Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanne...Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.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
Flying ad hoc networks(FANETs)present a challenging environment due to the dynamic and highly mobile nature of the network.Dynamic network topology and uncertain node mobility structure of FANETs do not aim to conside...Flying ad hoc networks(FANETs)present a challenging environment due to the dynamic and highly mobile nature of the network.Dynamic network topology and uncertain node mobility structure of FANETs do not aim to consider only one path transmission.Several different techniques are adopted to address the issues arising in FANETs,from game theory to clustering to channel estimation and other statistical schemes.These approaches mostly employ traditional concepts for problem solutions.One of the novel approaches that provide simpler solutions to more complex problems is to use biologically inspired schemes.Several Nature-inspired schemes address cooperation and alliance which can be used to ensure connectivity among network nodes.One such species that resembles the dynamicity of FANETs are Bats.In this paper,the biologically inspired metaheuristic technique of the BAT Algorithm is proposed to present a routing protocol called iBATCOOP(Improved BAT Algorithm using Cooperation technique).We opt for the design implementation of the natural posture of bats to handle the necessary flying requirements.Moreover,we envision the concept of cooperative diversity using multiple relays and present an iBAT-COOP routing protocol for FANETs.This paper employs cooperation for an optimal route selection and reflects on distance,Signal to Noise Ratio(SNR),and link conditions to an efficient level to deal with FANET’s routing.By way of simulations,the performance of iBAT-COOP protocol outperforms BAT-FANET protocol and reduces packet loss ratio,end-to-end delay,and transmission loss by 81%,21%,and 82%respectively.Furthermore,the average link duration is improved by 25%compared to the BAT-FANET protocol.展开更多
Most interesting area is the growing demand of flying-IoT mergers with smart cities.However,aerial vehicles,especially unmanned aerial vehicles(UAVs),have limited capabilities for maintaining node energy efficiency.In...Most interesting area is the growing demand of flying-IoT mergers with smart cities.However,aerial vehicles,especially unmanned aerial vehicles(UAVs),have limited capabilities for maintaining node energy efficiency.In order to communicate effectively,IoT is a key element for smart cities.While improving network performance,routing protocols can be deployed in flying-IoT to improve latency,packet drop rate,packet delivery,power utilization,and average-end-to-end delay.Furthermore,in literature,proposed techniques are verymuch complex which cannot be easily implemented in realworld applications.This issue leads to the development of lightweight energyefficient routing in flying-IoT networks.This paper addresses the energy conservation problem in flying-IoT.This paper presents a novel approach for the internet of flying vehicles using DSDV routing.ISH-DSDV gives the notion of bellman-ford algorithm consisting of routing updates,information broadcasting,and stale method.DSDV shows optimal results in comparison with other contemporary routing protocols.Nomadic mobility model is utilized in the scenario of flying networks to check the performance of routing protocols.展开更多
Great strides have been made to realistically deploy multiple Unmanned Aerial Vehicles(UAVs)within the commercial domain,which demands a proper coordination and reliable communication among the UAVs.UAVs suffer from l...Great strides have been made to realistically deploy multiple Unmanned Aerial Vehicles(UAVs)within the commercial domain,which demands a proper coordination and reliable communication among the UAVs.UAVs suffer from limited time of flight.Conventional techniques suffer from high delay,low throughput,and early node death due to aerial topology of UAV networks.To deal with these issues,this paper proposes a UAV parameter vector which considers node energy,channel state information and mobility of UAVs.By intelligently estimating the proposed parameter,the state of UAV can be predicted closely.Accordingly,efficient clustering may be achieved by using suitable metaheuristic techniques.In the current work,Elbow method has been used to determine optimal cluster count in the deployed FANET.The proposed UAV parameter vector is then integrated into two popular hybrid metaheuristic algorithms,namely,water cycle-moth flame optimization(WCMFO)and Grey Wolf-Particle Swarm optimization(GWPSO),thereby enhancing the lifespan of the system.A methodology based on the holistic approach of parameter and signal formulation,estimation model for intelligent clustering,and statistical parameters for performance analysis is carried out by the energy consumption of the network and the alive node analysis.Rigorous simulations are run to demonstrate node density variations to validate the theoretical developments for various proportions of network system sizes.The proposed method presents significant improvement over conventional stateof-the-art methods.展开更多
