Multiple UAVs cooperative target search has been widely used in various environments,such as emergency rescue and traffic monitoring.However,uncertain communication network among UAVs exhibits unstable links and rapid...Multiple UAVs cooperative target search has been widely used in various environments,such as emergency rescue and traffic monitoring.However,uncertain communication network among UAVs exhibits unstable links and rapid topological fluctuations due to mission complexity and unpredictable environmental states.This limitation hinders timely information sharing and insightful path decisions for UAVs,resulting in inefficient or even failed collaborative search.Aiming at this issue,this paper proposes a multi-UAV cooperative search strategy by developing a real-time trajectory decision that incorporates autonomous connectivity to reinforce multi-UAV collaboration and achieve search acceleration in uncertain search environments.Specifically,an autonomous connectivity strategy based on node cognitive information and network states is introduced to enable effective message transmission and adapt to the dynamic network environment.Based on the fused information,we formalize the trajectory planning as a multiobjective optimization problem by jointly considering search performance and UAV energy harnessing.A multi-agent deep reinforcement learning based algorithm is proposed to solve it,where the reward-guided real-time path is determined to achieve an energyefficient search.Finally,extensive experimental results show that the proposed algorithm outperforms existing works in terms of average search rate and coverage rate with reduced energy consumption under uncertain search environments.展开更多
Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited reso...Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited resources and insufficient battery capacities of UAVs,it is necessary to fast locate the odor source with low-complexity computation and minimal interaction under complicated environmental states.To this end,we propose a multi-UAV collaboration based odor source localization(MUC-OSL)method,where source estimation and UAV navigation are iteratively performed,aiming to accelerate the searching process and reduce the resource consumption of UAVs.Specifically,in the source estimation phase,we present a collaborative particle filter algorithm on the basis of UAVs’cognitive difference and collaborative information to improve source estimation accuracy.In the following navigation phase,an adaptive path planning algorithm is designed based on partially observable Markov decision process to distributedly determine the subsequent flying direction and moving steps of each UAV.The results of experiments conducted on two simulation platforms demonstrate that MUC-OSL outperforms existing efforts in terms of mean search time and success rate,and effectively reduces the resource consumption of UAVs.展开更多
This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in differe...This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in different environmental conditions.We propose a robust FLC with low computational complexity by reducing the number of membership functions and rules.To optimize the performance of the FLC,metaheuristic algorithms are employed to determine the parameters of the FLC.We evaluate the proposed FLC in various panel configurations under different environmental conditions.The results indicate that the proposed FLC can easily adapt to various panel configurations and perform better than other benchmarks in terms of enhanced stability,responsiveness,and power transfer under various scenarios.展开更多
Intelligent vehicle applications provide convenience but raise privacy and security concerns.Misuse of sensitive data,including vehicle location,and facial recognition information,poses a threat to user privacy.Hence,...Intelligent vehicle applications provide convenience but raise privacy and security concerns.Misuse of sensitive data,including vehicle location,and facial recognition information,poses a threat to user privacy.Hence,traffic classification is vital for promptly overseeing and controlling applications with sensitive information.In this paper,we propose ETNet,a framework that combines multiple features and leverages self-attention mechanisms to learn deep relationships between packets.ET-Net employs a multisimilarity triplet network to extract features from raw bytes,and exploits self-attention to capture long-range dependencies within packets in a session and contextual information features.Additionally,we utilizing the loss function to more effectively integrate information acquired from both byte sequences and their corresponding lengths.Through simulated evaluations on datasets with similar attributes,ET-Net demonstrates the ability to finely distinguish between nine categories of applications,achieving superior results compared to existing methods.展开更多
This paper studies the use of unmannedaerial vehicles (UAV) equipped with radio frequency(RF) energy harvesting (EH) technology to quickly establishtemporary communication networks in disasterareas to provide artifici...This paper studies the use of unmannedaerial vehicles (UAV) equipped with radio frequency(RF) energy harvesting (EH) technology to quickly establishtemporary communication networks in disasterareas to provide artificial intelligence (AI) basedservices to users with limited resources. In particular,to ensure the quality of AI-based services and improvethe lifetime of emergency communication networks,we study how to reduce the service latency andenergy consumption when fine-tuning models of AIbasedservices in the resource-constrained emergencysystem. A joint optimization problem of model trainingand RF EH for UAV-based emergency communicationnetwork is formulated. Due to the nonlinear RFEH circuit characteristics, the optimization problemis non-convex. We transform the non-convex probleminto solvable subproblems and propose an energyefficientand low-latency federated learning algorithm(EL-FL) to solve these subproblems. Theoretical analysisof the convergence and computational complexityof EL-FL is provided. Simulation results show thatthe proposed scheme significantly outperforms otherbaseline methods in various network environments.展开更多
