Enhancing IoT-Based Environmental Monitoring and Power Forecasting: A Comparative Analysis of AI Models for Real-Time Applications
The Internet of Things (IoT) is transforming industries by integrating sensors and connectivity into everyday objects, enabling enhanced monitoring, management, and automation through Machine-to-Machine (M2M) communication. Despite these advancements, the IoT faces limitations in accurately predicti...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/24/11970 |
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| author | Md Minhazur Rahman Md Ibne Joha Md Shahriar Nazim Yeong Min Jang |
| author_facet | Md Minhazur Rahman Md Ibne Joha Md Shahriar Nazim Yeong Min Jang |
| author_sort | Md Minhazur Rahman |
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| description | The Internet of Things (IoT) is transforming industries by integrating sensors and connectivity into everyday objects, enabling enhanced monitoring, management, and automation through Machine-to-Machine (M2M) communication. Despite these advancements, the IoT faces limitations in accurately predicting environmental conditions and power consumption. This study proposes an advanced IoT platform that combines real-time data collection with secure transmission and forecasting using a hybrid Long Short-Term Memory (LSTM)–Gated Recurrent Unit (GRU) model. The hybrid architecture addresses the computational inefficiencies of LSTM and the short-term dependency challenges of GRU, achieving improved accuracy and efficiency in time-series forecasting. For all prediction use cases, the model achieves a Maximum Mean Absolute Error (MAE) of 3.78%, Root Mean Square Error (RMSE) of 8.15%, and a minimum <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> score of 82.04%, the showing proposed model’s superiority for real-life use cases. Furthermore, a comparative analysis also shows the performance of the proposed model outperforms standalone LSTM and GRU models, enhancing the IoT’s reliability in real-time environmental and power forecasting. |
| format | Article |
| id | doaj-art-465fa9f168b34a2caba0173e4f397704 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-465fa9f168b34a2caba0173e4f3977042024-12-27T14:08:57ZengMDPI AGApplied Sciences2076-34172024-12-0114241197010.3390/app142411970Enhancing IoT-Based Environmental Monitoring and Power Forecasting: A Comparative Analysis of AI Models for Real-Time ApplicationsMd Minhazur Rahman0Md Ibne Joha1Md Shahriar Nazim2Yeong Min Jang3Department of Electronics Engineering, Kookmin University, Seoul 02707, Republic of KoreaDepartment of Electronics Engineering, Kookmin University, Seoul 02707, Republic of KoreaDepartment of Electronics Engineering, Kookmin University, Seoul 02707, Republic of KoreaDepartment of Electronics Engineering, Kookmin University, Seoul 02707, Republic of KoreaThe Internet of Things (IoT) is transforming industries by integrating sensors and connectivity into everyday objects, enabling enhanced monitoring, management, and automation through Machine-to-Machine (M2M) communication. Despite these advancements, the IoT faces limitations in accurately predicting environmental conditions and power consumption. This study proposes an advanced IoT platform that combines real-time data collection with secure transmission and forecasting using a hybrid Long Short-Term Memory (LSTM)–Gated Recurrent Unit (GRU) model. The hybrid architecture addresses the computational inefficiencies of LSTM and the short-term dependency challenges of GRU, achieving improved accuracy and efficiency in time-series forecasting. For all prediction use cases, the model achieves a Maximum Mean Absolute Error (MAE) of 3.78%, Root Mean Square Error (RMSE) of 8.15%, and a minimum <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> score of 82.04%, the showing proposed model’s superiority for real-life use cases. Furthermore, a comparative analysis also shows the performance of the proposed model outperforms standalone LSTM and GRU models, enhancing the IoT’s reliability in real-time environmental and power forecasting.https://www.mdpi.com/2076-3417/14/24/11970Internet of Things (IoT)power forecastinghybrid LSTM-GRUsecure data transmissionenvironmental monitoring |
| spellingShingle | Md Minhazur Rahman Md Ibne Joha Md Shahriar Nazim Yeong Min Jang Enhancing IoT-Based Environmental Monitoring and Power Forecasting: A Comparative Analysis of AI Models for Real-Time Applications Applied Sciences Internet of Things (IoT) power forecasting hybrid LSTM-GRU secure data transmission environmental monitoring |
| title | Enhancing IoT-Based Environmental Monitoring and Power Forecasting: A Comparative Analysis of AI Models for Real-Time Applications |
| title_full | Enhancing IoT-Based Environmental Monitoring and Power Forecasting: A Comparative Analysis of AI Models for Real-Time Applications |
| title_fullStr | Enhancing IoT-Based Environmental Monitoring and Power Forecasting: A Comparative Analysis of AI Models for Real-Time Applications |
| title_full_unstemmed | Enhancing IoT-Based Environmental Monitoring and Power Forecasting: A Comparative Analysis of AI Models for Real-Time Applications |
| title_short | Enhancing IoT-Based Environmental Monitoring and Power Forecasting: A Comparative Analysis of AI Models for Real-Time Applications |
| title_sort | enhancing iot based environmental monitoring and power forecasting a comparative analysis of ai models for real time applications |
| topic | Internet of Things (IoT) power forecasting hybrid LSTM-GRU secure data transmission environmental monitoring |
| url | https://www.mdpi.com/2076-3417/14/24/11970 |
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