Energy Efficient UAV-Based Service Offloading Over Cloud-Fog Architectures
Energy Efficient UAV-Based Service Offloading Over Cloud-Fog Architectures
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DOI：
10.1109/access.2022.3201112
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发表时间：
2022-05
期刊：
影响因子：
3.9
通讯作者：
Hatem A. Alharbi;B. Yosuf;M. Aldossary;Jaber Almutairi;J. Elmirghani
中科院分区：
文献类型：
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作者：
Hatem A. Alharbi;B. Yosuf;M. Aldossary;Jaber Almutairi;J. Elmirghani
无人机(UAV)凭借其敏捷性、灵活性和成本效益，将在变革设想中的智能城市提供的未来服务方面发挥核心作用。无人机正被广泛部署在不同的垂直领域，包括监视、搜索和救援任务、物品运送，以及作为未来无线网络中空中通信的基础设施。无人机可用于测量目标位置，从地面收集原始数据(即视频流)，生成计算任务(S)，并将其卸载到可用的服务器进行处理。本文利用混合整数线性规划(MILP)优化模型，建立了网络资源分配和无人机航迹规划问题的多目标优化框架。考虑到云雾环境中可能存在的不同利益相关者，我们最小化了一个加权目标函数的和，该目标函数允许网络运营商调整权重以强调/淡化不同的成本函数，如端到端网络功耗(EENPC)、处理功耗(PPC)、无人机总飞行距离(UAVTFD)和无人机总功耗(UAVTPC)。我们的优化模型和结果使我们能够在与EENPC、PPC、UAVTFD和UAVTPC相关的不同约束下做出最优卸货决策，这是我们详细探索的。例如，当无人机的推进效率(UPE)处于最差(考虑10%)时，通过宏基站进行卸载是最佳选择，最大可实现34%的节电。人们已经对无人机的覆盖路径规划(CPP)和计算卸载进行了广泛的研究，但还没有人在实用的Cloud-Fog体系结构中解决这个问题，在像Cloud-Fog这样的分布式体系结构中，在评估服务卸载时考虑了接入层、城域层和核心层的所有元素。
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-to-end 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.