Flying ad hoc Networks(FANETs)have drawn people’s attention these years due to their wide range of civil and military applications.Due to the high mobility and limited battery capacity of unmanned aerial vehicles(UAV...Flying ad hoc Networks(FANETs)have drawn people’s attention these years due to their wide range of civil and military applications.Due to the high mobility and limited battery capacity of unmanned aerial vehicles(UAVs),it is difficult to exploit existing ad hoc network routing algorithms protocols in especially low-altitude complex environments with dense obstacles for FANETs.Therefore,this paper proposes a Q-learning-based visual information assisted routing(QVIR)algorithm for FANETs in low altitude complex environments,which could make use of the imaged data collected by the onboard camera to reduce the influence of flight environment on the network.Simulation results show that compared with the classical FANETs routing algorithm,the QVIR algorithm has better performance in terms of lower delay,packet delivery ratio,and energy efficiency.展开更多
Nowadays,flying ad hoc network(FANET)has captured great attention for its huge potential in military and civilian applications.However,the high-speed movement of unmanned aerial vehi-cles(UAVs)in three-dimensional(3D)...Nowadays,flying ad hoc network(FANET)has captured great attention for its huge potential in military and civilian applications.However,the high-speed movement of unmanned aerial vehi-cles(UAVs)in three-dimensional(3D)space leads to fast topology change in FANET and brings new challenges to traditional routing mechanisms.To improve the performance of packet trans-mission in the 3D high dynamic FANETs,we propose a 3D greedy perimeter stateless routing(GPSR)algorithm using adaptive Kalman prediction for FANETs with omnidirectional antenna(KOGPSR).Especially,in data forwarding part of the KOGPSR,we propose a new link metric for greedy forwarding based on a torus-shaped radiation pattern of the omnidirectional antenna of UAVs,and a restricted flooding strategy is introduced to solve the 3D void node problem in geographic routing.In addition,in order to enhance the accuracy of the location information of high dynamic UAVs,we design an adaptive Kalman algorithm to track and predict the motion of UAVs.Finally,a FANET simulation platform based on OPNET is built to depict the performance of the KOGPSR algorithm.The simulation results show that the proposed KOGPSR algorithm is more suitable for the actual 3D high dynamic FANET.展开更多
针对飞行自组网中最优化链路状态路由(Optimized Link State Routing,OLSR)协议在高速剧变的动态拓扑环境下由于传统多点中继(Multi Point Relay,MPR)机制冗余导致的路由开销大、时延较高等问题,提出了一种新的基于黑翅鸢算法(Black-win...针对飞行自组网中最优化链路状态路由(Optimized Link State Routing,OLSR)协议在高速剧变的动态拓扑环境下由于传统多点中继(Multi Point Relay,MPR)机制冗余导致的路由开销大、时延较高等问题,提出了一种新的基于黑翅鸢算法(Black-winged Kite Algorithm,BKA)的改进最优化链路状态协议BKA-OLSR。该算法通过模拟黑翅鸢高空盘旋搜索与俯冲攻击的仿生策略,构建双阶段优化机制。全局迁移阶段采用柯西扰动实现广域探索,局部攻击阶段通过正弦扰动进行精细开发。与基于贪婪策略的传统MPR方案相比,基于BKA算法的MPR方案生成的MPR集合规模平均减少34%,且能稳定实现100%2跳节点覆盖。与蚁群算法和细菌觅食算法等经典仿生算法相比,BKA在保证计算效果的同时,显著提升了计算速度。仿真结果表明,在高速动态拓扑环境下,BKA-OLSR在MPR数量、控制消息开销和端到端时延等关键性能指标上均优于传统OLSR协议。展开更多
This study investigates the design and implementation of Flying Ad Hoc Networks(FANETs),a network architec-ture inspired by the Mobile Ad Hoc Network(MANET)model,specifically tailored to support unmanned aerial vehicl...This study investigates the design and implementation of Flying Ad Hoc Networks(FANETs),a network architec-ture inspired by the Mobile Ad Hoc Network(MANET)model,specifically tailored to support unmanned aerial vehicles(UAVs).As UAVs increasingly contribute to diverse fields,from surveillance to delivery,FANETs have emerged as essential in ensuring stable,dynamic communication channels among drones in flight.This research adopts a dual approach,combining rigorous theoretical analysis with detailed practical simulations to assess the performance,adaptability,and efficiency of FANETs in varying conditions.The findings emphasize the ability of FANETs to manage network congestion effectively in densely populated areas,a critical feature for maintaining reliable communications in complex scenarios.Moreover,FANETs demonstrate high potential to support critical applications,such as emergency response,disaster management,and public safety operations,where quick and coordinated action is paramount.The study also underscores the importance of establishing a hierarchical structure among nodes within the network,which allows for more efficient data exchange and helps optimize the overall network performance.Through this work,significant insights are offered into the design principles that can enhance UAV communication networks,providing a foundation for the development of more resilient,scalable,and efficient technological solutions.These advancements could accelerate the deployment of UAVs across a variety of sectors,including logistics,agriculture,environmental monitoring,and more.As such,this study not only contributes to the field of ad hoc networking but also holds potential for transformative impacts across industries where UAVs play an increasingly central role,promoting greater integration and operational success.展开更多
Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic top...Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.展开更多
“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian services.The dynamic nature of F...“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian services.The dynamic nature of FANETs,with high mobility,quick node migration,and frequent topology changes,presents substantial hurdles for routing protocol development.Over the preceding few years,researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET,which is topology change and high mobility.This paper reviews current research on routing protocols and Machine Learning(ML)approaches applied to FANETs,emphasizing developments between 2021 and 2023.The research uses the PRISMA approach to sift through the literature,filtering results from the SCOPUS database to find 82 relevant publications.The research study uses machine learning-based routing algorithms to beat the issues of high mobility,dynamic topologies,and intermittent connection in FANETs.When compared with conventional routing,it gives an energy-efficient and fast decision-making solution in a real-time environment,with greater fault tolerance capabilities.These protocols aim to increase routing efficiency,flexibility,and network stability using ML’s predictive and adaptive capabilities.This comprehensive review seeks to integrate existing information,offer novel integration approaches,and recommend future research topics for improving routing efficiency and flexibility in FANETs.Moreover,the study highlights emerging trends in ML integration,discusses challenges faced during the review,and discusses overcoming these hurdles in future research.展开更多
Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control entity.In these networks,nodes function as senders,receivers,and routers.O...Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control entity.In these networks,nodes function as senders,receivers,and routers.One such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely controlled.With the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power batteries.An optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in FANET.This paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in FANET.The energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient nodes.Maintaining link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of IEE-DLSA.The simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing protocols.The evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.展开更多
The ever increasing demand of adhoc networks for adaptive topology and mobility aware communication led to new paradigm of networking among Unmanned Aerial Vehicles(UAVs)known as Flying ad-hoc Networks(FANETs).Due to ...The ever increasing demand of adhoc networks for adaptive topology and mobility aware communication led to new paradigm of networking among Unmanned Aerial Vehicles(UAVs)known as Flying ad-hoc Networks(FANETs).Due to their dynamic topology,FANETs can be deployed for disaster monitoring and surveillance applications.During these operations,UAVs need to transmit different disaster data,which consists of different types of data packets.Among them there are packets which need to be transmitted urgently because of the emergency situation in disaster management.To handle this situation,we propose a methodology of disaster data classification using urgency level and based on these urgency levels,priority index is assigned to data packets.An approach of Urgency Aware Scheduling(UAS)is proposed to efficiently transmit high and low priority packets with minimum delays in transmission queue.We take into account different scenarios of UAVs for disaster management and for N number of UAVs,we propose bio-inspired mechanism using behavioral study of bird flocking for cluster formation and maintenance.Furthermore,we propose a priority based route selection methodology for data communication in FANET cluster.Simulationresults show that our proposed mechanism shows better performance in the presence of evaluation benchmarks like average delay,queuing time,forward percentage and fairness.展开更多
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
基金the National Natural Science Foundation of China(NNSFC)(Grant Nos.72001213 and 72301292)the National Social Science Fund of China(Grant No.19BGL297)the Basic Research Program of Natural Science in Shaanxi Province(Grant No.2021JQ-369).
