UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we inve...UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we investigate current works about UAV coverage problem and propose a multi-UAV coverage model based on energy-efficient communication. The proposed model is decomposed into two steps: coverage maximization and power control, both are proved to be exact potential games(EPG) and have Nash equilibrium(NE) points. Then the multi-UAV energy-efficient coverage deployment algorithm based on spatial adaptive play(MUECD-SAP) is adopted to perform coverage maximization and power control, which guarantees optimal energy-efficient coverage deployment. Finally, simulation results show the effectiveness of our proposed approach, and confirm the reliability of proposed model.展开更多
The evolving“Industry 4.0”domain encompasses a collection of future industrial developments with cyber-physical systems(CPS),Internet of things(IoT),big data,cloud computing,etc.Besides,the industrial Internet of th...The evolving“Industry 4.0”domain encompasses a collection of future industrial developments with cyber-physical systems(CPS),Internet of things(IoT),big data,cloud computing,etc.Besides,the industrial Internet of things(IIoT)directs data from systems for monitoring and controlling the physical world to the data processing system.A major novelty of the IIoT is the unmanned aerial vehicles(UAVs),which are treated as an efficient remote sensing technique to gather data from large regions.UAVs are commonly employed in the industrial sector to solve several issues and help decision making.But the strict regulations leading to data privacy possibly hinder data sharing across autonomous UAVs.Federated learning(FL)becomes a recent advancement of machine learning(ML)which aims to protect user data.In this aspect,this study designs federated learning with blockchain assisted image classification model for clustered UAV networks(FLBIC-CUAV)on IIoT environment.The proposed FLBIC-CUAV technique involves three major processes namely clustering,blockchain enabled secure communication and FL based image classification.For UAV cluster construction process,beetle swarm optimization(BSO)algorithm with three input parameters is designed to cluster the UAVs for effective communication.In addition,blockchain enabled secure data transmission process take place to transmit the data from UAVs to cloud servers.Finally,the cloud server uses an FL with Residual Network model to carry out the image classification process.A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-CUAV approach.The experimental outcomes portrayed the betterment of the FLBIC-CUAV approach over the recent state of art methods.展开更多
In this paper,we investigate a backhaul framework jointly considering topology construction and power adjustment for self-organizing UAV networks.To enhance the backhaul rate with limited information exchange and avoi...In this paper,we investigate a backhaul framework jointly considering topology construction and power adjustment for self-organizing UAV networks.To enhance the backhaul rate with limited information exchange and avoid malicious power competition,we propose a deep reinforcement learning(DRL)based method to construct the backhaul framework where each UAV distributedly makes decisions.First,we decompose the backhaul framework into three submodules,i.e.,transmission target selection(TS),total power control(PC),and multi-channel power allocation(PA).Then,the three submodules are solved by heterogeneous DRL algorithms with tailored rewards to regulate UAVs’behaviors.In particular,TS is solved by deep-Q learning to construct topology with less relay and guarantee the backhaul rate.PC and PA are solved by deep deterministic policy gradient to match the traffic requirement with proper finegrained transmission power.As a result,the malicious power competition is alleviated,and the backhaul rate is further enhanced.Simulation results show that the proposed framework effectively achieves system-level and all-around performance gain compared with DQL and max-min method,i.e.,higher backhaul rate,lower transmission power,and fewer hop.展开更多
Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitor...Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitoring.Frequent topology changes,high mobility,and limited energy availability pose significant challenges to maintaining stable and high-performance routing.Traditional routing protocols,such as Ad hoc On-Demand Distance Vector(AODV),Load-Balanced Optimized Predictive Ad hoc Routing(LB-OPAR),and Destination-Sequenced Distance Vector(DSDV),often experience performance degradation under such conditions.To address these limitations,this study evaluates the effectiveness of Dynamic Adaptive Routing(DAR),a protocol designed to adapt routing decisions in real time based on network dynamics and resource constraints.The research utilizes the Network Simulator 3(NS-3)platform to conduct controlled simulations,measuring key performance indicators such as latency,Packet Delivery Ratio(PDR),energy consumption,and throughput.Comparative analysis reveals that DAR consistently outperforms conventional protocols,achieving a 20%-30% reduction in latency,a 25% decrease in energy consumption,and marked improvements in throughput and PDR.These results highlight DAR’s ability to maintain high communication reliability while optimizing resource usage in challenging operational scenarios.By providing empirical evidence of DAR’s advantages in highly dynamic UAV network environments,this study contributes to advancing adaptive routing strategies.The findings not only validate DAR’s robustness and scalability but also lay the groundwork for integrating artificial intelligence-driven decision-making and real-world UAV deployment.Future work will explore cross-layer optimization,multi-UAV coordination,and experimental validation in field trials,aiming to further enhance communication resilience and energy efficiency in next-generation aerial networks.展开更多
