The Backscatter communication has gained widespread attention from academia and industry in recent years. In this paper, A method of resource allocation and trajectory optimization is proposed for UAV-assisted backsca...The Backscatter communication has gained widespread attention from academia and industry in recent years. In this paper, A method of resource allocation and trajectory optimization is proposed for UAV-assisted backscatter communication based on user trajectory. This paper will establish an optimization problem of jointly optimizing the UAV trajectories, UAV transmission power and BD scheduling based on the large-scale channel state signals estimated in advance of the known user trajectories, taking into account the constraints of BD data and working energy consumption, to maximize the energy efficiency of the system. The problem is a non-convex optimization problem in fractional form, and there is nonlinear coupling between optimization variables.An iterative algorithm is proposed based on Dinkelbach algorithm, block coordinate descent method and continuous convex optimization technology. First, the objective function is converted into a non-fractional programming problem based on Dinkelbach method,and then the block coordinate descent method is used to decompose the original complex problem into three independent sub-problems. Finally, the successive convex approximation method is used to solve the trajectory optimization sub-problem. The simulation results show that the proposed scheme and algorithm have obvious energy efficiency gains compared with the comparison scheme.展开更多
With the ever-expanding applications of vehicles and the development of wireless communication technology,the burgeoning unmanned aerial vehicle(UAV)assisted vehicular internet of things(UVIoTs)has emerged,where the g...With the ever-expanding applications of vehicles and the development of wireless communication technology,the burgeoning unmanned aerial vehicle(UAV)assisted vehicular internet of things(UVIoTs)has emerged,where the ground vehicles can experience more efficient wireless services by employing UAVs as a temporary mobile base station.However,due to the diversity of UAVs,there exist UAVs such as jammers to degenerate the performance of wireless communication between the normal UAVs and vehicles.To solve above the problem,in this paper,we propose a game based secure data transmission scheme in UVIoTs.Specifically,we exploit the offensive and defensive game to model the interactions between the normal UAVs and jammers.Here,the strategy of the normal UAV is to determine whether to transmit data,while that of the jammer is whether to interfere.We then formulate two optimization problems,i.e.,maximizing the both utilities of UAVs and jammers.Afterwards,we exploit the backward induction method to analyze the proposed countermeasures and finally solve the optimal solution.Lastly,the simulation results show that the proposed scheme can improve the wireless communication performance under the attacks of jammers compared with conventional schemes.展开更多
The high-capacity vehicle-to-vehicle(V2 V) communication provides a promising solution to support ubiquitous media streaming and content sharing among vehicles.To extend the V2 V links to multiple cells and manage the...The high-capacity vehicle-to-vehicle(V2 V) communication provides a promising solution to support ubiquitous media streaming and content sharing among vehicles.To extend the V2 V links to multiple cells and manage the inter-cell interference,we proposed an UAV-assisted inter-cell V2 V communication model,in which a shared UAV node is placed in the center of V2 V users.By charging the V2 V users underlay spectrum access fee,the cellular network earn profit at the cost of encountering co-channel interference from V2 V links.A Stackelberg game is formulated to model the interactions between the V2 V links and the cellular links,which are the game follower and the leader respectively.Their utility functions are maximized in terms of accessing price as well as transmit power of V2 V users and UAV relays.Simulation evaluations verify that the power-price tradeoff between V2 V network and cellular network has significant potentials to enhance their utility.展开更多
Unmanned Aerial Vehicles(UAVs)will be essential to support mission-critical applications of Ultra Reliable Low Latency Communication(URLLC)in futuristic Sixth-Generation(6G)networks.However,several security vulnerabil...Unmanned Aerial Vehicles(UAVs)will be essential to support mission-critical applications of Ultra Reliable Low Latency Communication(URLLC)in futuristic Sixth-Generation(6G)networks.However,several security vulnerabilities and attacks have plagued previous generations of communication systems;thus,physical layer security,especially against eavesdroppers,is vital,especially for upcoming 6G networks.In this regard,UAVs have appeared as a winning candidate to mitigate security risks.In this paper,we leverage UAVs to propose two methods.The first method utilizes a UAV as Decode-and-Forward(DF)relay,whereas the second method utilizes a UAV as a jammer to mitigate eavesdropping attacks for URLLC between transmitter and receiver devices.Moreover,we present a low-complexity algorithm that outlines the two aforementioned methods of mitigating interception,i.e.increasing secrecy rate,and we compare them with the benchmark null method in which there is a direct communication link between transmitter and receiver without the UAV DF relay.Additionally,simulation results show the effectiveness of such methods by improving the secrecy rate and its dependency on UAV height,blocklength,decoding error probability and transmitter-receiver separation distance.Lastly,we recommend the best method to enhance the secrecy rate in the presence of an eavesdropper based on our simulations.展开更多
