论文标题
基于无人机的节能服务在云雾架构上卸载
Energy Efficient UAV-Based Service Offloading over Cloud-Fog Architectures
论文作者
论文摘要
由于其敏捷性，灵活性和成本效益，无人驾驶飞机（UAV）有望在革新设想的智能城市提供的未来服务方面发挥核心作用。无人机广泛部署在不同的垂直领域，包括监视，搜索和救援任务，物品交付以及作为未来无线网络中航空通信的基础架构。无人机可用于调查目标位置，从地面（即视频流）收集原始数据，生成计算任务并将其卸载到可用的服务器进行处理。在这项工作中，我们使用混合整数线性编程（MILP）优化模型为网络资源分配和UAV轨迹计划问题制定了多目标优化框架。考虑到可能在云灾害环境中可能存在的不同利益持有人，我们最大程度地减少了加权目标功能的总和，该功能使网络运营商可以调整权重，以强调/降低不同的成本功能，例如端到端网络网络功耗（例如EENPC），处理功耗（EENPC），处理功耗（PPC），UAVS总计距离（uavs flublting digption total digption digplation total距离（Uavtff）（Uavtff）（UAV）（UAV）（UAV）和UAV（UAV）（UAV）和UAVD（UAV）和UAV（UAV）和UAV（UAV）和UAV（UAV）和UAV（UAV）和UAV（UAV）和UAVS和UAV。我们的优化模型和结果使我们能够在与EENPC，PPC，UAVTFD和UAVTPC有关的不同约束下做出最佳卸载决策。例如，当无人机推进效率（UPE）处于最糟糕的情况下（考虑到10％）时，可以通过宏基站的卸载是最佳选择，并且可以实现最大节省34％的功率。已经进行了有关无人机覆盖路径计划（CPP）和计算卸载的广泛研究，但是在实用的云灾害体系结构中，没有人能解决该问题，其中在该构建中，在云事号（例如云雾）中的分布式体系结构中考虑了访问，地铁和核心层。
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), UAVs total flight distance (UAVTFD), and UAVs 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 UAVs 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 UAVs coverage path planning (CPP) and computation offloading have been conducted, but none has tackled the issue in a practical Cloud-Fog architecture in which access, metro and core layers are considered in the service offloading in a distributed architecture like the Cloud-Fog.
