Snapshot for IoT: Adaptive Measurement for Multidimensional QoS Resources

Zhao, Yuyu; Cheng, Guang; Liu, Chunxiang; Chen, Zihan

doi:10.1109/iwqos52092.2021.9521342

Cited by 3 publications

(2 citation statements)

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“…However, challenges in the execution efficiency of the proposed binary computation offloading technique were encountered. Magnetic RAM (MRAM), discussed in [4], aimed to free up IoT device resources but faced challenges in ensuring efficient fog-based services, posing potential hurdles in service optimization. Moreover, fog computing, proposed for low-latency data services in [4], faced challenges in service efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Magnetic RAM (MRAM), discussed in [4], aimed to free up IoT device resources but faced challenges in ensuring efficient fog-based services, posing potential hurdles in service optimization. Moreover, fog computing, proposed for low-latency data services in [4], faced challenges in service efficiency. In [25], a genetic algorithm addressed challenges in efficient resource allocation for HetNets, striving to optimize resource utilization despite inherent complexities.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Mushtaq,

Chughtai,

Zhang

et al. 2024

IEEE Access

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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.

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Section: Literature Reviewmentioning

confidence: 99%