Crop uniformity is a comprehensive indicator used to describe crop growth and is important for assessing crop yield and biomass potential.However,there is still a lack of continuous monitoring of uniformity throughout...Crop uniformity is a comprehensive indicator used to describe crop growth and is important for assessing crop yield and biomass potential.However,there is still a lack of continuous monitoring of uniformity throughout the growing season to explain their effects on yield and biomass.Therefore,this paper proposed a wheat uniformity quantification method based on unmanned aerial vehicle imaging technology to monitor and analyze the dynamic changes in wheat uniformity.The leaf area index(LAI),soil plant analysis development(SPAD),and fractional vegetation cover were estimated from hyperspectral images,while plant height was estimated by a point cloud model from RGB images.Based on these 4 agronomic parameters,a total of 20 uniformity indices covering multiple growing stages were calculated.The changing trends in the uniformity indices were consistent with the results of visual interpretation.The uniformity indices strongly correlated with yield and biomass were selected to construct multiple linear regression models for estimating yield and biomass.The results showed that Pielou's index of LAI had the strongest correlation with yield and biomass,with correlation coefficients of-0.760 and-0.801,respectively.The accuracies of the yield(coefficient of determination[R^(2)]=0.616,root mean square error[RMSE]=1.189 Mg/ha)and biomass estimation model(R^(2)=0.798,RMSE=1.952 Mg/ha)using uniformity indices were better than those of the models using the mean values of the 4 agronomic parameters.Therefore,the proposed uniformity monitoring method can be used to effectively evaluate the temporal and spatial variations in wheat uniformity and can provide new insights into the prediction of yield and biomass.展开更多
The rapid evolution of Fifth-Generation(5G)networks and the strategic development of Sixth-Generation(6G)technologies have significantly advanced the implementation of air-ground integrated networks with seamless cove...The rapid evolution of Fifth-Generation(5G)networks and the strategic development of Sixth-Generation(6G)technologies have significantly advanced the implementation of air-ground integrated networks with seamless coverage.Unmanned Aerial Vehicles(UAVs),serving as high-mobility aerial platforms,are extensively utilized to enhance coverage in long-distance emergency communication scenarios.The resource-constrained communication environments in emergencies by classifying UAVs into swarm UAVs and relay UAVs as aerial communication nodes is inversitgated.A horizontal deployment strategy for swarm UAVs is formulated through K-means clustering algorithm optimization,while a vertical deployment scheme is established using convex optimization methods.The minimum-path trajectory planning for relay UAVs is optimized via the Particle Swarm Optimization(PSO)algorithm,enhancing communication reliability between UAV swarms and terrestrial base stations.A three-dimensional heterogeneous network architecture is realized by modeling spatial multi-hop relay links.Experimental results demonstrate that the proposed joint UAV relay optimization framework outperforms conventional algorithms in both coverage performance and relay capability during video stream transmission,achieving significant improvements in coverage enhancement and relay efficiency.This work provides technical foundations for constructing high-reliability air-ground cooperative systems in emergency communications.展开更多
Backscatter communication(BC)is con-sidered a key technology in self-sustainable commu-nications,and the unmanned aerial vehicle(UAV)as a data collector can improve the efficiency of data col-lection.We consider a UAV...Backscatter communication(BC)is con-sidered a key technology in self-sustainable commu-nications,and the unmanned aerial vehicle(UAV)as a data collector can improve the efficiency of data col-lection.We consider a UAV-aided BC system,where the power beacons(PBs)are deployed as dedicated radio frequency(RF)sources to supply power for backscatter devices(BDs).After harvesting enough energy,the BDs transmit data to the UAV.We use stochastic geometry to model the large-scale BC sys-tem.Specifically,the PBs are modeled as a type II Mat´ern hard-core point process(MHCPP II)and the BDs are modeled as a homogeneous Poisson point process(HPPP).Firstly,the BDs’activation proba-bility and average coverage probability are derived.Then,to maximize the energy efficiency(EE),we opti-mize the RF power of the PBs under different PB den-sities.Furthermore,we compare the coverage proba-bility and EE performance of our system with a bench-mark scheme,in which the distribution of PBs is mod-eled as a HPPP.Simulation results show that the PBs modeled as MHCPP II has better performance,and we found that the higher the density of PBs,the smaller the RF power required,and the EE is also higher.展开更多
