. Venayagamoorthy, R. Singh, R. R. Brooks, and K. C. Wang, “Review of Internet of Things (IoT) in Electric Power and Energy Systems,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 847–870, 2018, doi: 10.1109/JIOT.2018.2802704.
N. S. Madhuri, K. Shailaja, D. Saha, R. P, K. B. Glory, and M. Sumithra, “IOT integrated smart grid management system for effective energy management,” Measurement: Sensors, vol. 24, no. September, p. 100488, 2022, doi: 10.1016/j.measen.2022.100488.
S. Divyapriya, Amutha, and R. Vijayakumar, “Design of Residential Plug-in Electric Vehicle Charging Station with Time of Use Tariff and IoT Technology,” in ICSNS 2018 - Proceedings of IEEE International Conference on Soft-Computing and Network Security, 2018, pp. 5–9. doi: 10.1109/ICSNS.2018.8573637.
H. You and H. Tian, “Application of IoT Technology in Power Safety Management System Architecture,” IEEE 5th International Conference on Information Systems and Computer Aided Education (ICISCAE), pp. 149–153, 2022, doi: 10.1109/ICISCAE55891.2022.9927583.
J. Azar, A. Makhoul, M. Barhamgi, and R. Couturier, “An energy efficient IoT data compression approach for edge machine learning,” Future Generation Computer Systems, vol. 96, pp. 168–175, 2019, doi: 10.1016/j.future.2019.02.005.
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S. Benhamaid, A. Bouabdallah, and H. Lakhlef, “Recent advances in energy management for Green-IoT: An up-to-date and comprehensive survey,” Journal of Network and Computer Applications, vol. 198, no. November 2021, p. 103257, 2022, doi: 10.1016/j.jnca.2021.103257.
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M. A. Albreem, A. M. Sheikh, M. H. Alsharif, M. Jusoh, and M. N. Mohd Yasin, “Green Internet of Things (GIoT): Applications, Practices, Awareness, and Challenges,” IEEE Access, vol. 9, pp. 38833–38858, 2021, doi: 10.1109/ACCESS.2021.3061697.
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B. Memić, A. Hasković Džubur, and E. Avdagić-Golub, “Green IoT: sustainability environment and technologies,” Science, Engineering and Technology, vol. 2, no. 1, pp. 24–29, 2022, doi: 10.54327/set2022/v2.i1.25.
O. Said, “EMS : An Energy Management Scheme for Green IoT Environments,” IEEE Access, vol. 8, pp. 44983–44998, 2020, doi: 10.1109/ACCESS.2020.2976641.
Y. Zhang, H. Jiang, M. Shi, C. Wang, N. Jiang, and X. Wu, “Applying Delta Compression to Packed Datasets for Efficient Data Reduction,” IEEE Transactions on Computers, vol. 73, no. 1, pp. 73–85, 2024, doi: 10.1109/ICCD53106.2021.00078.
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S. N. Gowda, A. Ahmadian, V. Anantharaman, C. C. Chu, and R. Gadh, “Power Management via Integration of Battery Energy Storage Systems with Electric Bus Charging,” in 2022 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2022, 2022. doi: 10.1109/ISGT50606.2022.9817511.
D. Piątkowski, T. Puślecki, and K. Walkowiak, “Study of the Impact of Data Compression on the Energy Consumption Required for Data Transmission in a Microcontroller-Based System,” Sensors, vol. 24, no. 1, 2024, doi: 10.3390/s24010224.
W. B. Heinzelman, A. P. Chandrakasan, and S. Member, “An Application-Specific Protocol Architecture for Wireless Microsensor Networks,” IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, vol. 1, no. 4, 2002, doi: 10.1109/TWC.2002.804190.
M. Eduardo, R. Angeles, I. Yolanda, and O. Flores, “Tools for the selection of the transmission probability in the cluster formation phase for Event-Driven Wireless Sensor Networks,” pp. 101–110, 2014, doi:10.17533/udea.redin.15731.
Y. Li, A. C. Orgerie, I. Rodero, B. L. Amersho, M. Parashar, and J. M. Menaud, “End-to-end energy models for Edge Cloud-based IoT platforms: Application to data stream analysis in IoT,” Future Generation Computer Systems, vol. 87, pp. 667–678, 2018, doi: 10.1016/j.future.2017.12.048.