Energy Minimization In UAV-assisted Fog Computing Networks

In order to support more and more delay-sensitive applications emerging in the Internet of Things,the speed of data processing needs to be increased and transmission time needs to be reduced.Fog computing technology can provide computing services for devices in the network by deploying servers with strong computing capabilities at the edge of the network.It can also meet low latency requirements.As a result,fog computing has attracted widespread attention in academia and industry.In recent years,unmanned aerial vehicles(UAVs)have been widely used in various industries due to their flexibility and mobility.In wireless communication networks,UAVs can establish the line-of-sight transmission link with devices on the ground,which can improve the efficiency of data transmission greatly.The UAV-assisted fog computing network has become a promising research direction.However,the size of a UAV is often relatively not big and the energy it can carry is limited during flight.There are certain challenges in how to reduce the energy consumption of the UAV while ensuring the completion of the task.Therefore,this paper studies the optimization design of two typical system models in the UAV-assisted fog computing network.The specific innovations are as follows:(1)Firstly,for a UAV-assisted fog computing network with the UAV serves as fog server,the optimization of UAV's energy consumption is studied.Due to the limited energy of a large number of sensor devices in the network,the UAV will simultaneously serve as a mobile energy source to power the sensor devices.The UAV first broadcasts energy signals to charge the sensors,and then the sensors use the harvested energy to complete their tasks by local computing and fog computing.In addition,the sensors will also store a certain amount of energy to ensure normal operation in the future.In order to achieve a green communication and computing system design while guaranteeing computational tasks of the sensors to be completed within a given time period,an optimization problem is formulated to minimize the UAV's energy consumption.Since the problem is non-convex,an effective solution is designed based on the successive convex approximation(SCA)theory.In order to make the design more in line with the actual scenario,the piecewise nonlinear energy harvesting(EH)model,the charging requirement of sensors and the practical velocity constraint of UAV are taken into account.By comparing with different optimization schemes,the experimental results verify that the proposed algorithm can effectively reduce the energy consumption of the UAV,and the optimization results with the proposed joint optimization algorithm are better.(2)For a UAV-assisted fog computing network with the UAV serves as a data node,the optimization of UAV's energy consumption is studied.During the flight,the UAV can offload the data to the fog servers with more powerful computing capabilities on the ground for calculation.In order to achieve the computing system design while guaranteeing computational tasks of the UAV to be completed within a given time period,an optimization problem is formulated to minimize the UAV's energy consumption.More practical UAV flight constraints are taken into account.Since the problem is non-convex,a joint UAV flight trajectory and transmit power optimization algorithm based on block alternating iterative theory is designed.During each block optimization,the sub-problem is approximated as a convex optimization problem and solved using standard convex technology.Simulation results verify the effectiveness of the proposed algorithm,and effective trajectory and transmit power planning can be obtained under different calculation data requirements,and the optimization results with the proposed joint optimization algorithm are better.