Intelligent IoT-Based Network Clustering and Camera Distribution Algorithm Using Reinforcement Learning

被引:0
|
作者
Almalkawi, Islam T. [1 ]
Halloush, Rami [2 ]
Al-Hammouri, Mohammad F. [1 ]
Alghazo, Alaa [3 ]
Al-Abed, Loiy [1 ]
Amra, Mohammad [1 ]
Alsarhan, Ayooub [4 ]
Alshammari, Sami Aziz [5 ]
机构
[1] Hashemite Univ, Fac Engn, Dept Comp Engn, Zarqa 13133, Jordan
[2] Amer Univ Middle East, Coll Engn & Technol, Egaila 54200, Kuwait
[3] Hashemite Univ, Fac Engn, Dept Mechatron Engn, Zarqa 13133, Jordan
[4] Hashemite Univ, Fac Prince Al Hussein Bin Abdallah II Informat Tec, Dept Informat Technol, Zarqa 13133, Jordan
[5] Northern Border Univ, Fac Comp & Informat Technol, Dept Informat Technol, Rafha 91431, Saudi Arabia
关键词
Internet of things (IoT); surveillance systems; camera field of view (FoV); machine learning; reinforcement learning; Q-learning; deep neural Q-network (DQN); COVERAGE;
D O I
10.3390/technologies13010004
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
引用
收藏
页数:31
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