Decentralized content sharing in mobile ad-hoc networks:A survey

The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic.To address this issue and take advantage of the short-range communication capabilities of smart mobile devices,the decentralized content sharing approach has emerged as a suitable and promising alternative.Decentralized content sharing uses a peer-to-peer network among colocated smart mobile device users to fulfil content requests.Several articles have been published to date to address its different aspects including group management,interest extraction,message forwarding,participation incentive,and content replication.This survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration.

Adaptive 3D Routing Protocol for Flying Ad Hoc Networks Based on Prediction-Driven Q-Learning

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.

On Connectivity of Flying Ad Hoc Networks in the Presence of Ground Terminal

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.

A Cyber–Physical Routing Protocol Exploiting Trajectory Dynamics for Mission-Oriented Flying Ad Hoc Networks

As a special type of mobile ad hoc network(MANET),the flying ad hoc network(FANET)has the potential to enable a variety of emerging applications in both civilian wireless communications(e.g.,5G and 6G)and the defense industry.The routing protocol plays a pivotal role in FANET.However,when designing the routing protocol for FANET,it is conventionally assumed that the aerial nodes move randomly.This is clearly inappropriate for a mission-oriented FANET(MO-FANET),in which the aerial nodes typically move toward a given destination from given departure point(s),possibly along a roughly deterministic flight path while maintaining a well-established formation,in order to carry out certain missions.In this paper,a novel cyber–physical routing protocol exploiting the particular mobility pattern of an MO-FANET is proposed based on cross-disciplinary integration,which makes full use of the missiondetermined trajectory dynamics to construct the time sequence of rejoining and separating,as well as the adjacency matrix for each node,as prior information.Compared with the existing representative routing protocols used in FANETs,our protocol achieves a higher packet-delivery ratio(PDR)at the cost of even lower overhead and lower average end-to-end latency,while maintaining a reasonably moderate and stable network jitter,as demonstrated by extensive ns-3-based simulations assuming realistic configurations in an MO-FANET.

飞行自组织网的全双工双向时间传递技术研究

针对飞行自组网(FANET)中节点间相对运动导致双向时间传递精度下降的问题,该文提出一种全双工双向时间传递(TWTT)方法。首先,根据全双工双向时间传递过程构造了需要求解的方程组,推导了单次时间传递的同步误差表达式;然后,分析了在无频偏和有频偏条件下,迭代进行全双工双向时间传递的收敛性;最后,通过仿真分析和实验验证比较了全双工双向时间传递方法和传统双向时间传递方法的性能。仿真和实验结果表明,全双工双向时间传递方法在节点高速机动下,时间同步精度可达到与物理层时间戳相同的精度,优于现有的运动补偿方法。

一种中心节点调度时隙分配的无人机自组网TDMA协议

针对现有无人机自组网协议中,集中式树形网络中控制时隙存在冗余、帧中数据时段占比较小的问题,提出了一种基于中心节点调度时隙分配的无人机时分多址接入(Time Division Multiple Access,TDMA)协议。该协议采用基于全网拓扑感知的时隙分配机制调整控制时段的帧结构与时隙顺序,基于中心节点调度的控制时隙复用机制提高控制时隙的利用率,缩短控制时段总长度以提高数据时段占比。与BiPi-TMAC(Bidirectional-Pipelined TDMA MAC)协议和ELDM-TDMA(Efficient and Low Delay Multi-hop TDMA)协议相比,该协议的数据平均时延和控制开销降低了9.6%和21.9%,网络吞吐量提高了10.5%,在无人机自组网场景中表现出良好的性能。

Joint Task Scheduling, Resource Allocation, and UAV Trajectory under Clustering for FANETs

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.

Routing Protocol for Heterogeneous FANETs with Mobility Prediction

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.

一种新的三维FANETs跨层自适应广播协议

针对盲目广播带来的广播风暴问题,该文提出一种新的3维FANETs自适应广播协议NABP。NABP应用跨层设计的方法,在维持原有协议层间结构分离的基础上,允许路由层共享MAC层的接收信号强度信息,并根据转发分组时增加通信范围计算出转发概率,倾向于让增加通信范围大的节点以高概率转发分组,从而减少了分组在网络中的重复传输,降低了节点间的竞争和碰撞,提高广播效率。NS-2仿真结果表明:与flooding+802.11和fp-flooding+802.11协议相比,NABP提高了转播节省率、减少了平均端对端延迟和平均丢包率。