Unmanned Aerial Vehicles(UAVs)are gaining increasing attention in many fields,such as military,logistics,and hazardous site mapping.Utilizing UAVs to assist communications is one of the promising applications and rese...Unmanned Aerial Vehicles(UAVs)are gaining increasing attention in many fields,such as military,logistics,and hazardous site mapping.Utilizing UAVs to assist communications is one of the promising applications and research directions.The future Industrial Internet places higher demands on communication quality.The easy deployment,dynamic mobility,and low cost of UAVs make them a viable tool for wireless communication in the Industrial Internet.Therefore,UAVs are considered as an integral part of Industry 4.0.In this article,three typical use cases of UAVs-assisted communications in Industrial Internet are first summarized.Then,the state-of-the-art technologies for drone-assisted communication in support of the Industrial Internet are presented.According to the current research,it can be assumed that UAV-assisted communication can support the future Industrial Internet to a certain extent.Finally,the potential research directions and open challenges in UAV-assisted communications in the upcoming future Industrial Internet are discussed.展开更多
Recent advances in computer vision and deep learning have shown that the fusion of depth information can significantly enhance the performance of RGB-based damage detection and segmentation models.However,alongside th...Recent advances in computer vision and deep learning have shown that the fusion of depth information can significantly enhance the performance of RGB-based damage detection and segmentation models.However,alongside the advantages,depth-sensing also presents many practical challenges.For instance,the depth sensors impose an additional payload burden on the robotic inspection platforms limiting the operation time and increasing the inspection cost.Additionally,some lidar-based depth sensors have poor outdoor performance due to sunlight contamination during the daytime.In this context,this study investigates the feasibility of abolishing depth-sensing at test time without compromising the segmentation performance.An autonomous damage segmentation framework is developed,based on recent advancements in vision-based multi-modal sensing such as modality hallucination(MH)and monocular depth estimation(MDE),which require depth data only during the model training.At the time of deployment,depth data becomes expendable as it can be simulated from the corresponding RGB frames.This makes it possible to reap the benefits of depth fusion without any depth perception per se.This study explored two different depth encoding techniques and three different fusion strategies in addition to a baseline RGB-based model.The proposed approach is validated on computer-generated RGB-D data of reinforced concrete buildings subjected to seismic damage.It was observed that the surrogate techniques can increase the segmentation IoU by up to 20.1%with a negligible increase in the computation cost.Overall,this study is believed to make a positive contribution to enhancing the resilience of critical civil infrastructure.展开更多
The rapid increase in the number of Internet of things(IoT)devices has led to significant access pressure,making network energy consumption and communication load key challenges.Edge caching,cooperative communication,...The rapid increase in the number of Internet of things(IoT)devices has led to significant access pressure,making network energy consumption and communication load key challenges.Edge caching,cooperative communication,and energy management technologies have proven to be effective in alleviating these issues.This paper investigates a unmanned aerial vehicle(UAV)-assisted Internet of everything(IoE)architecture that integrates caching,communication,and energy management.A collaborative communicationcaching-energy optimization scheme is proposed,which involves the joint operation of the UAV and base station(BS)to pre-cache content required by ground users,thus minimizing system energy consumption.We model the joint optimization of content caching,communication,and energy consumption as a Markov decision process(MDP),transforming it into a long-term optimization problem solvable by deep reinforcement learning.Based on the simple deep Q-network(DQN),we design a dynamic content placement strategy that jointly optimizes communication,caching,and energy consumption.Simulation results demonstrate that the proposed method,compared to branch and bound(B&B),particle swarm optimization(PSO),genetic algorithm(GA),and random algorithms,not only approaches the optimal solution most closely,effectively reducing system energy consumption,but also exhibits the lowest time complexity.展开更多
