Ambient Intelligence to Detect Misuse of Electricity Consumption Based on IoT Using Blockchain Technology
Electricity abuse and energy inefficiencies are still open issues in smart grid systems, demanding high-performance anomaly detection mechanisms. In this paper, we propose an IoT-enabled electricity monitoring system that combines machine learning (LightGBM) and blockchain (Polygon network) for real...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10966853/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850278440669806592 |
|---|---|
| author | Soiful Hadi Wahyul Amien Syafei Adi Wibowo |
| author_facet | Soiful Hadi Wahyul Amien Syafei Adi Wibowo |
| author_sort | Soiful Hadi |
| collection | DOAJ |
| description | Electricity abuse and energy inefficiencies are still open issues in smart grid systems, demanding high-performance anomaly detection mechanisms. In this paper, we propose an IoT-enabled electricity monitoring system that combines machine learning (LightGBM) and blockchain (Polygon network) for real-time anomaly detection, secure data storage, and transparent energy tracking. IoT smart meters are utilized to monitor real-time electricity usage data, whereas LightGBM classifies anomalies efficiently with high precision. The key innovation is the use of blockchain for decentralized anomaly logging with tamper-proof records and enhanced trustworthiness. Unlike centralized approaches, Polygon blockchain immutably stores electricity data, giving verifiable anomaly logs. Using an interactive IoT dashboard and real-time notifications, users can monitor consumption patterns and respond to anomalies efficiently. The proposed system achieves 96.77% accuracy, an AUC-ROC of 0.99, and an F1-score of 96.69%, outperforming CNN-LSTM and CNN-XGBoost in both accuracy and use of computational resources. Despite these advantages, blockchain transaction costs (0.001996 POL per transaction) and IoT-wallet integration complexity pose challenges. Future work will explore cost-reducing blockchain optimizations such as meta-transactions and relayers, along with model enhancements for IoT scalability. This approach provides a low-cost, scalable, and secure solution for smart energy management, enhancing grid security and sustainability. |
| format | Article |
| id | doaj-art-30e2b4d3bf664e3f9ec1fb82a62b2d37 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-30e2b4d3bf664e3f9ec1fb82a62b2d372025-08-20T01:49:31ZengIEEEIEEE Access2169-35362025-01-0113803718039410.1109/ACCESS.2025.356167710966853Ambient Intelligence to Detect Misuse of Electricity Consumption Based on IoT Using Blockchain TechnologySoiful Hadi0https://orcid.org/0009-0002-5396-3647Wahyul Amien Syafei1Adi Wibowo2https://orcid.org/0000-0002-7966-1017Information System School of Postgraduate, Diponegoro University, Semarang, IndonesiaDepartment of Electrical Engineering, Diponegoro University, Semarang, IndonesiaDepartment of Informatics, Faculty of Science and Mathematics, Diponegoro University, Semarang, IndonesiaElectricity abuse and energy inefficiencies are still open issues in smart grid systems, demanding high-performance anomaly detection mechanisms. In this paper, we propose an IoT-enabled electricity monitoring system that combines machine learning (LightGBM) and blockchain (Polygon network) for real-time anomaly detection, secure data storage, and transparent energy tracking. IoT smart meters are utilized to monitor real-time electricity usage data, whereas LightGBM classifies anomalies efficiently with high precision. The key innovation is the use of blockchain for decentralized anomaly logging with tamper-proof records and enhanced trustworthiness. Unlike centralized approaches, Polygon blockchain immutably stores electricity data, giving verifiable anomaly logs. Using an interactive IoT dashboard and real-time notifications, users can monitor consumption patterns and respond to anomalies efficiently. The proposed system achieves 96.77% accuracy, an AUC-ROC of 0.99, and an F1-score of 96.69%, outperforming CNN-LSTM and CNN-XGBoost in both accuracy and use of computational resources. Despite these advantages, blockchain transaction costs (0.001996 POL per transaction) and IoT-wallet integration complexity pose challenges. Future work will explore cost-reducing blockchain optimizations such as meta-transactions and relayers, along with model enhancements for IoT scalability. This approach provides a low-cost, scalable, and secure solution for smart energy management, enhancing grid security and sustainability.https://ieeexplore.ieee.org/document/10966853/Electricity misuseambient intelligence (AmI)Internet of Things (IoT)blockchainsmart contract |
| spellingShingle | Soiful Hadi Wahyul Amien Syafei Adi Wibowo Ambient Intelligence to Detect Misuse of Electricity Consumption Based on IoT Using Blockchain Technology IEEE Access Electricity misuse ambient intelligence (AmI) Internet of Things (IoT) blockchain smart contract |
| title | Ambient Intelligence to Detect Misuse of Electricity Consumption Based on IoT Using Blockchain Technology |
| title_full | Ambient Intelligence to Detect Misuse of Electricity Consumption Based on IoT Using Blockchain Technology |
| title_fullStr | Ambient Intelligence to Detect Misuse of Electricity Consumption Based on IoT Using Blockchain Technology |
| title_full_unstemmed | Ambient Intelligence to Detect Misuse of Electricity Consumption Based on IoT Using Blockchain Technology |
| title_short | Ambient Intelligence to Detect Misuse of Electricity Consumption Based on IoT Using Blockchain Technology |
| title_sort | ambient intelligence to detect misuse of electricity consumption based on iot using blockchain technology |
| topic | Electricity misuse ambient intelligence (AmI) Internet of Things (IoT) blockchain smart contract |
| url | https://ieeexplore.ieee.org/document/10966853/ |
| work_keys_str_mv | AT soifulhadi ambientintelligencetodetectmisuseofelectricityconsumptionbasedoniotusingblockchaintechnology AT wahyulamiensyafei ambientintelligencetodetectmisuseofelectricityconsumptionbasedoniotusingblockchaintechnology AT adiwibowo ambientintelligencetodetectmisuseofelectricityconsumptionbasedoniotusingblockchaintechnology |