Framework for Optimized Resource Allocation in Multi-User, Multi-Service, Multi-Device Aerial Networks

• Published in: IEEE access 2024, Vol.12, p.54866-54878
• Main Authors: Mushtaq, Muhammad Irfan, Chughtai, Omer, Zhang, Yudong, Alenezi, Ali H., Alqahtani, Nayef
• Format: Article
• Language: English
• Subjects: aerial networks; Algorithms; CAD; Computational modeling; Computer aided design; Costs; Drone aircraft; Drone vehicles; Drones; Genetic algorithms; heterogeneous devices; Integer programming; intelligent relaxation; Long Term Evolution; multi-user environments; Multiuser channels; Optimization; Penalty function; Rendering (computer graphics); Resource allocation; Resource management; service diversity; Task analysis; Tethered balloons; Unmanned aerial vehicles; utility minimization; Wireless fidelity; Wireless networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3384562

With the increasing prevalence of multi-user, multi-service, and heterogeneous multi-device environments, there is a need to address the imperative for efficient resource allocation in contemporary wireless networks, such as those involving unmanned aerial vehicles (UAVs) or drones. In this regard, this work addresses the challenges within a five-dimensional heterogeneous wireless network model, focusing on diverse services such as Big Data Analytics, Video Rendering, and Computer-Aided Design, and the allocation of resources among heterogeneous devices, including UAVs, tethered balloons, and multi-rotors. The resource allocation is facilitated through multiple interfaces like LTE, Wifi, LoRa, and Sigfox, catering to the diverse needs of users operating in aerial Networks. Additionally, this work introduces a novel Intelligent Relaxation using the Penalty Function (IRPF) approach for resource allocation, treating it as an integer programming problem to balance user needs while ensuring affordability. A comparative analysis is conducted between the proposed approach and the traditional branch-and-bound algorithm. In scenarios requiring resource allocation for numerous services based on user demand and device capabilities, the proposed work presents a penalty-based integrality gap solution adept at managing fractional values. The resulting optimization framework is meticulously designed to minimize activation and operating costs while optimizing utility. Additionally, the computing efficiency of the proposed approach is demonstrated by extensive simulations that prove its superiority over the traditional algorithm. Consequently, this research emphasizes the essential role of the proposed model in navigating the intricate challenges of resource allocation in modern drone-centric wireless networks.