The routing protocols are paramount to guarantee the Quality of Service(QoS)for Flying Ad Hoc Networks(FANETs).However,they still face several challenges owing to high mobility and dynamic topology.This paper mainly f...The routing protocols are paramount to guarantee the Quality of Service(QoS)for Flying Ad Hoc Networks(FANETs).However,they still face several challenges owing to high mobility and dynamic topology.This paper mainly focuses on the adaptive routing protocol and proposes a Three Dimensional Q-Learning(3DQ)based routing protocol to guarantee the packet delivery ratio and improve the QoS.In 3DQ routing,we propose a Q-Learning based routing decision scheme,which contains a link-state prediction module and routing decision module.The link-state prediction module allows each Unmanned Aerial Vehicle(UAV)to predict the link-state of Neighboring UAVs(NUs),considering their Three Dimensional mobility and packet arrival.Then,UAV can produce routing decisions with the help of the routing decision module considering the link-state.We evaluate the various performance of 3DQ routing,and simulation results demonstrate that 3DQ can improve packet delivery ratio,goodput and delay of baseline protocol at most 71.36%,89.32%and 83.54%in FANETs over a variety of communication scenarios.展开更多
The Unmanned Aerial Vehicle(UAV)technologies are envisioned to play an important role in the era of Air-Space-Ground integrated networks.In this paper,we investigate the connectivity of a Flying Ad hoc Network(FANET)i...The Unmanned Aerial Vehicle(UAV)technologies are envisioned to play an important role in the era of Air-Space-Ground integrated networks.In this paper,we investigate the connectivity of a Flying Ad hoc Network(FANET)in the presence of a groundbased terminal.In particular,the connected probability of the UAV-to-UAV (U2U) link as well as that of the UAV-to-Ground (U2G) link in a three dimensional (3D) space are analyzed.Furthermore,to mitigate the aggregate interference from UAV individuals,a priority based power control scheme is implemented for enhancing the connectivity of both U2U and U2G links.Numerical results illustrate the effectiveness of the proposed analysis.展开更多
This paper establishes a new layered flying ad hoc networks(FANETs) system of mobile edge computing(MEC) supported by multiple UAVs,where the first layer of user UAVs can perform tasks such as area coverage, and the s...This paper establishes a new layered flying ad hoc networks(FANETs) system of mobile edge computing(MEC) supported by multiple UAVs,where the first layer of user UAVs can perform tasks such as area coverage, and the second layer of MEC UAVs are deployed as flying MEC sever for user UAVs with computing-intensive tasks. In this system, we first divide the user UAVs into multiple clusters, and transmit the tasks of the cluster members(CMs) within a cluster to its cluster head(CH). Then, we need to determine whether each CH’ tasks are executed locally or offloaded to one of the MEC UAVs for remote execution(i.e., task scheduling), and how much resources should be allocated to each CH(i.e., resource allocation), as well as the trajectories of all MEC UAVs.We formulate an optimization problem with the aim of minimizing the overall energy consumption of all user UAVs, under the constraints of task completion deadline and computing resource, which is a mixed integer non-convex problem and hard to solve. We propose an iterative algorithm by applying block coordinate descent methods. To be specific, the task scheduling between CH UAVs and MEC UAVs, computing resource allocation, and MEC UAV trajectory are alternately optimized in each iteration. For the joint task scheduling and computing resource allocation subproblem and MEC UAV trajectory subproblem, we employ branch and bound method and continuous convex approximation technique to solve them,respectively. Extensive simulation results validate the superiority of our proposed approach to several benchmarks.展开更多