Unmanned aerial vehicles(UAVs)have become one of the key technologies to achieve future data collection due to their high mobility,rapid deployment,low cost,and the ability to establish line-of-sight communication lin...Unmanned aerial vehicles(UAVs)have become one of the key technologies to achieve future data collection due to their high mobility,rapid deployment,low cost,and the ability to establish line-of-sight communication links.However,when UAV swarm perform tasks in narrow spaces,they often encounter various spatial obstacles,building shielding materials,and high-speed node movements,which result in intermittent network communication links and cannot support the smooth comple-tion of tasks.In this paper,a high mobility and dynamic topol-ogy of the UAV swarm is particularly considered and the high dynamic mobile topology-based clustering(HDMTC)algorithm is proposed.Simulation and real flight verification results verify that the proposed HDMTC algorithm achieves higher stability of net-work,longer link expiration time(LET),and longer node lifetime,all of which improve the communication performance for UAV swarm networks.展开更多
The increasing importance of terminal privacy in the Unmanned Aerial Vehicle(UAV)network has led to a growing recognition of the crucial role of authentication technology in UAV network security.However,traditional au...The increasing importance of terminal privacy in the Unmanned Aerial Vehicle(UAV)network has led to a growing recognition of the crucial role of authentication technology in UAV network security.However,traditional authentication approaches are vulnerable due to the transmission of identity information between UAVs and cryptographic paradigm management centers over a public channel.These vulnerabilities include brute-force attacks,single point of failure,and information leakage.Blockchain,as a decentralized distributed ledger with blockchain storage,tamper-proof,secure,and trustworthy features,can solve problems such as single-point-of-failure and trust issues,while the hidden communication in the physical layer can effectively resist information leakage and violent attacks.In this paper,we propose a lightweight UAV network authentication mechanism that leverages blockchain and covert communication,where the identity information is transmitted as covert tags carried by normal modulated signals.In addition,a weight-based Practical Byzantine Fault-Tolerant(wPBFT)consensus protocol is devised,where the weights are determined by the channel states of UAVs and the outcomes of past authentication scenarios.Simulation results demonstrate that the proposed mechanism outperforms traditional benchmarks in terms of security and robustness,particularly under conditions of low Signal-to-Noise Ratio(SNR)and short tag length.展开更多
Many extensive UAV communication networks have used UAV cooperative control.Wireless networking services can be offered using unmanned aerial vehicles(UAVs)as aerial base stations.Not only is coverage maximization,but...Many extensive UAV communication networks have used UAV cooperative control.Wireless networking services can be offered using unmanned aerial vehicles(UAVs)as aerial base stations.Not only is coverage maximization,but also better connectivity,a fundamental design challenge that must be solved.The number of applications for unmanned aerial vehicles(UAVs)operating in unlicensed bands is fast expanding as the Internet of Things(IoT)develops.Those bands,however,have become overcrowded as the number of systems that use them grows.Cognitive Radio(CR)and spectrum allocation approaches have emerged as a potential approach for resolving spectrum scarcity in wireless networks,and hence as technological solutions for future generations,from this perspective.As a result,combining CR with UAVs has the potential to give significant benefits for large-scale UAV deployment.The paper examines existing research on the subject of UAV covering and proposes a multi-UAV cognitive-based error-free model for energy-efficient communication.Coverage maximization,power control,and enhanced connection quality are the three steps of the proposed model.To satisfy the desired signal-to-noise ratio,the covering zone efficacy is investigated as a function of the distance among UAVs stationed in a specific geographic region depending on multiple deployment configurations like as rural,suburban,and urban macro deployment scenarios of the ITU-R M.2135 standard(SNR).展开更多