文摘The Backscatter communication has gained widespread attention from academia and industry in recent years. In this paper, A method of resource allocation and trajectory optimization is proposed for UAV-assisted backscatter communication based on user trajectory. This paper will establish an optimization problem of jointly optimizing the UAV trajectories, UAV transmission power and BD scheduling based on the large-scale channel state signals estimated in advance of the known user trajectories, taking into account the constraints of BD data and working energy consumption, to maximize the energy efficiency of the system. The problem is a non-convex optimization problem in fractional form, and there is nonlinear coupling between optimization variables.An iterative algorithm is proposed based on Dinkelbach algorithm, block coordinate descent method and continuous convex optimization technology. First, the objective function is converted into a non-fractional programming problem based on Dinkelbach method,and then the block coordinate descent method is used to decompose the original complex problem into three independent sub-problems. Finally, the successive convex approximation method is used to solve the trajectory optimization sub-problem. The simulation results show that the proposed scheme and algorithm have obvious energy efficiency gains compared with the comparison scheme.
基金This work is supported in part by NSFC(nos.U1808207,U20A20175)the Project of Shanghai Municipal Science and Technology Commission(18510761000).
文摘With the ever-expanding applications of vehicles and the development of wireless communication technology,the burgeoning unmanned aerial vehicle(UAV)assisted vehicular internet of things(UVIoTs)has emerged,where the ground vehicles can experience more efficient wireless services by employing UAVs as a temporary mobile base station.However,due to the diversity of UAVs,there exist UAVs such as jammers to degenerate the performance of wireless communication between the normal UAVs and vehicles.To solve above the problem,in this paper,we propose a game based secure data transmission scheme in UVIoTs.Specifically,we exploit the offensive and defensive game to model the interactions between the normal UAVs and jammers.Here,the strategy of the normal UAV is to determine whether to transmit data,while that of the jammer is whether to interfere.We then formulate two optimization problems,i.e.,maximizing the both utilities of UAVs and jammers.Afterwards,we exploit the backward induction method to analyze the proposed countermeasures and finally solve the optimal solution.Lastly,the simulation results show that the proposed scheme can improve the wireless communication performance under the attacks of jammers compared with conventional schemes.
基金supported in part by the National Key R&D Program of China(2018YFC1314903)the National Natural Science Foundation of China(61801238 and 61427801)+1 种基金the NUPTSF(NY217033)NYIT 2017 Global Faculty Summer Research and Creativity(GFSRC)Grant
文摘The high-capacity vehicle-to-vehicle(V2 V) communication provides a promising solution to support ubiquitous media streaming and content sharing among vehicles.To extend the V2 V links to multiple cells and manage the inter-cell interference,we proposed an UAV-assisted inter-cell V2 V communication model,in which a shared UAV node is placed in the center of V2 V users.By charging the V2 V users underlay spectrum access fee,the cellular network earn profit at the cost of encountering co-channel interference from V2 V links.A Stackelberg game is formulated to model the interactions between the V2 V links and the cellular links,which are the game follower and the leader respectively.Their utility functions are maximized in terms of accessing price as well as transmit power of V2 V users and UAV relays.Simulation evaluations verify that the power-price tradeoff between V2 V network and cellular network has significant potentials to enhance their utility.
文摘Unmanned Aerial Vehicles(UAVs)will be essential to support mission-critical applications of Ultra Reliable Low Latency Communication(URLLC)in futuristic Sixth-Generation(6G)networks.However,several security vulnerabilities and attacks have plagued previous generations of communication systems;thus,physical layer security,especially against eavesdroppers,is vital,especially for upcoming 6G networks.In this regard,UAVs have appeared as a winning candidate to mitigate security risks.In this paper,we leverage UAVs to propose two methods.The first method utilizes a UAV as Decode-and-Forward(DF)relay,whereas the second method utilizes a UAV as a jammer to mitigate eavesdropping attacks for URLLC between transmitter and receiver devices.Moreover,we present a low-complexity algorithm that outlines the two aforementioned methods of mitigating interception,i.e.increasing secrecy rate,and we compare them with the benchmark null method in which there is a direct communication link between transmitter and receiver without the UAV DF relay.Additionally,simulation results show the effectiveness of such methods by improving the secrecy rate and its dependency on UAV height,blocklength,decoding error probability and transmitter-receiver separation distance.Lastly,we recommend the best method to enhance the secrecy rate in the presence of an eavesdropper based on our simulations.
