Radio Access Network Techniques Beyond 5G Network: A brief Overview

Ibrahim, Karar; Sadkhan, Sattar B.

doi:10.1109/aca52198.2021.9626804

Cited by 8 publications

(1 citation statement)

References 22 publications

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“…The RAN connects client devices, such as cell phones, computers, or remotely controlled machines, via a fiber-optic or wireless backhaul link. This connection links to the core network, which manages subscriber information and location data, among other things [11]. Modern computational techniques are an appropriate solution in this context, as they allow storage and computational resources to be moved closer to IoT systems, as represented by base stations (BSs).…”

Section: Introductionmentioning

confidence: 99%

Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT

et al. 2022

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Smart cities using the Internet of Things (IoT) can operate various IoT systems with better services that provide intelligent and efficient solutions for various aspects of urban life. With the rapidly growing number of IoT systems, the many smart city services, and their various quality of service (QoS) constraints, servers face the challenge of allocating limited resources across all Internet-based applications to achieve an efficient per-formance. The presence of a cloud in the IoT system of a smart city results in high energy con-sumption and delays in the network. Edge computing is based on a cloud computing framework where computation, storage, and network resources are moved close to the data source. The IoT framework is identical to cloud computing. The critical issue in edge computing when executing tasks generated by IoT systems is the efficient use of energy while maintaining delay limitations. In this paper, we study a multicriteria optimization approach for resource allocation with distributed edge computing in IoT-based smart cities. We present a three-layer network architecture for IoT-based smart cities. An edge resource allocation scheme based on an auctionable approach is proposed to ensure efficient resource computation for delay-sensitive tasks.

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Section: Introductionmentioning

confidence: 99%