Intelligent IoT-Based Network Clustering and Camera Distribution Algorithm Using Reinforcement Learning
The advent of a wide variety of affordable communication devices and cameras has enabled IoT systems to provide effective solutions for a wide range of civil and military applications. One of the potential applications is a surveillance system in which several cameras collaborate to monitor a specif...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/13/1/4 |
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| author | Islam T. Almalkawi Rami Halloush Mohammad F. Al-Hammouri Alaa Alghazo Loiy Al-Abed Mohammad Amra Ayooub Alsarhan Sami Aziz Alshammari |
| author_facet | Islam T. Almalkawi Rami Halloush Mohammad F. Al-Hammouri Alaa Alghazo Loiy Al-Abed Mohammad Amra Ayooub Alsarhan Sami Aziz Alshammari |
| author_sort | Islam T. Almalkawi |
| collection | DOAJ |
| description | The advent of a wide variety of affordable communication devices and cameras has enabled IoT systems to provide effective solutions for a wide range of civil and military applications. One of the potential applications is a surveillance system in which several cameras collaborate to monitor a specific area. However, existing surveillance systems are often based on traditional camera distribution and come with additional communication costs and redundancy in the detection range. Thus, we propose a smart and efficient camera distribution system based on machine learning using two Reinforcement Learning (RL) methods: Q-Learning and neural networks. Our proposed approach initially uses a geometric distributed network clustering algorithm that optimizes camera placement based on the camera Field of View (FoV). Then, to improve the camera distribution system, we integrate it with an RL technique, the role of which is to dynamically adjust the previous/existing setup to maximize target coverage while minimizing the number of cameras. The reinforcement agent modifies system parameters—such as the overlap distance between adjacent cameras, the camera FoV, and the number of deployed cameras—based on changing traffic distribution and conditions in the surveilled area. Simulation results confirm that the proposed camera distribution algorithm outperforms the existing methods when comparing the required number of cameras, network coverage percentage, and traffic coverage. |
| format | Article |
| id | doaj-art-a2b04c8addc84e1b8e759eb78af4fcdf |
| institution | Kabale University |
| issn | 2227-7080 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-a2b04c8addc84e1b8e759eb78af4fcdf2025-01-24T13:50:42ZengMDPI AGTechnologies2227-70802024-12-01131410.3390/technologies13010004Intelligent IoT-Based Network Clustering and Camera Distribution Algorithm Using Reinforcement LearningIslam T. Almalkawi0Rami Halloush1Mohammad F. Al-Hammouri2Alaa Alghazo3Loiy Al-Abed4Mohammad Amra5Ayooub Alsarhan6Sami Aziz Alshammari7Department of Computer Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, JordanCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitDepartment of Computer Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, JordanDepartment of Mechatronics Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, JordanDepartment of Computer Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, JordanDepartment of Computer Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, JordanDepartment of Information Technology, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, Zarqa 13133, JordanDepartment of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Rafha 91431, Saudi ArabiaThe advent of a wide variety of affordable communication devices and cameras has enabled IoT systems to provide effective solutions for a wide range of civil and military applications. One of the potential applications is a surveillance system in which several cameras collaborate to monitor a specific area. However, existing surveillance systems are often based on traditional camera distribution and come with additional communication costs and redundancy in the detection range. Thus, we propose a smart and efficient camera distribution system based on machine learning using two Reinforcement Learning (RL) methods: Q-Learning and neural networks. Our proposed approach initially uses a geometric distributed network clustering algorithm that optimizes camera placement based on the camera Field of View (FoV). Then, to improve the camera distribution system, we integrate it with an RL technique, the role of which is to dynamically adjust the previous/existing setup to maximize target coverage while minimizing the number of cameras. The reinforcement agent modifies system parameters—such as the overlap distance between adjacent cameras, the camera FoV, and the number of deployed cameras—based on changing traffic distribution and conditions in the surveilled area. Simulation results confirm that the proposed camera distribution algorithm outperforms the existing methods when comparing the required number of cameras, network coverage percentage, and traffic coverage.https://www.mdpi.com/2227-7080/13/1/4Internet of things (IoT)surveillance systemscamera field of view (FoV)machine learningreinforcement learningQ-learning |
| spellingShingle | Islam T. Almalkawi Rami Halloush Mohammad F. Al-Hammouri Alaa Alghazo Loiy Al-Abed Mohammad Amra Ayooub Alsarhan Sami Aziz Alshammari Intelligent IoT-Based Network Clustering and Camera Distribution Algorithm Using Reinforcement Learning Technologies Internet of things (IoT) surveillance systems camera field of view (FoV) machine learning reinforcement learning Q-learning |
| title | Intelligent IoT-Based Network Clustering and Camera Distribution Algorithm Using Reinforcement Learning |
| title_full | Intelligent IoT-Based Network Clustering and Camera Distribution Algorithm Using Reinforcement Learning |
| title_fullStr | Intelligent IoT-Based Network Clustering and Camera Distribution Algorithm Using Reinforcement Learning |
| title_full_unstemmed | Intelligent IoT-Based Network Clustering and Camera Distribution Algorithm Using Reinforcement Learning |
| title_short | Intelligent IoT-Based Network Clustering and Camera Distribution Algorithm Using Reinforcement Learning |
| title_sort | intelligent iot based network clustering and camera distribution algorithm using reinforcement learning |
| topic | Internet of things (IoT) surveillance systems camera field of view (FoV) machine learning reinforcement learning Q-learning |
| url | https://www.mdpi.com/2227-7080/13/1/4 |
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