The sixth-generation(6G)wireless communication networks are anticipated in integrating aerial,terrestrial,and maritime communication into a robust system to accomplish trustworthy,quick,and low latency needs.It enable...The sixth-generation(6G)wireless communication networks are anticipated in integrating aerial,terrestrial,and maritime communication into a robust system to accomplish trustworthy,quick,and low latency needs.It enables to achieve maximum throughput and delay for several applications.Besides,the evolution of 6G leads to the design of unmanned aerial vehicles(UAVs)in providing inexpensive and effective solutions in various application areas such as healthcare,environment monitoring,and so on.In the UAV network,effective data collection with restricted energy capacity poses a major issue to achieving high quality network communication.It can be addressed by the use of clustering techniques forUAVs in 6G networks.In this aspect,this study develops a novel metaheuristic based energy efficient data gathering scheme for clustered unmanned aerial vehicles(MEEDG-CUAV).The proposed MEEDG-CUAV technique intends in partitioning the UAV networks into various clusters and assign a cluster head(CH)to reduce the overall energy utilization.Besides,the quantum chaotic butterfly optimization algorithm(QCBOA)with a fitness function is derived to choose CHs and construct clusters.The experimental validation of the MEEDG-CUAV technique occurs utilizing benchmark dataset and the experimental results highlighted the better performance over the other state of art techniques interms of different measures.展开更多
Capable of flexibly supporting diverse applications and providing computation services,the Mobile Edge Computing(MEC)-assisted Unmanned Aerial Vehicle(UAV)network is emerging as an innovational paradigm.In this paradi...Capable of flexibly supporting diverse applications and providing computation services,the Mobile Edge Computing(MEC)-assisted Unmanned Aerial Vehicle(UAV)network is emerging as an innovational paradigm.In this paradigm,the heterogeneous resources of the network,including computing and communication resources,should be allocated properly to reduce computation and communication latency as well as energy consumption.However,most existing works solely focus on the optimization issues with global information,which is generally difficult to obtain in real-world scenarios.In this paper,fully considering the incomplete information resulting from diverse types of tasks,we study the joint task offloading and spectrum allocation problem in UAV network,where free UAV nodes serve as helpers for cooperative computation.The objective is to jointly optimize offloading mode,collaboration pairing,and channel allocation to minimize the weighted network cost.To achieve the purpose with only partial observation,an extensive-form game is introduced to reformulate the problem,and a regret learning-based scheme is proposed to achieve the equilibrium solution.With retrospective improvement property and information set concept,the designed algorithm is capable of combating incomplete information and obtaining more precise allocation patterns for diverse tasks.Numerical results show that our proposed algorithm outperforms the benchmarks across various settings.展开更多
As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerou...As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerous advantages,resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently.Specifically,due to the inherent requirements and future development trend,distributed resource management is suitable.In this article,we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner.By exploring the inherent features,the distinctive challenges are discussed.Then,we explore several gametheoretic models that not only combat the challenges but also have broad application prospects.We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks.Specifically,mean-field game,graphical game,Stackelberg game,coalition game and potential game are included.After that,we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models.Finally,we give some future research directions to shed light on future opportunities and applications.展开更多
This work focuses on an unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) system based on device-to-device(D2D) communication. In this system, the UAV exhibits caching,computing and relaying capabilities...This work focuses on an unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) system based on device-to-device(D2D) communication. In this system, the UAV exhibits caching,computing and relaying capabilities to periodically provide specific service to cellular users and D2D receiver nodes in the appointed time slot. Besides, the D2D transmitter can provide additional caching services to D2D receiver to reduce the pressure of the UAV. Note that communication between multi-type nodes is mutually restricted and different links share spectrum resources. To achieve an improved balance between different types of node, we aim to maximize the overall energy efficiency while satisfying the quality-of-service requirements of the cellular nodes.To address this problem, we propose an alternating iteration algorithm to jointly optimize the scheduling strategies of the user, transmitting power of the UAV and D2D-TX nodes, and UAV trajectory. The successive convex approximation, penalty function, and Dinkelbach method are employed to transform the original problem into a group of solvable subproblems and the convergence of the method is proved. Simulation results show that the proposed scheme performs better than other benchmark algorithms, particularly in terms of balancing the tradeoff between minimizing UAV energy consumption and maximizing throughput.展开更多