文摘Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.
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.
基金funding support for this work by the Department of Information Technology,College of Computer,Qassim University,Buraydah,Saudi Arabia.
文摘Flying ad hoc networks(FANETs)present a challenging environment due to the dynamic and highly mobile nature of the network.Dynamic network topology and uncertain node mobility structure of FANETs do not aim to consider only one path transmission.Several different techniques are adopted to address the issues arising in FANETs,from game theory to clustering to channel estimation and other statistical schemes.These approaches mostly employ traditional concepts for problem solutions.One of the novel approaches that provide simpler solutions to more complex problems is to use biologically inspired schemes.Several Nature-inspired schemes address cooperation and alliance which can be used to ensure connectivity among network nodes.One such species that resembles the dynamicity of FANETs are Bats.In this paper,the biologically inspired metaheuristic technique of the BAT Algorithm is proposed to present a routing protocol called iBATCOOP(Improved BAT Algorithm using Cooperation technique).We opt for the design implementation of the natural posture of bats to handle the necessary flying requirements.Moreover,we envision the concept of cooperative diversity using multiple relays and present an iBAT-COOP routing protocol for FANETs.This paper employs cooperation for an optimal route selection and reflects on distance,Signal to Noise Ratio(SNR),and link conditions to an efficient level to deal with FANET’s routing.By way of simulations,the performance of iBAT-COOP protocol outperforms BAT-FANET protocol and reduces packet loss ratio,end-to-end delay,and transmission loss by 81%,21%,and 82%respectively.Furthermore,the average link duration is improved by 25%compared to the BAT-FANET protocol.
基金This work was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(Grant No.NRF-2020R1I1A3074141)the Brain Research Program through the NRF funded by the Ministry of Science,ICT and Future Planning(Grant No.NRF-2019M3C7A1020406),and“Regional Innovation Strategy(RIS)”through the NRF funded by the Ministry of Education.
文摘Most interesting area is the growing demand of flying-IoT mergers with smart cities.However,aerial vehicles,especially unmanned aerial vehicles(UAVs),have limited capabilities for maintaining node energy efficiency.In order to communicate effectively,IoT is a key element for smart cities.While improving network performance,routing protocols can be deployed in flying-IoT to improve latency,packet drop rate,packet delivery,power utilization,and average-end-to-end delay.Furthermore,in literature,proposed techniques are verymuch complex which cannot be easily implemented in realworld applications.This issue leads to the development of lightweight energyefficient routing in flying-IoT networks.This paper addresses the energy conservation problem in flying-IoT.This paper presents a novel approach for the internet of flying vehicles using DSDV routing.ISH-DSDV gives the notion of bellman-ford algorithm consisting of routing updates,information broadcasting,and stale method.DSDV shows optimal results in comparison with other contemporary routing protocols.Nomadic mobility model is utilized in the scenario of flying networks to check the performance of routing protocols.
文摘Great strides have been made to realistically deploy multiple Unmanned Aerial Vehicles(UAVs)within the commercial domain,which demands a proper coordination and reliable communication among the UAVs.UAVs suffer from limited time of flight.Conventional techniques suffer from high delay,low throughput,and early node death due to aerial topology of UAV networks.To deal with these issues,this paper proposes a UAV parameter vector which considers node energy,channel state information and mobility of UAVs.By intelligently estimating the proposed parameter,the state of UAV can be predicted closely.Accordingly,efficient clustering may be achieved by using suitable metaheuristic techniques.In the current work,Elbow method has been used to determine optimal cluster count in the deployed FANET.The proposed UAV parameter vector is then integrated into two popular hybrid metaheuristic algorithms,namely,water cycle-moth flame optimization(WCMFO)and Grey Wolf-Particle Swarm optimization(GWPSO),thereby enhancing the lifespan of the system.A methodology based on the holistic approach of parameter and signal formulation,estimation model for intelligent clustering,and statistical parameters for performance analysis is carried out by the energy consumption of the network and the alive node analysis.Rigorous simulations are run to demonstrate node density variations to validate the theoretical developments for various proportions of network system sizes.The proposed method presents significant improvement over conventional stateof-the-art methods.