基金supported by the National Key R&D Program of China(no.2022YFE0116200)the“JBGS”Project of Seed Industry Revitalization in Jiangsu Province(JBGS[2021]007)+2 种基金the National Natural Science Foundation of China(32272213,32030076,U1803235,and 32021004)the Fundamental Research Funds for the Central Universities(XUEKEN2023013)the National Key Research and Development Program of China(2020YFE0202900).
文摘Crop uniformity is a comprehensive indicator used to describe crop growth and is important for assessing crop yield and biomass potential.However,there is still a lack of continuous monitoring of uniformity throughout the growing season to explain their effects on yield and biomass.Therefore,this paper proposed a wheat uniformity quantification method based on unmanned aerial vehicle imaging technology to monitor and analyze the dynamic changes in wheat uniformity.The leaf area index(LAI),soil plant analysis development(SPAD),and fractional vegetation cover were estimated from hyperspectral images,while plant height was estimated by a point cloud model from RGB images.Based on these 4 agronomic parameters,a total of 20 uniformity indices covering multiple growing stages were calculated.The changing trends in the uniformity indices were consistent with the results of visual interpretation.The uniformity indices strongly correlated with yield and biomass were selected to construct multiple linear regression models for estimating yield and biomass.The results showed that Pielou's index of LAI had the strongest correlation with yield and biomass,with correlation coefficients of-0.760 and-0.801,respectively.The accuracies of the yield(coefficient of determination[R^(2)]=0.616,root mean square error[RMSE]=1.189 Mg/ha)and biomass estimation model(R^(2)=0.798,RMSE=1.952 Mg/ha)using uniformity indices were better than those of the models using the mean values of the 4 agronomic parameters.Therefore,the proposed uniformity monitoring method can be used to effectively evaluate the temporal and spatial variations in wheat uniformity and can provide new insights into the prediction of yield and biomass.
文摘The rapid evolution of Fifth-Generation(5G)networks and the strategic development of Sixth-Generation(6G)technologies have significantly advanced the implementation of air-ground integrated networks with seamless coverage.Unmanned Aerial Vehicles(UAVs),serving as high-mobility aerial platforms,are extensively utilized to enhance coverage in long-distance emergency communication scenarios.The resource-constrained communication environments in emergencies by classifying UAVs into swarm UAVs and relay UAVs as aerial communication nodes is inversitgated.A horizontal deployment strategy for swarm UAVs is formulated through K-means clustering algorithm optimization,while a vertical deployment scheme is established using convex optimization methods.The minimum-path trajectory planning for relay UAVs is optimized via the Particle Swarm Optimization(PSO)algorithm,enhancing communication reliability between UAV swarms and terrestrial base stations.A three-dimensional heterogeneous network architecture is realized by modeling spatial multi-hop relay links.Experimental results demonstrate that the proposed joint UAV relay optimization framework outperforms conventional algorithms in both coverage performance and relay capability during video stream transmission,achieving significant improvements in coverage enhancement and relay efficiency.This work provides technical foundations for constructing high-reliability air-ground cooperative systems in emergency communications.
文摘Backscatter communication(BC)is con-sidered a key technology in self-sustainable commu-nications,and the unmanned aerial vehicle(UAV)as a data collector can improve the efficiency of data col-lection.We consider a UAV-aided BC system,where the power beacons(PBs)are deployed as dedicated radio frequency(RF)sources to supply power for backscatter devices(BDs).After harvesting enough energy,the BDs transmit data to the UAV.We use stochastic geometry to model the large-scale BC sys-tem.Specifically,the PBs are modeled as a type II Mat´ern hard-core point process(MHCPP II)and the BDs are modeled as a homogeneous Poisson point process(HPPP).Firstly,the BDs’activation proba-bility and average coverage probability are derived.Then,to maximize the energy efficiency(EE),we opti-mize the RF power of the PBs under different PB den-sities.Furthermore,we compare the coverage proba-bility and EE performance of our system with a bench-mark scheme,in which the distribution of PBs is mod-eled as a HPPP.Simulation results show that the PBs modeled as MHCPP II has better performance,and we found that the higher the density of PBs,the smaller the RF power required,and the EE is also higher.