With the rapid increase of Unmanned Aircraft Vehicle(UAV) numbers,the contradiction between extensive flight demands and limited low-altitude airspace resources has become increasingly prominent.To ensure the safety a...With the rapid increase of Unmanned Aircraft Vehicle(UAV) numbers,the contradiction between extensive flight demands and limited low-altitude airspace resources has become increasingly prominent.To ensure the safety and efficiency of low-altitude UAV operations,the low-altitude UAV public air route creatively proposed by the Chinese Academy of Sciences(CAS) and supported by the Civil Aviation Administration of China(CAAC) has been gradually recognized.However,present planning research on UAV low-altitude air route is not enough to explore how to use the ground transportation infrastructure,how to closely combine the surface pattern characteristics,and how to form the mechanism of "network".Based on the solution proposed in the early stage and related researches,this paper further deepens the exploration of the low-altitude public air route network and the implementation of key technologies and steps with an actual case study in Tianjin,China.Firstly,a path-planning environment consisting of favorable spaces,obstacle spaces,and mobile communication spaces for UAV flights was pre-constructed.Subsequently,air routes were planned by using the conflict detection and path re-planning algorithm.Our study also assessed the network by computing the population exposure risk index(PERI) and found that the index value was greatly reduced after the construction of the network,indicating that the network can effectively reduce the operational risk.In this study,a low-altitude UAV air route network in an actual region was constructed using multidisciplinary approaches such as remote sensing,geographic information,aviation,and transportation;it indirectly verified the rationality of the outcomes.This can provide practical solutions to low-altitude traffic problems in urban areas.展开更多
Due to flexible deployment,unmanned aerial vehicle(UAV)mounted aerial access points are capable of expanding the coverage capabilities of existing terrestrial base stations(TBSs).Different from TBSs,however,UAV access...Due to flexible deployment,unmanned aerial vehicle(UAV)mounted aerial access points are capable of expanding the coverage capabilities of existing terrestrial base stations(TBSs).Different from TBSs,however,UAV access points(UAPs)are of high mobility in horizontal and vertical dimensions,which may deteriorate the coverage performance.Worsestill,the mobility of UAPs would as well increase the pressure of wireless backhaul.In this light,we investigate the performance of the cache-enabled UAV communications network(CUCN)in terms of network spatial throughput(ST)by analyzing the line of sight(LoS)connections and non-line of sight(NLoS)connections.It is found that the network ST is exponentially decreased with the square of UAP altitude.Furthermore,contrary to intuition,a large cache size may deteriorate the network ST when UAPs are over-deployed.The reason is that a large cache size increases the hit probability,which may increase the activation of UAPs and consequently result in complicated interference.Aiming to maximize the network ST,we optimize the cache strategy under limited backhaul.Remarkably,the results show that network ST could be substantially improved by the optimized cache strategy and the performance degeneration brought by UAP high mobility could be even eliminated especially when the UAP altitude is high.展开更多
An abundance of data from seismic and geodetic monitoring has provided new insight into dyke propagation and emplacement mechanisms.These studies show that faulting and fracturing is part of the magma
<div style="text-align:justify;"> Due to its air superiority and high mobility, unmanned aerial vehicle (UAV) can obtain better line-of-sight (LoS) link transmission channel. Therefore, UAV assisted da...<div style="text-align:justify;"> Due to its air superiority and high mobility, unmanned aerial vehicle (UAV) can obtain better line-of-sight (LoS) link transmission channel. Therefore, UAV assisted data collection for wireless sensor networks (WSNs) has become an important research direction. This paper intends to minimize the loss of WSNs for the robust data acquisition and communication assisted by UAV under the imperfect channel state information (CSI). On the premise of ensuring the completion of the communication task, we jointly optimize the wake-up schedule of SNs and the flight trajectory of the UAV, by considering the flight speed of the UAV and the sparse access of all sensor nodes (SNs) in WSN. Because the formulated optimization problem is a mixed integer nonconvex problem, we decompose the original problem into the efficient suboptimal solutions to overcome the difficulty of the optimization. Finally, the number of access node corresponding to the optimized operation time and access efficiency is induced for the entire WSN system efficiency improving. The simulation shows the performance gains of our proposed scheme and the influences of the system parameters are analyzed. </div>展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 61771488in part by the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province under Grant No. BK20160034+1 种基金 in part by the Open Research Foundation of Science and Technology on Communication Networks Laboratorythe Guang Xi Universities Key Laboratory Fund of Embedded Technology and Intelligent System (Guilin University of Technology)
文摘UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we investigate current works about UAV coverage problem and propose a multi-UAV coverage model based on energy-efficient communication. The proposed model is decomposed into two steps: coverage maximization and power control, both are proved to be exact potential games(EPG) and have Nash equilibrium(NE) points. Then the multi-UAV energy-efficient coverage deployment algorithm based on spatial adaptive play(MUECD-SAP) is adopted to perform coverage maximization and power control, which guarantees optimal energy-efficient coverage deployment. Finally, simulation results show the effectiveness of our proposed approach, and confirm the reliability of proposed model.
