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Mourad ELGORMAOffline

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  • Tlemcen, Algeria
  • Tlemcen, Algeria
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    Mourad ELGORMA

    1 week, 5 days ago

    Data traffic management in AI-IoT network to reduce congestion
    👨‍🔬 Muhammad Usama, Ubaid Ullah, Zaid Muhammad, Muhammad Bux, Inam Ullah, Muhammad Rouf, Taminul Islam
    📅 2024-06-20
    📖
    🔗 DOI: https://doi.org/10.1201/9781003496410-10
    📊 Citations: 6
    🔓 Closed

    Abstract:
    As the artificial intelligence–internet of things (AI-IoT) network expands, the exponential growth in data generated by connected devices is leading to increased data traffic in AI-IoT networks. The surge in data traffic poses significant challenges to network infrastructure, causing congestion, latency, and inefficiencies. To address this issue, effective data traffic management techniques are crucial. This book chapter focuses on data traffic management in AI-AI-IoT networks to reduce congestion. It explores the underlying causes of congestion in AI-IoT networks and presents a comprehensive overview of existing congestion control mechanisms. Additionally, the chapter highlights the unique characteristics and requirements of AI-AI-IoT networks that differentiate them from traditional networks. It also examines various congestion detection and avoidance techniques designed explicitly for AI-AI-IoT environments, considering AI-IoT devices’ heterogeneity, scalability, and resource constraints. It discusses the importance of intelligent routing algorithms, traffic classification, and prioritization mechanisms in managing data traffic effectively. Moreover, the chapter delves into emerging technologies such as edge computing, fog computing, and network slicing, which can be leveraged to alleviate congestion in AI-IoT networks. It explores how off-loading computation and data processing tasks to the network edge can enhance traffic management and reduce latency. So this chapter provides valuable insights into the domain of data traffic management in AI-AI-IoT networks. It is a comprehensive resource for researchers, practitioners, and professionals interested in understanding and implementing effective congestion control mechanisms to ensure optimal performance and scalability in AI-IoT environments.

    References:

    #AI #IoT #Networking

About me

Mourad ELGORMA

PhD Student

ELGORMA Mourad is a PhD student in Information and Communication Technology (ICT) at Abou Bekr Belkaid University of Tlemcen, Algeria, where he is affiliated with the STIC Laboratory (Information and Communication Systems). His academic and professional work focuses on advanced topics in computer networking, cybersecurity, and Internet of Things (IoT).

He has developed strong expertise in network design, configuration, and troubleshooting, particularly with Cisco systems, routing, switching, and Quality of Service (QoS). His research interests include wireless networks, LTE technologies, IoT-based monitoring systems, and network security, with a special emphasis on detecting vulnerabilities and improving system reliability.

Mourad has contributed to several research works covering areas such as IoT applications for sports monitoring, drowning detection systems, and Wi-Fi intrusion detection using embedded systems. His work combines both theoretical research and practical implementation, reflecting a strong background in hardware-software integration.

In addition to his academic career, he works as a freelancer in IT services, specializing in VPS deployment, server administration, and WordPress platform management using tools such as cPanel and Webmin.

With a solid foundation in both research and real-world applications, Mourad is committed to advancing innovative solutions in networking, IoT, and cybersecurity, while continuously expanding his technical and scientific contributions.

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