Unmanned aerial vehicle(UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things(AIoT)in the forthcoming sixth-generation(6G)communication networks.With the use of flexible ...Unmanned aerial vehicle(UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things(AIoT)in the forthcoming sixth-generation(6G)communication networks.With the use of flexible UAVs,massive sensing data is gathered and processed promptly without considering geographical locations.Deep neural networks(DNNs)are becoming a driving force to extract valuable information from sensing data.However,the lightweight servers installed on UAVs are not able to meet the extremely high requirements of inference tasks due to the limited battery capacities of UAVs.In this work,we investigate a DNN model placement problem for AIoT applications,where the trained DNN models are selected and placed on UAVs to execute inference tasks locally.It is impractical to obtain future DNN model request profiles and system operation states in UAV-enabled edge computing.The Lyapunov optimization technique is leveraged for the proposed DNN model placement problem.Based on the observed system overview,an advanced online placement(AOP)algorithm is developed to solve the transformed problem in each time slot,which can reduce DNN model transmission delay and disk I/O energy cost simultaneously while keeping the input data queues stable.Finally,extensive simulations are provided to depict the effectiveness of the AOP algorithm.The numerical results demonstrate that the AOP algorithm can reduce 18.14%of the model placement cost and 29.89%of the input data queue backlog on average by comparing it with benchmark algorithms.展开更多
The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications a...The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer(WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things(IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energyefficiency(EE) minimization problem is formulated.Due to non-convexity and the time domain coupling of the variables in the formulated problem, a lowcomplexity online computation offloading and trajectory scheduling algorithm(OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.展开更多
Unmanned aerial vehicles(UAVs)are en-visioned as a promising means of providing wireless services for various complex terrains and emergency situations.In this paper,we consider a wireless UAV-enabled cognitive commun...Unmanned aerial vehicles(UAVs)are en-visioned as a promising means of providing wireless services for various complex terrains and emergency situations.In this paper,we consider a wireless UAV-enabled cognitive communication network,where a rotary-wing UAV transmits confidential information to a ground cognitive user over the spectrum assigned to primary users(PUs),while eavesdroppers attempt to wiretap the legitimate transmission.In order to en-hance the secrecy performance of wireless communi-cations,the secrecy rate(SR)of the UAV-enabled cog-nitive communication system is maximized through optimizing UAV three-dimensional(3D)flying trajec-tory while satisfying the requirements of UAV’s initial and final locations and guaranteeing the constraint of maximum speed of UAV and the interference thresh-old of each PU.However,the formulated SR maxi-mization(SRM)problem is non-convex.For the pur-pose of dealing with this intractable problem,we em-ploy the difference of two-convex functions approxi-mation approach to convert the non-convex optimiza-tion problem into a convex one,which is then solved through applying standard convex optimization tech-niques.Moreover,an iterative 3D trajectory opti-mization algorithm for SRM scheme is proposed to achieve the near-optimal 3D trajectory.Simulation re-sults show that our proposed 3D trajectory optimiza-tion based SRM algorithm has good convergence,and the proposed SRM scheme outperforms the bench-mark approach in terms of the SR performance.展开更多
With the evolution of 5th generation(5G)and 6th generation(6G)wireless communication technologies,various Internet of Things(IoT)devices and artificial intelligence applications are proliferating,putting enormous pres...With the evolution of 5th generation(5G)and 6th generation(6G)wireless communication technologies,various Internet of Things(IoT)devices and artificial intelligence applications are proliferating,putting enormous pressure on existing computing power networks.Unmanned aerial vehicle(UAV)-enabled mobile edge computing(U-MEC)shows potential to alleviate this pressure and has been recognized as a new paradigm for responding to data explosion.Nevertheless,the conflict between computing demands and resource-constrained UAVs poses a great challenge.Recently,researchers have proposed resource management solutions in U-MEC for computing tasks with dependency.However,the repeatability among the tasks was ignored.In this paper,considering repeatability and dependency,we propose a U-MEC paradigm based on a computing power pool for processing computationally intensive tasks,in which UAVs can share information and computing resources.To ensure the effectiveness of computing power pool construction,the problem of balancing the energy consumption of UAVs is formulated through joint optimization of an offloading strategy,task scheduling,and resource allocation.To address this NP-hard problem,we adopt a two-stage alternate optimization algorithm based on successive convex approximation(SCA)and an improved genetic algorithm(GA).The simulation results show that the proposed scheme reduces time consumption by 18.41%and energy consumption by 21.68%on average,which can improve the working efficiency of UAVs.展开更多
基金supported by the National Science Foundation of China(Grant No.62202118)the Top-Technology Talent Project from Guizhou Education Department(Qianjiao Ji[2022]073)+1 种基金the Natural Science Foundation of Hebei Province(Grant No.F2022203045 and F2022203026)the Central Government Guided Local Science and Technology Development Fund Project(Grant No.226Z0701G).