基金supported in part by the National Natural Science Foundation of China under Grants 62371158 and 62027802in part by the Major Key Project of PCL,Peng Cheng Laboratory under Grant PCL2024A01.
文摘Flying ad hoc Networks(FANETs)have drawn people’s attention these years due to their wide range of civil and military applications.Due to the high mobility and limited battery capacity of unmanned aerial vehicles(UAVs),it is difficult to exploit existing ad hoc network routing algorithms protocols in especially low-altitude complex environments with dense obstacles for FANETs.Therefore,this paper proposes a Q-learning-based visual information assisted routing(QVIR)algorithm for FANETs in low altitude complex environments,which could make use of the imaged data collected by the onboard camera to reduce the influence of flight environment on the network.Simulation results show that compared with the classical FANETs routing algorithm,the QVIR algorithm has better performance in terms of lower delay,packet delivery ratio,and energy efficiency.
基金supported in part by the Shaanxi Provincial Key Research and Development Programs(2022ZDLGY05-04,2022ZDLGY05-03,2023-ZDLGY-33,2021ZDLGY04-08)。
文摘Nowadays,flying ad hoc network(FANET)has captured great attention for its huge potential in military and civilian applications.However,the high-speed movement of unmanned aerial vehi-cles(UAVs)in three-dimensional(3D)space leads to fast topology change in FANET and brings new challenges to traditional routing mechanisms.To improve the performance of packet trans-mission in the 3D high dynamic FANETs,we propose a 3D greedy perimeter stateless routing(GPSR)algorithm using adaptive Kalman prediction for FANETs with omnidirectional antenna(KOGPSR).Especially,in data forwarding part of the KOGPSR,we propose a new link metric for greedy forwarding based on a torus-shaped radiation pattern of the omnidirectional antenna of UAVs,and a restricted flooding strategy is introduced to solve the 3D void node problem in geographic routing.In addition,in order to enhance the accuracy of the location information of high dynamic UAVs,we design an adaptive Kalman algorithm to track and predict the motion of UAVs.Finally,a FANET simulation platform based on OPNET is built to depict the performance of the KOGPSR algorithm.The simulation results show that the proposed KOGPSR algorithm is more suitable for the actual 3D high dynamic FANET.
文摘针对飞行自组网中最优化链路状态路由(Optimized Link State Routing,OLSR)协议在高速剧变的动态拓扑环境下由于传统多点中继(Multi Point Relay,MPR)机制冗余导致的路由开销大、时延较高等问题,提出了一种新的基于黑翅鸢算法(Black-winged Kite Algorithm,BKA)的改进最优化链路状态协议BKA-OLSR。该算法通过模拟黑翅鸢高空盘旋搜索与俯冲攻击的仿生策略,构建双阶段优化机制。全局迁移阶段采用柯西扰动实现广域探索,局部攻击阶段通过正弦扰动进行精细开发。与基于贪婪策略的传统MPR方案相比,基于BKA算法的MPR方案生成的MPR集合规模平均减少34%,且能稳定实现100%2跳节点覆盖。与蚁群算法和细菌觅食算法等经典仿生算法相比,BKA在保证计算效果的同时,显著提升了计算速度。仿真结果表明,在高速动态拓扑环境下,BKA-OLSR在MPR数量、控制消息开销和端到端时延等关键性能指标上均优于传统OLSR协议。
基金funded by Direccion General de Investigaciones of Universidad Santiago de Cali under call No.01-2024.
文摘This study investigates the design and implementation of Flying Ad Hoc Networks(FANETs),a network architec-ture inspired by the Mobile Ad Hoc Network(MANET)model,specifically tailored to support unmanned aerial vehicles(UAVs).As UAVs increasingly contribute to diverse fields,from surveillance to delivery,FANETs have emerged as essential in ensuring stable,dynamic communication channels among drones in flight.This research adopts a dual approach,combining rigorous theoretical analysis with detailed practical simulations to assess the performance,adaptability,and efficiency of FANETs in varying conditions.The findings emphasize the ability of FANETs to manage network congestion effectively in densely populated areas,a critical feature for maintaining reliable communications in complex scenarios.Moreover,FANETs demonstrate high potential to support critical applications,such as emergency response,disaster management,and public safety operations,where quick and coordinated action is paramount.The study also underscores the importance of establishing a hierarchical structure among nodes within the network,which allows for more efficient data exchange and helps optimize the overall network performance.Through this work,significant insights are offered into the design principles that can enhance UAV communication networks,providing a foundation for the development of more resilient,scalable,and efficient technological solutions.These advancements could accelerate the deployment of UAVs across a variety of sectors,including logistics,agriculture,environmental monitoring,and more.As such,this study not only contributes to the field of ad hoc networking but also holds potential for transformative impacts across industries where UAVs play an increasingly central role,promoting greater integration and operational success.
