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    • Revista IJIMAI
    • 2022
    • vol. 7, nº 5, september 2022
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    • Revista IJIMAI
    • 2022
    • vol. 7, nº 5, september 2022
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    Design of Integrated Artificial Intelligence Techniques for Video Surveillance on IoT Enabled Wireless Multimedia Sensor Networks

    Autor: 
    Mansour, Romany F.
    ;
    Soto, Carlos
    ;
    Soto-Díaz, Roosvel
    ;
    Escorcia Gutierrez, José
    ;
    Gupta, Deepak
    ;
    Khanna, Ashish
    Fecha: 
    09/2022
    Palabra clave: 
    artificial intelligence; technological surveillance; internet of things; object detection; wireless sensor networks; IJIMAI
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/13709
    DOI: 
    https://doi.org/10.9781/ijimai.2022.08.005
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/3156
    Open Access
    Resumen:
    The recent advancements in the Internet of Things (IoT) and Wireless Multimedia Sensor Networks (WMSN) made high-speed multimedia streaming, data processing, and essential analytics processes with minimal delay. Multimedia sensors used in WMSN-based surveillance applications are beneficial helpful in attaining accurate and elaborate details. However, it has become essential to design an effective and lightweight solution for data traffic management in WMSN owing to the massive quantities of data, generated by multimedia sensors. The development of Artificial Intelligence (AI) and Machine Learning (ML) techniques can be leveraged to investigate, collect, store, and process multimedia streaming data for decision-making in real-time scenarios. In this aspect, the current study develops an Integrated AI technique for Video Surveillance in IoT-enabled WMSN, called IAIVS-WMSN. The proposed IAIVS-WMSN technique aims to design a practical scheme for object detection and data transmission in WMSN. The proposed IAIVS-WMSN approach encompasses three stages: object detection, image compression, and clustering. The Mask Regional Convolutional Neural Network (Mask RCNN) technique is primarily utilized for object detection in the target region. Besides, Neighbourhood Correlation Sequence-based Image Compression (NCSIC) technique is applied to reduce data transmission. Finally, Artificial Flora Algorithm (AFA)-based clustering technique is designed for the election of Cluster Heads (CHs) and construction clusters. The design of object detection with compression and clustering techniques for WMSN shows the novelty of the work. These three processes’ designs enable one to accomplish effective data transmission in IoT-enabled WMSN. The researchers conducted multiple simulations to highlight the supreme performance of the IAIVS-WMSN approach. The simulation outcomes inferred the enhanced performance of the IAIVS-WMSN algorithm to the existing approaches.
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