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
针对飞行自组网(Flying Ad Hoc Network,FANET)在通信空白场景下存在的高时延问题,提出了一种深度强化学习(Deep Reinforcement Learning,DRL)辅助的双跳信息增强路由协议(Double-Hop Information Enhanced Routing Protocol,DHRP)。为...针对飞行自组网(Flying Ad Hoc Network,FANET)在通信空白场景下存在的高时延问题,提出了一种深度强化学习(Deep Reinforcement Learning,DRL)辅助的双跳信息增强路由协议(Double-Hop Information Enhanced Routing Protocol,DHRP)。为了实现有效的路由决策,采用马尔可夫决策过程(Markov Decision Process,MDP)对路由行为进行建模,在状态空间设计中结合了节点位置信息与链路信道容量,并综合考虑了双跳范围内的网络信息,以深度值网络为核心,在融合实时网络状态动态调整机制的奖励函数引导下,做出最优下一跳路由决策。实验结果表明,在通信空白场景下,DHRP相较于现有的路由方案,显著降低了FANET的平均端到端时延。此外,在不同节点规模和网络拥塞条件下,DHRP均表现出优越的适应性和鲁棒性,通过对动态网络环境的实时感知与智能决策机制,有效保障了整体网络性能。展开更多
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
This article shows the quality of services in a wireless swarm of drones that form an ad hoc network between them Fly Ad Hoc Networks(FANET).Each drone has the ability to send and receive information(like a router);an...This article shows the quality of services in a wireless swarm of drones that form an ad hoc network between them Fly Ad Hoc Networks(FANET).Each drone has the ability to send and receive information(like a router);and can behave as a hierarchical node whit the intregration of three protocols:Multiprotocol Label Switch(MPLS),Fast Hierarchical AD Hoc Mobile(FHAM)and Internet Protocol version 6(IPv6),in conclusion MPLS+FHAM+IPv6.The metrics analyzed in the FANET are:delay,jitter,throughput,lost and sent packets/received.Testing process was carried out with swarms composed of 10,20,30 and 40 units;In this work,the stage with 40 droneswas analyzed showing registration processes,and sentmessages sequences between different drones that were part of the same swarm.A special analysis about the traffic between drones(end-to-end)was carried out,as well as the possible security flaws in each drone and the current status and future trends in real services.Regarding future trends,in a real environment,we took as a starting point,metrics results obtained in the simulation(positive according to the obtained results).These results gave us a clear vision of how the network will behave in a real environment with the aim to carry out the experiment on a physical level in the near future.This work also shows the experience quality from the service quality metrics obtained through a mathematical model.This quality of experience model will allow us to use it objectively in the agricultural sector,which is a great interest area and is where we are working with drones.Finally in this article we show our advances for a business model applied to the aforementioned agricultural sector,as well as the data analysis and services available to the end customer.These services available to the end customer have been classified into a basic,medium,advanced and plus level.展开更多
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.
文摘针对飞行自组网(Flying Ad Hoc Network,FANET)在通信空白场景下存在的高时延问题,提出了一种深度强化学习(Deep Reinforcement Learning,DRL)辅助的双跳信息增强路由协议(Double-Hop Information Enhanced Routing Protocol,DHRP)。为了实现有效的路由决策,采用马尔可夫决策过程(Markov Decision Process,MDP)对路由行为进行建模,在状态空间设计中结合了节点位置信息与链路信道容量,并综合考虑了双跳范围内的网络信息,以深度值网络为核心,在融合实时网络状态动态调整机制的奖励函数引导下,做出最优下一跳路由决策。实验结果表明,在通信空白场景下,DHRP相较于现有的路由方案,显著降低了FANET的平均端到端时延。此外,在不同节点规模和网络拥塞条件下,DHRP均表现出优越的适应性和鲁棒性,通过对动态网络环境的实时感知与智能决策机制,有效保障了整体网络性能。
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This research has been funded by Dirección General de Investigaciones of Universidad Santiago de Cali under Call No.01-2021.
文摘This article shows the quality of services in a wireless swarm of drones that form an ad hoc network between them Fly Ad Hoc Networks(FANET).Each drone has the ability to send and receive information(like a router);and can behave as a hierarchical node whit the intregration of three protocols:Multiprotocol Label Switch(MPLS),Fast Hierarchical AD Hoc Mobile(FHAM)and Internet Protocol version 6(IPv6),in conclusion MPLS+FHAM+IPv6.The metrics analyzed in the FANET are:delay,jitter,throughput,lost and sent packets/received.Testing process was carried out with swarms composed of 10,20,30 and 40 units;In this work,the stage with 40 droneswas analyzed showing registration processes,and sentmessages sequences between different drones that were part of the same swarm.A special analysis about the traffic between drones(end-to-end)was carried out,as well as the possible security flaws in each drone and the current status and future trends in real services.Regarding future trends,in a real environment,we took as a starting point,metrics results obtained in the simulation(positive according to the obtained results).These results gave us a clear vision of how the network will behave in a real environment with the aim to carry out the experiment on a physical level in the near future.This work also shows the experience quality from the service quality metrics obtained through a mathematical model.This quality of experience model will allow us to use it objectively in the agricultural sector,which is a great interest area and is where we are working with drones.Finally in this article we show our advances for a business model applied to the aforementioned agricultural sector,as well as the data analysis and services available to the end customer.These services available to the end customer have been classified into a basic,medium,advanced and plus level.