文摘Unmanned aerial vehicle(UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things(AIoT)in the forthcoming sixth-generation(6G)communication networks.With the use of flexible UAVs,massive sensing data is gathered and processed promptly without considering geographical locations.Deep neural networks(DNNs)are becoming a driving force to extract valuable information from sensing data.However,the lightweight servers installed on UAVs are not able to meet the extremely high requirements of inference tasks due to the limited battery capacities of UAVs.In this work,we investigate a DNN model placement problem for AIoT applications,where the trained DNN models are selected and placed on UAVs to execute inference tasks locally.It is impractical to obtain future DNN model request profiles and system operation states in UAV-enabled edge computing.The Lyapunov optimization technique is leveraged for the proposed DNN model placement problem.Based on the observed system overview,an advanced online placement(AOP)algorithm is developed to solve the transformed problem in each time slot,which can reduce DNN model transmission delay and disk I/O energy cost simultaneously while keeping the input data queues stable.Finally,extensive simulations are provided to depict the effectiveness of the AOP algorithm.The numerical results demonstrate that the AOP algorithm can reduce 18.14%of the model placement cost and 29.89%of the input data queue backlog on average by comparing it with benchmark algorithms.
基金supported in part by the U.S. National Science Foundation under Grant CNS-2007995in part by the National Natural Science Foundation of China under Grant 92067201,62171231in part by Jiangsu Provincial Key Research and Development Program under Grant BE2020084-1。
文摘The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer(WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things(IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energyefficiency(EE) minimization problem is formulated.Due to non-convexity and the time domain coupling of the variables in the formulated problem, a lowcomplexity online computation offloading and trajectory scheduling algorithm(OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.
基金National Natural Sci-ence Foundation of China(Nos.61631020,61671253,62071253 and 91738201)the Key Project of Nat-ural Science Research of Higher Education Institu-tions of Jiangsu Province(Grant No.18KJB510031).
文摘Unmanned aerial vehicles(UAVs)are en-visioned as a promising means of providing wireless services for various complex terrains and emergency situations.In this paper,we consider a wireless UAV-enabled cognitive communication network,where a rotary-wing UAV transmits confidential information to a ground cognitive user over the spectrum assigned to primary users(PUs),while eavesdroppers attempt to wiretap the legitimate transmission.In order to en-hance the secrecy performance of wireless communi-cations,the secrecy rate(SR)of the UAV-enabled cog-nitive communication system is maximized through optimizing UAV three-dimensional(3D)flying trajec-tory while satisfying the requirements of UAV’s initial and final locations and guaranteeing the constraint of maximum speed of UAV and the interference thresh-old of each PU.However,the formulated SR maxi-mization(SRM)problem is non-convex.For the pur-pose of dealing with this intractable problem,we em-ploy the difference of two-convex functions approxi-mation approach to convert the non-convex optimiza-tion problem into a convex one,which is then solved through applying standard convex optimization tech-niques.Moreover,an iterative 3D trajectory opti-mization algorithm for SRM scheme is proposed to achieve the near-optimal 3D trajectory.Simulation re-sults show that our proposed 3D trajectory optimiza-tion based SRM algorithm has good convergence,and the proposed SRM scheme outperforms the bench-mark approach in terms of the SR performance.
基金supported by the Natural Science Foundation of Jiangsu Province,China(No.BK20211227)the National Natural Science Foundation of China(No.62273356)。
文摘With the evolution of 5th generation(5G)and 6th generation(6G)wireless communication technologies,various Internet of Things(IoT)devices and artificial intelligence applications are proliferating,putting enormous pressure on existing computing power networks.Unmanned aerial vehicle(UAV)-enabled mobile edge computing(U-MEC)shows potential to alleviate this pressure and has been recognized as a new paradigm for responding to data explosion.Nevertheless,the conflict between computing demands and resource-constrained UAVs poses a great challenge.Recently,researchers have proposed resource management solutions in U-MEC for computing tasks with dependency.However,the repeatability among the tasks was ignored.In this paper,considering repeatability and dependency,we propose a U-MEC paradigm based on a computing power pool for processing computationally intensive tasks,in which UAVs can share information and computing resources.To ensure the effectiveness of computing power pool construction,the problem of balancing the energy consumption of UAVs is formulated through joint optimization of an offloading strategy,task scheduling,and resource allocation.To address this NP-hard problem,we adopt a two-stage alternate optimization algorithm based on successive convex approximation(SCA)and an improved genetic algorithm(GA).The simulation results show that the proposed scheme reduces time consumption by 18.41%and energy consumption by 21.68%on average,which can improve the working efficiency of UAVs.