Fog Computing is a new platform that can serve mobile devices in the local area. In Fog Computing, the resources need to be shared or cached in the widely deployed Fog clusters. In this paper, we propose a Steiner tre...Fog Computing is a new platform that can serve mobile devices in the local area. In Fog Computing, the resources need to be shared or cached in the widely deployed Fog clusters. In this paper, we propose a Steiner tree based caching scheme, in which the Fog servers, when caching resources, first produce a Steiner tree to minimize the total path weight(or cost) such that the cost of resource caching using this tree could be minimized. Then we give a running illustration to show how the Fog Computing works and we compare the traditional shortest path scheme with the proposed one. The outcome shows that the Steiner tree based scheme could work more efficiently.展开更多
The architecture of cislunar multi-hop communication networks, which focuses on the requirements of lunar full-coverage and continuous cislunar communications, is presented on the basis of Geosynchronous Orbit (GEO) s...The architecture of cislunar multi-hop communication networks, which focuses on the requirements of lunar full-coverage and continuous cislunar communications, is presented on the basis of Geosynchronous Orbit (GEO) satellite network relays. According to the geographical distribution of the forthcoming Chinese Deep Space Measuring and Controlling Network (DSMCN), two networking schemes are proposed and two elevation angle optimization models are established for locating GEO relay satellites. To analyze the dynamic connectivity, a dynamic network model is constructed with respect to the time-varying characteristics of cislunar trunk links. The advantages of the two proposed schemes, in terms of the Connectivity Rate (CR), Interruption Frequency (IF), and Average Length of Connecting Duration (ALCD), are corroborated by several simulations. In the case of the lunar polar orbit constellation case, the gains in the performance of scheme I are observed to be 134.55%, 117.03%, and 217.47% compared with DSMCN for three evaluation indicators, and the gains in the performance of scheme II are observed to be 238. 22%, 240.40%, and 572.71%. The results validate that the connectivity of GEO satellites outperforms that of earth facilities significantly and schemes based on GEO satellite relays are promising options for cislunar multi-hop communication networking.展开更多
Internet of Things （IoT） refers to an infrastructure which enables the forms of com- munication and collaboration between people and things, and between things themselves. In order to improve its performance, we pre...Internet of Things （IoT） refers to an infrastructure which enables the forms of com- munication and collaboration between people and things, and between things themselves. In order to improve its performance, we present a tradeoff between bandwidth and energy con- sumption in the loT in this paper. A service providing model is built to find the relation- ship between bandwidth and energy consump- tion using a cooperative differential game mo- del. The game solution is gotten in the condi- tion of grand coalition, feedback Nash equili- brium and intermediate coalitions and an allo- cation policy is obtain by Shapley theory. The results are shown as follows. Firstly, the per- formance of IoT decreases with the increasing of bandwidth cost or with the decreasing of en- ergy cost; secondly, all the nodes in the IoT com- posing a grand coalition can save bandwidth and energy consumption; thirdly, when the fac- tors of bandwidth cost and energy cost are eq- ual, the obtained number of provided services is an optimised value which is the trade-off between energy and bandwidth consumption.展开更多
Resource allocation is crucial for satellite networks. In this paper, we propose a multi-resource fair allocation scheme, namely Dominant and Max-min Fair(DMMF), to efficiently and fairly allocate resources. It consis...Resource allocation is crucial for satellite networks. In this paper, we propose a multi-resource fair allocation scheme, namely Dominant and Max-min Fair(DMMF), to efficiently and fairly allocate resources. It consists of two allocation stages, dominant resource fair(DRF) allocation stage and max-min fairness(MMF) allocation stage. The proposed DMMF scheme exhibits desirable properties, including share incentive, strategy proofness, envy freeness and Pareto optimality. Meanwhile, DMMF can improve the allocation efficiency and reach 100% allocation efficiency.展开更多
While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. However, there is an obvious contrad...While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. In this paper, we firstly reviewed the enormous benefits and challenges of security and privacy in Big Data. Then, we present some possible methods and techniques to ensure Big Data security and privacy.展开更多
In peer-assisted streaming network, service provider needs to pre-pushing resources to some users for caching. This scheme can increase the resource availability. In traditional strategy, in order to guarantee the use...In peer-assisted streaming network, service provider needs to pre-pushing resources to some users for caching. This scheme can increase the resource availability. In traditional strategy, in order to guarantee the user＇s quality of service （QoS）, the pre-pushing action can only start at the time point that the certain user has not used the computer for more than 20 minutes. If the user comes back, the pre-pushing action will be stopped. We claim that this is not an efficient scheme. In this paper, we propose a novel pre-pushing scheme to improve the pre-pushing efficiency, while meeting the user＇s QoS requirements. The basic idea is using the user＇s available bandwidth as much as possible in the condition of meeting the user＇s QoS requirements. Then we design an available bandwidth calculating strategy. The numerical simulation demonstrates that our proposed scheme outperforms the traditional one.展开更多