基于DRL的飞行自组网自适应多模式路由算法

针对传统飞行自组网协议自适应能力不强、大规模网络应用场景效果不佳的问题,提出了一种基于深度强化学习的多模式路由算法。该算法综合利用系统吞吐量、分组递交率和平均端到端时延等参数构建价值函数,通过智能体自动调节各个无人机的路由工作模式,将大型网络分解为主体网络和数个与之相连的小型异构网络,降低了系统复杂度,局部性能达到最优,提升了整个网络的性能。使用NS3仿真平台测试了算法和传统协议AODV、DSDV的性能指标。仿真结果表明,算法显著优于传统协议,且网络规模越大、负载越高则优势越明显,平均吞吐量提升了55.46%,分组递交率提升了39.85%,平均端到端时延降低了60.94%。

基于自适应蜂鸟算法的飞行自组网拓扑优化

针对飞行自组网(FANET)中无人机(UAVs)快速移动造成的网络拓扑管理困难问题,考虑实际场景中无人机位置变化引起的可用信道差异,该文提出一种自适应蜂鸟算法对网络拓扑进行优化。首先,建立一个针对分簇结构的无人机拓扑模型,并且形成一个以最小化簇数量、负载偏差和簇移动度为目标的优化问题。其次,通过调节人工蜂鸟的觅食动作、加入扰动变异的方式,提出寻优能力更强的自适应蜂鸟算法(ADHA)。然后,设计合理的蜂鸟个体编码方式,将拓扑优化的决策过程转化为自适应蜂鸟算法的寻优过程。最后,通过仿真验证所提算法的收敛性,并与基于其他群智能优化算法的拓扑优化方法进行对比。实验结果表明,所提算法得到的拓扑优化策略不仅能够有效减少网络拓扑的簇数量,而且能够得到负载均衡、结构稳定的簇群。

一种基于聚类算法的ETX辅助无人机AODV路由协议

为了保证飞行自组网(flying ad hoc network,FANET)在拓扑快速变化下的稳定信息传输,在期望传输次数(expected transmission count,ETX)度量的无线自组网按需平面距离向量(ad hoc on-demand distant vector,AODV)路由协议基础上,提出了一种基于K-means聚类算法的K-AODV-ETX路由协议。协议采用到目的节点的跳数、错误传输次数、节点缓冲空间3个聚类特征对无人机节点进行分类,RREQ分组转发时选择最佳集群进行路由发现,最后采用ETX机制选择ETX值和最小路径进行数据传输。结果表明,K-AODV-ETX路由协议与现有的AODV-ETX路由协议和ND-AODV-ETX路由协议相比,能够有效保持网络的吞吐量,在端到端数据包投递率性能上稍有下降,在路由开销和时延性能方面有着显著的效果。K-AODV-ETX路由协议优化了路由发现过程的泛洪广播机制,有效改善了现有的ETX机制下协议的高开销、高延迟问题,并保证了网络的吞吐量,为设计低时延路由协议以及平衡ETX机制带来的高开销问题提供了一种有效的方法。

软件定义的无人机网络架构研究综述

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。

面向FANET的N-UCDS虚拟骨干网构建方法

大规模小型无人机通信网络会出现频繁的链路失效,因此有效的拓扑控制是当前飞行自组网的研究热点。为了符合大规模无人机任务多样性的场景,提出一种随机路径点-飞行信息预测移动模型,无人机节点可以通过邻居节点的飞行信息对链路持续时间进行预测。在此基础上,提出一种新的统一连通支配集(new unifying connected dominating set,N-UCDS)算法,与UCDS算法相比,N-UCDS算法改进了连通支配集成员的选取方式、虚拟骨干网的构建和维护机制、支配因子的计算方法和HELLO报文发送间隔。仿真结果表明,该方法的算法鲁棒性有较大提升,网络构建和维护时间均减少了一个周期,网络生存时间提升了5%,HELLO报文开销随节点最大通信半径的增大而减小。

基于多目标跟踪FANET网络的UAV协同通信算法研究

针对无人飞行器(unmanned aerial vehicles, UAV)群在飞行过程中动态无线通信飞行自组织网络(flying ad-hoc network, FANET)参数和扰动不确定的问题,采用多UAV无线协作通信模型研究动态不确定性,提出一种基于递归神经网络(recursive neural network, RNN)的多目标跟踪自适应控制算法。带有接入点控制方法的FANET网络可以优化无人机的通信系统容量和覆盖范围以及获得更快的响应速度。仿真结果表明:通过确定无人飞行器的最佳覆盖区域,所需总功率将有所降低,最大程度地确保地面设备最大的吞吐量,提高了UAV协作通信控制的准确性和灵敏度。