基金We deeply acknowledge Taif University for supporting this research through Taif University Researchers Supporting Project Number(TURSP-2020/328),Taif University,Taif,Saudi Arabia.
文摘The evolving“Industry 4.0”domain encompasses a collection of future industrial developments with cyber-physical systems(CPS),Internet of things(IoT),big data,cloud computing,etc.Besides,the industrial Internet of things(IIoT)directs data from systems for monitoring and controlling the physical world to the data processing system.A major novelty of the IIoT is the unmanned aerial vehicles(UAVs),which are treated as an efficient remote sensing technique to gather data from large regions.UAVs are commonly employed in the industrial sector to solve several issues and help decision making.But the strict regulations leading to data privacy possibly hinder data sharing across autonomous UAVs.Federated learning(FL)becomes a recent advancement of machine learning(ML)which aims to protect user data.In this aspect,this study designs federated learning with blockchain assisted image classification model for clustered UAV networks(FLBIC-CUAV)on IIoT environment.The proposed FLBIC-CUAV technique involves three major processes namely clustering,blockchain enabled secure communication and FL based image classification.For UAV cluster construction process,beetle swarm optimization(BSO)algorithm with three input parameters is designed to cluster the UAVs for effective communication.In addition,blockchain enabled secure data transmission process take place to transmit the data from UAVs to cloud servers.Finally,the cloud server uses an FL with Residual Network model to carry out the image classification process.A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-CUAV approach.The experimental outcomes portrayed the betterment of the FLBIC-CUAV approach over the recent state of art methods.
文摘In this paper,we investigate a backhaul framework jointly considering topology construction and power adjustment for self-organizing UAV networks.To enhance the backhaul rate with limited information exchange and avoid malicious power competition,we propose a deep reinforcement learning(DRL)based method to construct the backhaul framework where each UAV distributedly makes decisions.First,we decompose the backhaul framework into three submodules,i.e.,transmission target selection(TS),total power control(PC),and multi-channel power allocation(PA).Then,the three submodules are solved by heterogeneous DRL algorithms with tailored rewards to regulate UAVs’behaviors.In particular,TS is solved by deep-Q learning to construct topology with less relay and guarantee the backhaul rate.PC and PA are solved by deep deterministic policy gradient to match the traffic requirement with proper finegrained transmission power.As a result,the malicious power competition is alleviated,and the backhaul rate is further enhanced.Simulation results show that the proposed framework effectively achieves system-level and all-around performance gain compared with DQL and max-min method,i.e.,higher backhaul rate,lower transmission power,and fewer hop.
文摘Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitoring.Frequent topology changes,high mobility,and limited energy availability pose significant challenges to maintaining stable and high-performance routing.Traditional routing protocols,such as Ad hoc On-Demand Distance Vector(AODV),Load-Balanced Optimized Predictive Ad hoc Routing(LB-OPAR),and Destination-Sequenced Distance Vector(DSDV),often experience performance degradation under such conditions.To address these limitations,this study evaluates the effectiveness of Dynamic Adaptive Routing(DAR),a protocol designed to adapt routing decisions in real time based on network dynamics and resource constraints.The research utilizes the Network Simulator 3(NS-3)platform to conduct controlled simulations,measuring key performance indicators such as latency,Packet Delivery Ratio(PDR),energy consumption,and throughput.Comparative analysis reveals that DAR consistently outperforms conventional protocols,achieving a 20%-30% reduction in latency,a 25% decrease in energy consumption,and marked improvements in throughput and PDR.These results highlight DAR’s ability to maintain high communication reliability while optimizing resource usage in challenging operational scenarios.By providing empirical evidence of DAR’s advantages in highly dynamic UAV network environments,this study contributes to advancing adaptive routing strategies.The findings not only validate DAR’s robustness and scalability but also lay the groundwork for integrating artificial intelligence-driven decision-making and real-world UAV deployment.Future work will explore cross-layer optimization,multi-UAV coordination,and experimental validation in field trials,aiming to further enhance communication resilience and energy efficiency in next-generation aerial networks.
