Delay-Optimal Task Offloading for UAV-Enabled Edge-Cloud Computing Systems

Almutairi, Jaber; Aldossary, Mohammad; Alharbi, Hatem A.; Yosuf, Barzan A.; Elmirghani, Jaafar M. H.

doi:10.1109/access.2022.3174127

Cited by 38 publications

(10 citation statements)

References 56 publications

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“…This paper extends our previous proposals in [13] and [14]. In [13], we study the Internet of Things (IoT) processing placement problem, where the goal is to evaluate and compare the performance of the fog approach to the centralized processing at the cloud under two design approaches: 1) a fog network that is capacitated i.e., additional servers cannot be deployed at fog sites, and 2) an un-capacitated case whereby the number of deployed servers is unlimited at fog sites.…”

Section: Introductionsupporting

confidence: 87%

“…However, the work in [13] does not take into account the mobility of the IoT devices (i.e., IoT devices are assumed to be fixed) and the heterogeneity of the communication networks, aspects that are critical for the efficient delivery of mobile IoT services. In [14], we looked at minimizing the service delay encountered by the computational offloading problem in a multi-tier edge computing system whereby multiple UAVs generate tasks for processing either on local central processing units (CPU) or edge servers connected to base stations. The problem was formulated using linear programming and an algorithm was designed for real-time implementations.…”

Section: Introductionmentioning

confidence: 99%

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Energy Efficient UAV-Based Service Offloading Over Cloud-Fog Architectures

et al. 2022

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Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future services offered by the envisioned smart cities, thanks to their agility, flexibility, and cost-efficiency. UAVs are being widely deployed in different verticals including surveillance, search and rescue missions, delivery of items, and as an infrastructure for aerial communications in future wireless networks. UAVs can be used to survey target locations, collect raw data from the ground (i.e., video streams), generate computing task(s) and offload it to the available servers for processing. In this work, we formulate a multi-objective optimization framework for both the network resource allocation and the UAV trajectory planning problem using Mixed Integer Linear Programming (MILP) optimization model. In consideration of the different stake holders that may exist in a Cloud-Fog environment, we minimize the sum of a weighted objective function, which allows network operators to tune the weights to emphasize/de-emphasize different cost functions such as the end-toend network power consumption (EENPC), processing power consumption (PPC), UAV's total flight distance (UAVTFD), and UAV's total power consumption (UAVTPC). Our optimization models and results enable the optimum offloading decisions to be made under different constraints relating to EENPC, PPC, UAVTFD and UAVTPC which we explore in detail. For example, when the UAV's propulsion efficiency (UPE) is at its worst (10% considered), offloading via the macro base station is the best choice and a maximum power saving of 34% can be achieved. Extensive studies on the UAV's coverage path planning (CPP) and computation offloading have been conducted, but none has tackled the issue in a practical Cloud-Fog architecture in which all the elements of the access, metro and core layers are considered when evaluating the service offloading in a distributed architecture like the Cloud-Fog.

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Section: Introductionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Energy Efficient UAV-Based Service Offloading Over Cloud-Fog Architectures

et al. 2022

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“…The goal of the optimization problem is to find the optimal UAV position q u to maximize the sum of data rates of users defined in equation (10) while satisfying the SNR threshold in equation (16). The optimization problem can be formulated as follows:…”

Section: A Problem Formulationmentioning

confidence: 99%

Resource Scheduling Based on Deep Reinforcement Learning in UAV Assisted Emergency Communication Networks

Wang

Deng

et al. 2022

IEEE Trans. Commun.

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The advent of fifth generation (5G) networks has opened new avenues for enhancing connectivity, particularly in challenging environments like remote areas or disaster-struck regions. Unmanned aerial vehicles (UAVs) have been identified as a versatile tool in this context, particularly for improving network performance through the Integrated access and backhaul (IAB) feature of 5G. However, existing approaches to UAV-assisted network enhancement face limitations in dynamically adapting to varying user locations and network demands. This paper introduces a novel approach leveraging deep reinforcement learning (DRL) to optimize UAV placement in real-time, dynamically adjusting to changing network conditions and user requirements. Our method focuses on the intricate balance between fronthaul and backhaul links, a critical aspect often overlooked in current solutions. The unique contribution of this work lies in its ability to autonomously position UAVs in a way that not only ensures robust connectivity to ground users but also maintains seamless integration with central network infrastructure. Through various simulated scenarios, we demonstrate how our approach effectively addresses these challenges, enhancing coverage and network performance in critical areas. This research fills a significant gap in UAV-assisted 5G networks, providing a scalable and adaptive solution for future mobile networks.

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“…10 MB [58], [59], [60] Arrival rate of requests for task (λ j ) 10 requests/s [61] 6. Performance Evaluation…”

Section: Dataset Generationmentioning

confidence: 99%