蜂群无人机自组网多优先级自适应退避算法

针对现有媒质接入控制(MAC)协议退避算法无法为蜂群无人机自组网(FANETs)提供区分服务,且在重负载时性能严重恶化等问题,提出一种多优先级自适应退避算法。采用忙闲因子自适应机制和最优竞争窗自适应机制,根据信道忙闲程度和网络状态参数自适应实时更新各优先级竞争窗口(CW)长度,从而使每次退避的竞争窗口可快速收敛到最佳状态,并实现了多业务区分服务,得到了最优的系统性能。通过建立不同优先级退避过程的三维Markov链模型求解得到了饱和吞吐量下的最优竞争窗自适应因子,并且理论推导了系统吞吐量和平均MAC时延的数学表达式。仿真结果表明,所提算法在重负载时能够实现多优先级区分服务并有效提高系统的吞吐量性能,相比区分业务优先级的自适应退避(PAB)算法和支持QoS的自适应竞争窗口退避算法(Q-ABACW),性能均有较大提升。

适用于飞行自组网的闲置时隙预约TDMA协议

在飞行自组网中,固定时隙分配时分多址接入(TDMA)协议存在闲置时隙无法成功使用的问题。通过对TDMA协议引入闲置时隙预约机制,提出一种支持业务优先级传输机制的闲置时隙预约TDMA协议。采用短帧长的方式满足协同与控制业务的低时延传输需求,并利用闲置时隙预约机制允许节点使用闲置时隙传输感知业务,从而满足感知业务的高吞吐量传输性能要求。仿真结果表明,与CF-MAC和CTMAC协议相比,该协议能够在降低传输时延的同时,有效提高信道利用率和网络吞吐量。

一种基于OLSR的无人机网络适用路由算法

无人机自组网的高动态特性以及节点能量高度受限的特点,使得传统路由协议难以适用于无人机网络。针对该问题,在OLSR协议的基础上提出一种无人机网络适用路由(UAV-OLSR)算法。依据链路变化情况实现无人机集群状态感知,综合考虑节点能量、节点位置等因素进行节点质量评估。采用多径思想并通过特定的路径度量准则选择较优路径进行数据转发。仿真结果表明,与OLSR和AODV协议相比,UAV-OLSR具有更低的数据包平均传输延迟、更高的数据包投递率以及更好的能量均衡效果,可以延长无人机网络的生存时间。

面向无人机自组网的可信路由协议

针对无人机自组织网络(FANETs)容易遭受内部攻击、链路中断频繁的问题,提出了一种基于信任的按需多径距离矢量路由协议(TAOMDV)。首先,建立了节点信任度评估模型,引入数据包转发率、可信交互度、探测包接收率作为信任评估因子,设计了自适应模糊信任聚合网络(AFTAN)计算节点的直接信任度,综合可信邻居节点的间接信任度以计算节点信任度。其次,引入信任波动惩罚机制,抵御信任模型中的开关攻击。最后,将信任模型应用于按需多径距离矢量路由协议(AOMDV)中,在路由发现阶段引入路径信任度作为路由选择依据,建立可信路径,在路由维护阶段通过路径报警机制对恶意节点做出及时响应。仿真结果表明TAOMDV不仅能抵御常见的黑洞、灰洞攻击与开关攻击,而且能够降低网络拓扑变更与链路中断的影响。与轻量级信任增强路由协议(TEAOMDV)相比,有效提高了网络的数据包投递率与吞吐量,优化了路由开销与平均端到端时延。

基于函数逼近的强化学习FANET路由优化算法

针对高速移动状态下的飞行自组网路由协议链路维护困难问题,提出一种基于强化学习的自适应链路状态路由优化算法QLA-OLSR。借鉴强化学习中的Q学习算法,通过感知动态环境下节点邻居数量变化和业务负载程度,构建价值函数求解最优HELLO时隙,提高节点链路发现与维护能力。利用优化Kanerva编码算法的状态相似度机制,降低QLA-OLSR算法复杂度并增强稳定性。仿真结果表明,QLA-OLSR算法能有效提升网络吞吐量,减少路由维护开销,且具有自学习特性,适用于高动态环境下的飞行自组网。