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Smart city article details
| Title | A Deep Learning Framework With Learning Without Forgetting For Intelligent Surveillance In Iot-Enabled Home Environments In Smart Cities |
|---|---|
| ID_Doc | 1356 |
| Authors | Dalal S.; Dahiya N.; Verma A.; Faujdar N.; Rathee S.; Jaglan V.; Rani U.; Le D.-N. |
| Year | 2025 |
| Published | Recent Advances in Computer Science and Communications |
| DOI | http://dx.doi.org/10.2174/0126662558329951241024183922 |
| Abstract | Background: Internet of Things (IoT) technology in smart urban homes has revolutionised sophisticated monitoring. This progress uses interconnected devices and systems to improve security, resource management, and resident safety. Smart cities use technology to improve efficiency, sustainability, and quality. Internet of Things-enabled intelligent monitoring technologies are key to this goal. Objectives: Intelligent monitoring in IoT-enabled homes in smart cities improves security, convenience, and quality of life from advanced technologies. Using live monitoring and risk identification tools to quickly discover and resolve security breaches and suspicious activity to protect citizens. Intelligent devices allow homeowners to remotely control lighting, security locks, and surveillance cameras. Using advanced technologies to regulate heating, cooling, and lighting based on occupancy and usage. Method: This study introduces a deep learning architecture that uses LwF (Learning without Forgetting) to keep patterns while absorbing new data. The authors use IoT devices to collect and analyse data in real-time for monitoring and surveillance. They use sophisticated data pre-processing to handle IoT devices' massive data. The authors train the deep learning model with historical and real-time data and cross-validation to ensure resilience. Result: The proposed model has been validated on two different Robloflow datasets of 7382 images. The proposed model gains an accuracy level of 98.27%. The proposed Yolo-LwF model outperforms both the original Yolo and LwF models in terms of detection speed and adaptive learning. Conclusion: By raising the bar for intelligent monitoring solutions in smart cities, the suggested system is ideal for real-time, adaptive surveillance in IoT-enabled households. By embracing adaptability and knowledge retention, authors envision heightened security and safety levels in urban settings. © 2024 Bentham Science Publishers. |
| Author Keywords | accuracy; Deep learning; face detection; face recognition; real-time security surveillance |
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