Universal IoT-Enabled Smart Battery Charging Optimization: Leveraging AI, Automata Theory, and Real-Time Data for Enhanced Lifespan and Efficiency
Universal IoT-Enabled Smart Battery Charging Optimization System introduces a novel solution to addressing the widespread problem of battery degradation in IoT-enabled devices such as smartphones, wearables, stylus pens, and wireless earbuds. The system utilizes Artificial Intelligence (AI), Automat...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01055.pdf |
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| _version_ | 1850211428008460288 |
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| author | Arora Divyansh Goswami Ananya Ranjan Mritunjay Sattar Arif Md |
| author_facet | Arora Divyansh Goswami Ananya Ranjan Mritunjay Sattar Arif Md |
| author_sort | Arora Divyansh |
| collection | DOAJ |
| description | Universal IoT-Enabled Smart Battery Charging Optimization System introduces a novel solution to addressing the widespread problem of battery degradation in IoT-enabled devices such as smartphones, wearables, stylus pens, and wireless earbuds. The system utilizes Artificial Intelligence (AI), Automata Theory, and sensor data in real-time to develop a dynamic context-aware charging strategy that learns from the unique usage patterns and battery status of every device. By applying machine learning models like Kalman Filters, Recurrent Neural Networks (RNNs), and Finite State Machines (FSM), the system charges behaviors smartly, optimizing power consumption, charging efficiency, and battery life. Pushdown Automata (PDA) are used to facilitate non-deterministic state transitions from past charging data, providing more precise predictions of battery wear. The system constantly collects and processes data from IoT sensors with real-time charging cycle and battery health feedback. Initial results indicate that the system greatly mitigates overcharging, enhances battery life, and promotes energy efficiency, providing a green solution to maximize the lifespan of IoT-based devices in various applications. The solution offers a sound framework for further research on smart energy management and green computing. |
| format | Article |
| id | doaj-art-11c8e7b6d7544cdd84ea4bde9c65ac66 |
| institution | OA Journals |
| issn | 2100-014X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj-art-11c8e7b6d7544cdd84ea4bde9c65ac662025-08-20T02:09:33ZengEDP SciencesEPJ Web of Conferences2100-014X2025-01-013280105510.1051/epjconf/202532801055epjconf_icetsf2025_01055Universal IoT-Enabled Smart Battery Charging Optimization: Leveraging AI, Automata Theory, and Real-Time Data for Enhanced Lifespan and EfficiencyArora Divyansh0Goswami Ananya1Ranjan Mritunjay2Sattar Arif Md3Department of Design, Delhi Technological UniversitySchool of Computer Sciences and Engineering, Sandip UniversitySchool of Computer Sciences and Engineering, Sandip UniversityDepartment of MCA, Dewan institute of management studiesUniversal IoT-Enabled Smart Battery Charging Optimization System introduces a novel solution to addressing the widespread problem of battery degradation in IoT-enabled devices such as smartphones, wearables, stylus pens, and wireless earbuds. The system utilizes Artificial Intelligence (AI), Automata Theory, and sensor data in real-time to develop a dynamic context-aware charging strategy that learns from the unique usage patterns and battery status of every device. By applying machine learning models like Kalman Filters, Recurrent Neural Networks (RNNs), and Finite State Machines (FSM), the system charges behaviors smartly, optimizing power consumption, charging efficiency, and battery life. Pushdown Automata (PDA) are used to facilitate non-deterministic state transitions from past charging data, providing more precise predictions of battery wear. The system constantly collects and processes data from IoT sensors with real-time charging cycle and battery health feedback. Initial results indicate that the system greatly mitigates overcharging, enhances battery life, and promotes energy efficiency, providing a green solution to maximize the lifespan of IoT-based devices in various applications. The solution offers a sound framework for further research on smart energy management and green computing.https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01055.pdf |
| spellingShingle | Arora Divyansh Goswami Ananya Ranjan Mritunjay Sattar Arif Md Universal IoT-Enabled Smart Battery Charging Optimization: Leveraging AI, Automata Theory, and Real-Time Data for Enhanced Lifespan and Efficiency EPJ Web of Conferences |
| title | Universal IoT-Enabled Smart Battery Charging Optimization: Leveraging AI, Automata Theory, and Real-Time Data for Enhanced Lifespan and Efficiency |
| title_full | Universal IoT-Enabled Smart Battery Charging Optimization: Leveraging AI, Automata Theory, and Real-Time Data for Enhanced Lifespan and Efficiency |
| title_fullStr | Universal IoT-Enabled Smart Battery Charging Optimization: Leveraging AI, Automata Theory, and Real-Time Data for Enhanced Lifespan and Efficiency |
| title_full_unstemmed | Universal IoT-Enabled Smart Battery Charging Optimization: Leveraging AI, Automata Theory, and Real-Time Data for Enhanced Lifespan and Efficiency |
| title_short | Universal IoT-Enabled Smart Battery Charging Optimization: Leveraging AI, Automata Theory, and Real-Time Data for Enhanced Lifespan and Efficiency |
| title_sort | universal iot enabled smart battery charging optimization leveraging ai automata theory and real time data for enhanced lifespan and efficiency |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01055.pdf |
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