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Intelligent Target Cell Selection Algorithm for Low Latency 5G Networks

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Advances in Computational Intelligence and Communication

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

Security and efficiency during the handover process are critical challenges in 5G heterogeneous networks owing to its strict low latency and security requirements. The 5G networks support many services and communication paradigms. Examples of these applications include as Internet of Things (IoT), vehicular ad hoc networks (VANETs), device-to-device communication (D2D), among others. As such, security lapses in the core network can easily escalate to the supported communication devices and networks and vice versa. Unfortunately, legacy cellular networks deploy the received signal strength indicator (RSSI) as the only parameter during the handover process. This results in increased handover latencies, packet losses, and high handover failure probability. Consequently, other protocols have been introduced that deploy additional handover parameters such as data rates, service costs, battery power, bandwidth, and energy consumption. However, these schemes only deal with efficiency of the handoff process but rarely consider security and privacy issues. Conversely, security and privacy protocols rarely address efficiency aspects of the handover process. In this paper, an algorithm that leverages on artificial neural networks coupled with fuzzy logic for target cell selection is presented. Based on the obtained results, there is a 56.1% reduction in handover latency and a 38.8% reduction in packet losses when the proposed scheme is deployed. In terms of security, it upholds both backward and forward key secrecy. In addition, desynchronization and replay and attacks are effectively thwarted in the proposed algorithm.

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Author information

Authors and Affiliations

  1. Faculty of Biological & Physical Sciences, Tom Mboya University College, Homabay, Kenya

    Vincent Omollo Nyangaresi

  2. College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China

    Mustafa A. Al Sibahee

  3. Computer Technology Engineering Department, Iraq University College, Basrah, Iraq

    Mustafa A. Al Sibahee

  4. Department of Computer Science, College of Education for Pure Sciences, University of Basrah, Basrah, Iraq

    Zaid Ameen Abduljabbar

  5. Huazhong University of Science and Technology, Shenzhen Institute, Shenzhen, China

    Zaid Ameen Abduljabbar

  6. Communications Engineering Department, Iraq University College, Basrah, Iraq

    Abdulhadi Alhassani

  7. Department of Computer Science, College of Computer Science and Information Technology, University of Basrah, Basrah, Iraq

    Iman Qays Abduljaleel

  8. Department of Mathematics, College of Science, University of Basrah, Basrah, Iraq

    Enas Wahab Abood

Authors
  1. Vincent Omollo Nyangaresi
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  2. Mustafa A. Al Sibahee
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  3. Zaid Ameen Abduljabbar
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  4. Abdulhadi Alhassani
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  5. Iman Qays Abduljaleel
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  6. Enas Wahab Abood
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Corresponding author

Correspondence to Vincent Omollo Nyangaresi .

Editor information

Editors and Affiliations

  1. SIC Laboratory, Omnes Education Research Centre, ECE Paris School of Engineering, Paris, France

    Manolo Dulva Hina

  2. LISV laboratory, University of Versailles - Paris-Saclay, Vélizy, France

    Amar Ramdane-Cherif

  3. 5GIC & 6GIC, Institute for Communication Systems (ICS), University of Surrey, Guildford, UK

    Rafik Zitouni

  4. ECE Research Centre, ECE Paris School of Engineering, Paris, France

    Assia Soukane

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Nyangaresi, V.O., Al Sibahee, M.A., Abduljabbar, Z.A., Alhassani, A., Abduljaleel, I.Q., Abood, E.W. (2023). Intelligent Target Cell Selection Algorithm for Low Latency 5G Networks. In: Hina, M.D., Ramdane-Cherif, A., Zitouni, R., Soukane, A. (eds) Advances in Computational Intelligence and Communication. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-19523-5_6

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