基金supported by the National Key Research and Development Program of China(2024YFB4504500)Shanghai Collaborative Innovation Project(24xtcx00500).
文摘Unmanned aerial vehicles(UAVs)have become one of the key technologies to achieve future data collection due to their high mobility,rapid deployment,low cost,and the ability to establish line-of-sight communication links.However,when UAV swarm perform tasks in narrow spaces,they often encounter various spatial obstacles,building shielding materials,and high-speed node movements,which result in intermittent network communication links and cannot support the smooth comple-tion of tasks.In this paper,a high mobility and dynamic topol-ogy of the UAV swarm is particularly considered and the high dynamic mobile topology-based clustering(HDMTC)algorithm is proposed.Simulation and real flight verification results verify that the proposed HDMTC algorithm achieves higher stability of net-work,longer link expiration time(LET),and longer node lifetime,all of which improve the communication performance for UAV swarm networks.
基金supported by the Hainan Province Science and Technology Special Fund,China(No.ZDYF2024GXJS292).
文摘The increasing importance of terminal privacy in the Unmanned Aerial Vehicle(UAV)network has led to a growing recognition of the crucial role of authentication technology in UAV network security.However,traditional authentication approaches are vulnerable due to the transmission of identity information between UAVs and cryptographic paradigm management centers over a public channel.These vulnerabilities include brute-force attacks,single point of failure,and information leakage.Blockchain,as a decentralized distributed ledger with blockchain storage,tamper-proof,secure,and trustworthy features,can solve problems such as single-point-of-failure and trust issues,while the hidden communication in the physical layer can effectively resist information leakage and violent attacks.In this paper,we propose a lightweight UAV network authentication mechanism that leverages blockchain and covert communication,where the identity information is transmitted as covert tags carried by normal modulated signals.In addition,a weight-based Practical Byzantine Fault-Tolerant(wPBFT)consensus protocol is devised,where the weights are determined by the channel states of UAVs and the outcomes of past authentication scenarios.Simulation results demonstrate that the proposed mechanism outperforms traditional benchmarks in terms of security and robustness,particularly under conditions of low Signal-to-Noise Ratio(SNR)and short tag length.
基金Ahmed Alhussen would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2022-193.
文摘Many extensive UAV communication networks have used UAV cooperative control.Wireless networking services can be offered using unmanned aerial vehicles(UAVs)as aerial base stations.Not only is coverage maximization,but also better connectivity,a fundamental design challenge that must be solved.The number of applications for unmanned aerial vehicles(UAVs)operating in unlicensed bands is fast expanding as the Internet of Things(IoT)develops.Those bands,however,have become overcrowded as the number of systems that use them grows.Cognitive Radio(CR)and spectrum allocation approaches have emerged as a potential approach for resolving spectrum scarcity in wireless networks,and hence as technological solutions for future generations,from this perspective.As a result,combining CR with UAVs has the potential to give significant benefits for large-scale UAV deployment.The paper examines existing research on the subject of UAV covering and proposes a multi-UAV cognitive-based error-free model for energy-efficient communication.Coverage maximization,power control,and enhanced connection quality are the three steps of the proposed model.To satisfy the desired signal-to-noise ratio,the covering zone efficacy is investigated as a function of the distance among UAVs stationed in a specific geographic region depending on multiple deployment configurations like as rural,suburban,and urban macro deployment scenarios of the ITU-R M.2135 standard(SNR).
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 1/279/42).www.kku.edu.sa.