基金funded by Hung Yen University of Technology and Education under grand number UTEHY.L.2025.62.
文摘Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.
基金support the findings of this study are openly available in(Scopus database)at www.scopus.com(accessed on 07 January 2025).
文摘“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian services.The dynamic nature of FANETs,with high mobility,quick node migration,and frequent topology changes,presents substantial hurdles for routing protocol development.Over the preceding few years,researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET,which is topology change and high mobility.This paper reviews current research on routing protocols and Machine Learning(ML)approaches applied to FANETs,emphasizing developments between 2021 and 2023.The research uses the PRISMA approach to sift through the literature,filtering results from the SCOPUS database to find 82 relevant publications.The research study uses machine learning-based routing algorithms to beat the issues of high mobility,dynamic topologies,and intermittent connection in FANETs.When compared with conventional routing,it gives an energy-efficient and fast decision-making solution in a real-time environment,with greater fault tolerance capabilities.These protocols aim to increase routing efficiency,flexibility,and network stability using ML’s predictive and adaptive capabilities.This comprehensive review seeks to integrate existing information,offer novel integration approaches,and recommend future research topics for improving routing efficiency and flexibility in FANETs.Moreover,the study highlights emerging trends in ML integration,discusses challenges faced during the review,and discusses overcoming these hurdles in future research.
基金supported in part by the Chongqing Natural Science Foundation Innovation and Development Joint Foundation(No.CSTB2024NSCQ-LZX0035)Science and Technology Research Project of Chongqing Education Commission(No.KJZD-M202300605)+4 种基金Nanning“Yongjiang Plan”Youth Talent Project(RC20230107)Special General Project for Chongqing’s TechNological Innovation and Application Development(CSTB2022TIAD-GPX0028)Chongqing Natural Science Foundation Project(CSTB2022NSCQ-MSX0230)supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R 343)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia and the authors extend their appreciation to the Deanship of Scientific Research at Northern Border University,Arar,Kingdom of Saudi Arabia,for funding this research work through the Project Number“NBU-FFR2024-1092-07”.
文摘Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control entity.In these networks,nodes function as senders,receivers,and routers.One such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely controlled.With the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power batteries.An optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in FANET.This paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in FANET.The energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient nodes.Maintaining link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of IEE-DLSA.The simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing protocols.The evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.
基金supported in part by the Key Project of the National Natural Science Foundation of China under Grant 61431001in part by the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University,under Grant 2017D02+1 种基金in part by the Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education,Guilin University of Electronic Technologyin part by the Foundation of Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services
文摘The ever increasing demand of adhoc networks for adaptive topology and mobility aware communication led to new paradigm of networking among Unmanned Aerial Vehicles(UAVs)known as Flying ad-hoc Networks(FANETs).Due to their dynamic topology,FANETs can be deployed for disaster monitoring and surveillance applications.During these operations,UAVs need to transmit different disaster data,which consists of different types of data packets.Among them there are packets which need to be transmitted urgently because of the emergency situation in disaster management.To handle this situation,we propose a methodology of disaster data classification using urgency level and based on these urgency levels,priority index is assigned to data packets.An approach of Urgency Aware Scheduling(UAS)is proposed to efficiently transmit high and low priority packets with minimum delays in transmission queue.We take into account different scenarios of UAVs for disaster management and for N number of UAVs,we propose bio-inspired mechanism using behavioral study of bird flocking for cluster formation and maintenance.Furthermore,we propose a priority based route selection methodology for data communication in FANET cluster.Simulationresults show that our proposed mechanism shows better performance in the presence of evaluation benchmarks like average delay,queuing time,forward percentage and fairness.
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.