In recent years,with the growth in Unmanned Aerial Vehicles(UAVs),UAV-based systems have become popular in both military and civil applications.In these scenarios,the lack of reliable communication infrastructure has ...In recent years,with the growth in Unmanned Aerial Vehicles(UAVs),UAV-based systems have become popular in both military and civil applications.In these scenarios,the lack of reliable communication infrastructure has motivated UAVs to establish a network as flying nodes,also known as Flying Ad Hoc Networks(FANETs).However,in FANETs,the high mobility degree of flying and terrestrial users may be responsible for constant changes in the network topology,making end-to-end connections in FANETs challenging.Mobility estimation and prediction of UAVs can address the challenge mentioned above since it can provide better routing planning and improve overall FANET performance in terms of continuous service availability.We thus develop a Software Defined Network(SDN)-based heterogeneous architecture for reliable communication in FANETs.In this architecture,we apply an Extended Kalman Filter(EKF)for accurate mobility estimation and prediction of UAVs.In particular,we formulate the routing problem in SDN-based Heterogeneous FANETs as a graph decision problem.As the problem is NP-hard,we further propose a Directional Particle Swarming Optimization(DPSO)approach to solve it.The extensive simulation results demonstrate that the proposed DPSO routing can exhibit superior performance in improving the goodput,packet delivery ratio,and delay.展开更多
软件定义网络(Software Defined Network,SDN)依靠着其集中控制、可编程性和数控分离等优点,能够有效解决无人机网络(Flying Ad Hoc Network,FANET)面临的任务拓扑高度变化、网络链路连接不稳定、网络安全防护脆弱以及应用程序的异构性...软件定义网络(Software Defined Network,SDN)依靠着其集中控制、可编程性和数控分离等优点,能够有效解决无人机网络(Flying Ad Hoc Network,FANET)面临的任务拓扑高度变化、网络链路连接不稳定、网络安全防护脆弱以及应用程序的异构性等问题,极大地提升FANET的灵活性和可靠性。针对SDN架构与FANET的结合问题,描述了SDN的体系架构,并以SDN控制器部署方式为关注点分类别概括了近几年软件定义无人机网络(Software-defined Flying Ad Hoc Network,SD-FANET)的研究进展,重点阐述了结合移动边缘计算(Mobile Edge Computing,MEC)的SD-FANET研究现状,最后指出了SD-FANET的应用场景和一些具体的未来研究方向。展开更多
基金This work is supported in part by the National Natural Science Foundation of China under Grant No.61931011in part by the National Key Research and Development Project of China under Grant No.2018YFB1800801+2 种基金in part by the Primary Research&Development plan of Jiangsu Province under Grant BE2021013-4in part by the National Natural Science Foundation of China under Grants No.61827801 and 61631020the China Scholarship Council(CSC)Grant 202006830072.
文摘The routing protocols are paramount to guarantee the Quality of Service(QoS)for Flying Ad Hoc Networks(FANETs).However,they still face several challenges owing to high mobility and dynamic topology.This paper mainly focuses on the adaptive routing protocol and proposes a Three Dimensional Q-Learning(3DQ)based routing protocol to guarantee the packet delivery ratio and improve the QoS.In 3DQ routing,we propose a Q-Learning based routing decision scheme,which contains a link-state prediction module and routing decision module.The link-state prediction module allows each Unmanned Aerial Vehicle(UAV)to predict the link-state of Neighboring UAVs(NUs),considering their Three Dimensional mobility and packet arrival.Then,UAV can produce routing decisions with the help of the routing decision module considering the link-state.We evaluate the various performance of 3DQ routing,and simulation results demonstrate that 3DQ can improve packet delivery ratio,goodput and delay of baseline protocol at most 71.36%,89.32%and 83.54%in FANETs over a variety of communication scenarios.
基金National Natural Science Foundation of China(No.62071035)。
文摘The Unmanned Aerial Vehicle(UAV)technologies are envisioned to play an important role in the era of Air-Space-Ground integrated networks.In this paper,we investigate the connectivity of a Flying Ad hoc Network(FANET)in the presence of a groundbased terminal.In particular,the connected probability of the UAV-to-UAV (U2U) link as well as that of the UAV-to-Ground (U2G) link in a three dimensional (3D) space are analyzed.Furthermore,to mitigate the aggregate interference from UAV individuals,a priority based power control scheme is implemented for enhancing the connectivity of both U2U and U2G links.Numerical results illustrate the effectiveness of the proposed analysis.