文摘The sixth-generation(6G)wireless communication networks are anticipated in integrating aerial,terrestrial,and maritime communication into a robust system to accomplish trustworthy,quick,and low latency needs.It enables to achieve maximum throughput and delay for several applications.Besides,the evolution of 6G leads to the design of unmanned aerial vehicles(UAVs)in providing inexpensive and effective solutions in various application areas such as healthcare,environment monitoring,and so on.In the UAV network,effective data collection with restricted energy capacity poses a major issue to achieving high quality network communication.It can be addressed by the use of clustering techniques forUAVs in 6G networks.In this aspect,this study develops a novel metaheuristic based energy efficient data gathering scheme for clustered unmanned aerial vehicles(MEEDG-CUAV).The proposed MEEDG-CUAV technique intends in partitioning the UAV networks into various clusters and assign a cluster head(CH)to reduce the overall energy utilization.Besides,the quantum chaotic butterfly optimization algorithm(QCBOA)with a fitness function is derived to choose CHs and construct clusters.The experimental validation of the MEEDG-CUAV technique occurs utilizing benchmark dataset and the experimental results highlighted the better performance over the other state of art techniques interms of different measures.
基金supported by the Natural Science Foundation of China(NSFC)under Grant 62101051the Guangdong Key Laboratory of Intelligent Information ProcessingShenzhen Key Laboratory of Media Security,Shenzhen 518060,China。
文摘Capable of flexibly supporting diverse applications and providing computation services,the Mobile Edge Computing(MEC)-assisted Unmanned Aerial Vehicle(UAV)network is emerging as an innovational paradigm.In this paradigm,the heterogeneous resources of the network,including computing and communication resources,should be allocated properly to reduce computation and communication latency as well as energy consumption.However,most existing works solely focus on the optimization issues with global information,which is generally difficult to obtain in real-world scenarios.In this paper,fully considering the incomplete information resulting from diverse types of tasks,we study the joint task offloading and spectrum allocation problem in UAV network,where free UAV nodes serve as helpers for cooperative computation.The objective is to jointly optimize offloading mode,collaboration pairing,and channel allocation to minimize the weighted network cost.To achieve the purpose with only partial observation,an extensive-form game is introduced to reformulate the problem,and a regret learning-based scheme is proposed to achieve the equilibrium solution.With retrospective improvement property and information set concept,the designed algorithm is capable of combating incomplete information and obtaining more precise allocation patterns for diverse tasks.Numerical results show that our proposed algorithm outperforms the benchmarks across various settings.
基金This work was supported by National Key R&D Program of China under Grant 2018YFB1800802in part by the National Natural Science Foundation of China under Grant No.61771488,No.61631020 and No.61827801+1 种基金in part by State Key Laboratory of Air Traffic Management System and Technology under Grant No.SKLATM201808in part by Postgraduate Research and Practice Innovation Program of Jiangsu Province under No.KYCX190188.
文摘As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerous advantages,resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently.Specifically,due to the inherent requirements and future development trend,distributed resource management is suitable.In this article,we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner.By exploring the inherent features,the distinctive challenges are discussed.Then,we explore several gametheoretic models that not only combat the challenges but also have broad application prospects.We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks.Specifically,mean-field game,graphical game,Stackelberg game,coalition game and potential game are included.After that,we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models.Finally,we give some future research directions to shed light on future opportunities and applications.
基金the supports from the National Natural Science Foundation of China (61571156)Basic Research Project of Shenzhen (JCYJ20170413110004682 and JCYJ20150403161923521)。
文摘This work focuses on an unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) system based on device-to-device(D2D) communication. In this system, the UAV exhibits caching,computing and relaying capabilities to periodically provide specific service to cellular users and D2D receiver nodes in the appointed time slot. Besides, the D2D transmitter can provide additional caching services to D2D receiver to reduce the pressure of the UAV. Note that communication between multi-type nodes is mutually restricted and different links share spectrum resources. To achieve an improved balance between different types of node, we aim to maximize the overall energy efficiency while satisfying the quality-of-service requirements of the cellular nodes.To address this problem, we propose an alternating iteration algorithm to jointly optimize the scheduling strategies of the user, transmitting power of the UAV and D2D-TX nodes, and UAV trajectory. The successive convex approximation, penalty function, and Dinkelbach method are employed to transform the original problem into a group of solvable subproblems and the convergence of the method is proved. Simulation results show that the proposed scheme performs better than other benchmark algorithms, particularly in terms of balancing the tradeoff between minimizing UAV energy consumption and maximizing throughput.
