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Unmanned Aerial Vehicles (UAVs) are poised to play a central role inrevolutionizing future services offered by the envisioned smart cities, thanksto their agility, flexibility, and cost-efficiency. UAVs are being widelydeployed in different verticals including surveillance, search and rescuemissions, delivery of items, and as an infrastructure for aerial communicationsin future wireless networks. UAVs can be used to survey target locations,collect raw data from the ground (i.e., video streams), generate computingtask(s) and offload it to the available servers for processing. In this work,we formulate a multi-objective optimization framework for both the networkresource allocation and the UAV trajectory planning problem using Mixed IntegerLinear Programming (MILP) optimization model. In consideration of the differentstake holders that may exist in a Cloud-Fog environment, we minimize the sum ofa weighted objective function, which allows network operators to tune theweights to emphasize/de-emphasize different cost functions such as theend-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 offloadingdecisions to be made under different constraints relating to EENPC, PPC, UAVTFDand UAVTPC which we explore in detail. For example, when the UAVs propulsionefficiency (UPE) is at its worst (10% considered), offloading via the macrobase station is the best choice and a maximum power saving of 34% can beachieved. Extensive studies on the UAVs coverage path planning (CPP) andcomputation offloading have been conducted, but none has tackled the issue in apractical Cloud-Fog architecture in which access, metro and core layers areconsidered in the service offloading in a distributed architecture like theCloud-Fog.

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