Alkhaldi, TM, Darem, AA, Alhashmi, AA, Al-Hadhrami, T ORCID: https://orcid.org/0000-0001-7441-604X and Osman, AE, 2024. Enhancing smart city IoT communication: a two-layer NOMA-based network with caching mechanisms and optimized resource allocation. Computer Networks, 255: 110857. ISSN 1389-1286
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Abstract
With advancements in next-generation communication systems, large-scale Internet of Things device (IoTDs) deployments in smart cities face challenges like limited bandwidth, high latency, and network congestion. To address this, we propose a two-layer network architecture utilizing non-orthogonal multiple access (NOMA) and caching to enhance IoT communications’ performance, efficiency, and reliability. Our primary objective is to optimize resource allocation and solve the association problem in a two-layer network. We formulated a joint optimization problem to maximize system utility through device association, power, and bandwidth allocation, considering constraints like channel quality and interference. We decoupled the non-linear, non-convex problem using block coordinate descent (BCD) and inner approximation techniques to maximize the aggregated data rate in high-density IoT scenarios. This approach reduced computational complexity while proving the scheme’s theoretical and numerical convergence. To evaluate the proposed scheme, we compared its performance with an ideal backhauling approach, an exhaustive search (upper bound), and a Genetic algorithm-based heuristic. Our scheme outperformed the others, achieving 98.04% of the ideal backhauling and 99.60% of the upper bound. Statistical analysis confirmed its robustness and consistent performance across various conditions. The two-layer NOMA-based network with caching and optimized resource allocation significantly enhances IoT communication efficiency and resilience, offering a solid framework for future smart city deployments.
Item Type: | Journal article |
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Publication Title: | Computer Networks |
Creators: | Alkhaldi, T.M., Darem, A.A., Alhashmi, A.A., Al-Hadhrami, T. and Osman, A.E. |
Publisher: | Elsevier BV |
Date: | December 2024 |
Volume: | 255 |
ISSN: | 1389-1286 |
Identifiers: | Number Type 10.1016/j.comnet.2024.110857 DOI S1389128624006893 Publisher Item Identifier 2258937 Other |
Rights: | This accepted manuscript is available under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND) License. |
Divisions: | Schools > School of Science and Technology |
Record created by: | Melissa Cornwell |
Date Added: | 25 Oct 2024 14:05 |
Last Modified: | 25 Oct 2024 14:05 |
URI: | https://irep.ntu.ac.uk/id/eprint/52468 |
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