基金National Key Research and Development Program of China,No.2017YFB0503005Key Research Program of the Chinese Academy of Sciences,No.ZDRW-KT-2020-2+1 种基金National Natural Science Foundation of China,No.41971359,No.41771388Tianjin Intelligent Manufacturing Project Technology of Intelligent Networking by Autonomous Control UAVs for Observation and Application,No.Tianjin-IMP-2。
文摘With the rapid increase of Unmanned Aircraft Vehicle(UAV) numbers,the contradiction between extensive flight demands and limited low-altitude airspace resources has become increasingly prominent.To ensure the safety and efficiency of low-altitude UAV operations,the low-altitude UAV public air route creatively proposed by the Chinese Academy of Sciences(CAS) and supported by the Civil Aviation Administration of China(CAAC) has been gradually recognized.However,present planning research on UAV low-altitude air route is not enough to explore how to use the ground transportation infrastructure,how to closely combine the surface pattern characteristics,and how to form the mechanism of "network".Based on the solution proposed in the early stage and related researches,this paper further deepens the exploration of the low-altitude public air route network and the implementation of key technologies and steps with an actual case study in Tianjin,China.Firstly,a path-planning environment consisting of favorable spaces,obstacle spaces,and mobile communication spaces for UAV flights was pre-constructed.Subsequently,air routes were planned by using the conflict detection and path re-planning algorithm.Our study also assessed the network by computing the population exposure risk index(PERI) and found that the index value was greatly reduced after the construction of the network,indicating that the network can effectively reduce the operational risk.In this study,a low-altitude UAV air route network in an actual region was constructed using multidisciplinary approaches such as remote sensing,geographic information,aviation,and transportation;it indirectly verified the rationality of the outcomes.This can provide practical solutions to low-altitude traffic problems in urban areas.
基金supported in part by National Key Research and Development Program of China (Grant No. 2020YFB1807001)in part by Natural Science Foundation of China (Grant No. 62171344, 62121001, 61725103, 61931005)+1 种基金in part by Young Elite Scientists Sponsorship Program by CASTin part by Key Industry Innovation Chain of Shaanxi (Grant No. 2022ZDLGY05-01, 2022ZDLGY05-06)
文摘Due to flexible deployment,unmanned aerial vehicle(UAV)mounted aerial access points are capable of expanding the coverage capabilities of existing terrestrial base stations(TBSs).Different from TBSs,however,UAV access points(UAPs)are of high mobility in horizontal and vertical dimensions,which may deteriorate the coverage performance.Worsestill,the mobility of UAPs would as well increase the pressure of wireless backhaul.In this light,we investigate the performance of the cache-enabled UAV communications network(CUCN)in terms of network spatial throughput(ST)by analyzing the line of sight(LoS)connections and non-line of sight(NLoS)connections.It is found that the network ST is exponentially decreased with the square of UAP altitude.Furthermore,contrary to intuition,a large cache size may deteriorate the network ST when UAPs are over-deployed.The reason is that a large cache size increases the hit probability,which may increase the activation of UAPs and consequently result in complicated interference.Aiming to maximize the network ST,we optimize the cache strategy under limited backhaul.Remarkably,the results show that network ST could be substantially improved by the optimized cache strategy and the performance degeneration brought by UAP high mobility could be even eliminated especially when the UAP altitude is high.
文摘An abundance of data from seismic and geodetic monitoring has provided new insight into dyke propagation and emplacement mechanisms.These studies show that faulting and fracturing is part of the magma
文摘<div style="text-align:justify;"> Due to its air superiority and high mobility, unmanned aerial vehicle (UAV) can obtain better line-of-sight (LoS) link transmission channel. Therefore, UAV assisted data collection for wireless sensor networks (WSNs) has become an important research direction. This paper intends to minimize the loss of WSNs for the robust data acquisition and communication assisted by UAV under the imperfect channel state information (CSI). On the premise of ensuring the completion of the communication task, we jointly optimize the wake-up schedule of SNs and the flight trajectory of the UAV, by considering the flight speed of the UAV and the sparse access of all sensor nodes (SNs) in WSN. Because the formulated optimization problem is a mixed integer nonconvex problem, we decompose the original problem into the efficient suboptimal solutions to overcome the difficulty of the optimization. Finally, the number of access node corresponding to the optimized operation time and access efficiency is induced for the entire WSN system efficiency improving. The simulation shows the performance gains of our proposed scheme and the influences of the system parameters are analyzed. </div>