基金supported in part by the National Natural Science Foundation of China under Grant No.61931011in part by the Primary Research & Developement Plan of Jiangsu Province No. BE2021013-4+2 种基金in part by the National Natural Science Foundation of China under Grant No. 62072303in part by the National Postdoctoral Program for Innovative Talents of China No. BX20190202in part by the Open Project Program of the Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space No. KF20202105。
文摘This paper establishes a new layered flying ad hoc networks(FANETs) system of mobile edge computing(MEC) supported by multiple UAVs,where the first layer of user UAVs can perform tasks such as area coverage, and the second layer of MEC UAVs are deployed as flying MEC sever for user UAVs with computing-intensive tasks. In this system, we first divide the user UAVs into multiple clusters, and transmit the tasks of the cluster members(CMs) within a cluster to its cluster head(CH). Then, we need to determine whether each CH’ tasks are executed locally or offloaded to one of the MEC UAVs for remote execution(i.e., task scheduling), and how much resources should be allocated to each CH(i.e., resource allocation), as well as the trajectories of all MEC UAVs.We formulate an optimization problem with the aim of minimizing the overall energy consumption of all user UAVs, under the constraints of task completion deadline and computing resource, which is a mixed integer non-convex problem and hard to solve. We propose an iterative algorithm by applying block coordinate descent methods. To be specific, the task scheduling between CH UAVs and MEC UAVs, computing resource allocation, and MEC UAV trajectory are alternately optimized in each iteration. For the joint task scheduling and computing resource allocation subproblem and MEC UAV trajectory subproblem, we employ branch and bound method and continuous convex approximation technique to solve them,respectively. Extensive simulation results validate the superiority of our proposed approach to several benchmarks.
文摘In recent years,with the growth in Unmanned Aerial Vehicles(UAVs),UAV-based systems have become popular in both military and civil applications.In these scenarios,the lack of reliable communication infrastructure has motivated UAVs to establish a network as flying nodes,also known as Flying Ad Hoc Networks(FANETs).However,in FANETs,the high mobility degree of flying and terrestrial users may be responsible for constant changes in the network topology,making end-to-end connections in FANETs challenging.Mobility estimation and prediction of UAVs can address the challenge mentioned above since it can provide better routing planning and improve overall FANET performance in terms of continuous service availability.We thus develop a Software Defined Network(SDN)-based heterogeneous architecture for reliable communication in FANETs.In this architecture,we apply an Extended Kalman Filter(EKF)for accurate mobility estimation and prediction of UAVs.In particular,we formulate the routing problem in SDN-based Heterogeneous FANETs as a graph decision problem.As the problem is NP-hard,we further propose a Directional Particle Swarming Optimization(DPSO)approach to solve it.The extensive simulation results demonstrate that the proposed DPSO routing can exhibit superior performance in improving the goodput,packet delivery ratio,and delay.
文摘软件定义网络(Software Defined Network,SDN)依靠着其集中控制、可编程性和数控分离等优点,能够有效解决无人机网络(Flying Ad Hoc Network,FANET)面临的任务拓扑高度变化、网络链路连接不稳定、网络安全防护脆弱以及应用程序的异构性等问题,极大地提升FANET的灵活性和可靠性。针对SDN架构与FANET的结合问题,描述了SDN的体系架构,并以SDN控制器部署方式为关注点分类别概括了近几年软件定义无人机网络(Software-defined Flying Ad Hoc Network,SD-FANET)的研究进展,重点阐述了结合移动边缘计算(Mobile Edge Computing,MEC)的SD-FANET研究现状,最后指出了SD-FANET的应用场景和一些具体的未来研究方向。