基金supported in part by National Key Research&Devel-opment Program of China(2021YFB2900801)in part by Guangdong Basic and Applied Basic Research Foundation(2022A1515110335)in party by Fundamental Research Funds for the Central Universities(FRF-TP-22-094A1).
文摘Unmanned Aerial Vehicles(UAVs)are gaining increasing attention in many fields,such as military,logistics,and hazardous site mapping.Utilizing UAVs to assist communications is one of the promising applications and research directions.The future Industrial Internet places higher demands on communication quality.The easy deployment,dynamic mobility,and low cost of UAVs make them a viable tool for wireless communication in the Industrial Internet.Therefore,UAVs are considered as an integral part of Industry 4.0.In this article,three typical use cases of UAVs-assisted communications in Industrial Internet are first summarized.Then,the state-of-the-art technologies for drone-assisted communication in support of the Industrial Internet are presented.According to the current research,it can be assumed that UAV-assisted communication can support the future Industrial Internet to a certain extent.Finally,the potential research directions and open challenges in UAV-assisted communications in the upcoming future Industrial Internet are discussed.
基金supported in part by a fund from Bentley Systems,Inc.
文摘Recent advances in computer vision and deep learning have shown that the fusion of depth information can significantly enhance the performance of RGB-based damage detection and segmentation models.However,alongside the advantages,depth-sensing also presents many practical challenges.For instance,the depth sensors impose an additional payload burden on the robotic inspection platforms limiting the operation time and increasing the inspection cost.Additionally,some lidar-based depth sensors have poor outdoor performance due to sunlight contamination during the daytime.In this context,this study investigates the feasibility of abolishing depth-sensing at test time without compromising the segmentation performance.An autonomous damage segmentation framework is developed,based on recent advancements in vision-based multi-modal sensing such as modality hallucination(MH)and monocular depth estimation(MDE),which require depth data only during the model training.At the time of deployment,depth data becomes expendable as it can be simulated from the corresponding RGB frames.This makes it possible to reap the benefits of depth fusion without any depth perception per se.This study explored two different depth encoding techniques and three different fusion strategies in addition to a baseline RGB-based model.The proposed approach is validated on computer-generated RGB-D data of reinforced concrete buildings subjected to seismic damage.It was observed that the surrogate techniques can increase the segmentation IoU by up to 20.1%with a negligible increase in the computation cost.Overall,this study is believed to make a positive contribution to enhancing the resilience of critical civil infrastructure.
基金supported by the National Key Research and Development Program of China under Grant 2021ZD0113003the National Natural Science Foundation of China under Grant 92367302.
文摘The rapid increase in the number of Internet of things(IoT)devices has led to significant access pressure,making network energy consumption and communication load key challenges.Edge caching,cooperative communication,and energy management technologies have proven to be effective in alleviating these issues.This paper investigates a unmanned aerial vehicle(UAV)-assisted Internet of everything(IoE)architecture that integrates caching,communication,and energy management.A collaborative communicationcaching-energy optimization scheme is proposed,which involves the joint operation of the UAV and base station(BS)to pre-cache content required by ground users,thus minimizing system energy consumption.We model the joint optimization of content caching,communication,and energy consumption as a Markov decision process(MDP),transforming it into a long-term optimization problem solvable by deep reinforcement learning.Based on the simple deep Q-network(DQN),we design a dynamic content placement strategy that jointly optimizes communication,caching,and energy consumption.Simulation results demonstrate that the proposed method,compared to branch and bound(B&B),particle swarm optimization(PSO),genetic algorithm(GA),and random algorithms,not only approaches the optimal solution most closely,effectively reducing system energy consumption,but also exhibits the lowest time complexity.