Low Earth Orbit （LEO） satellites networks can provide multimedia service and plays an increasingly important role in the exploitation of space. However, one of the challenges in LEO satellites networks is that the s...Low Earth Orbit （LEO） satellites networks can provide multimedia service and plays an increasingly important role in the exploitation of space. However, one of the challenges in LEO satellites networks is that the services are suffered from high symbol error, limited storage space and limited available energy. To analyze the performance of the service in LEO satellites networks, a model, based on differential game, is proposed for satisfying the QoS requirements of multimedia applications. The controller of our model is the transmitting rate and the object is to maximize the payoff depending on the error symbol rate, the available energy, the bandwidth and the process ability so as to guarantee the QoS service. In order to solve our built model, we use the Bellman theorem to make formulas on the trance of the optimal transmitting rate. Furthermore, simulation results verify that the service can be maximized by using our derived transmitting rate trance.展开更多
In the dispersed computing environment driven by intelligent networks,intrusion detection faces significant challenges.This paper proposes a multilayer decentralized federated learning framework based on mean field ga...In the dispersed computing environment driven by intelligent networks,intrusion detection faces significant challenges.This paper proposes a multilayer decentralized federated learning framework based on mean field game theory(MFG-DFL).The framework organizes networked computing points(NCPs)into a three-layer collaborative architecture,and innovatively introduces MFG theory to model the complex dynamic interactions,which among large-scale NCPs as a game between a representative NCP and the mean field.By solving the coupled HJB and FPK equations,we design a dynamic incentive mechanism to fairly quantify and reward NCP contributions,thus aligning individual rationality with the global objectives of the system.The simulation results on the CICIoT2023 data set demonstrate the outstanding performance of the proposed framework.Specifically,it achieves an intrusion detection accuracy of 81.09%in highly non-IID scenarios,showcasing a well-balanced trade-off between computational efficiency and performance enhancement.展开更多
基金supported by National Natural Science Foundation of China(No.62202449 and No.62472410)National Key Research and Development Program of China(2021YFB2900102)。
文摘Multiple UAVs cooperative target search has been widely used in various environments,such as emergency rescue and traffic monitoring.However,uncertain communication network among UAVs exhibits unstable links and rapid topological fluctuations due to mission complexity and unpredictable environmental states.This limitation hinders timely information sharing and insightful path decisions for UAVs,resulting in inefficient or even failed collaborative search.Aiming at this issue,this paper proposes a multi-UAV cooperative search strategy by developing a real-time trajectory decision that incorporates autonomous connectivity to reinforce multi-UAV collaboration and achieve search acceleration in uncertain search environments.Specifically,an autonomous connectivity strategy based on node cognitive information and network states is introduced to enable effective message transmission and adapt to the dynamic network environment.Based on the fused information,we formalize the trajectory planning as a multiobjective optimization problem by jointly considering search performance and UAV energy harnessing.A multi-agent deep reinforcement learning based algorithm is proposed to solve it,where the reward-guided real-time path is determined to achieve an energyefficient search.Finally,extensive experimental results show that the proposed algorithm outperforms existing works in terms of average search rate and coverage rate with reduced energy consumption under uncertain search environments.
基金supported by National Natural Science Foundation of China(No.62072436 and No.62202449)National Key Research and Development Program of China(2021YFB2900102).
文摘Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited resources and insufficient battery capacities of UAVs,it is necessary to fast locate the odor source with low-complexity computation and minimal interaction under complicated environmental states.To this end,we propose a multi-UAV collaboration based odor source localization(MUC-OSL)method,where source estimation and UAV navigation are iteratively performed,aiming to accelerate the searching process and reduce the resource consumption of UAVs.Specifically,in the source estimation phase,we present a collaborative particle filter algorithm on the basis of UAVs’cognitive difference and collaborative information to improve source estimation accuracy.In the following navigation phase,an adaptive path planning algorithm is designed based on partially observable Markov decision process to distributedly determine the subsequent flying direction and moving steps of each UAV.The results of experiments conducted on two simulation platforms demonstrate that MUC-OSL outperforms existing efforts in terms of mean search time and success rate,and effectively reduces the resource consumption of UAVs.
文摘This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in different environmental conditions.We propose a robust FLC with low computational complexity by reducing the number of membership functions and rules.To optimize the performance of the FLC,metaheuristic algorithms are employed to determine the parameters of the FLC.We evaluate the proposed FLC in various panel configurations under different environmental conditions.The results indicate that the proposed FLC can easily adapt to various panel configurations and perform better than other benchmarks in terms of enhanced stability,responsiveness,and power transfer under various scenarios.
基金supported by National Key Research and Development Program of China(2022YFB3104903)S&T Program of Hebei(No.SZX2020034).
文摘Intelligent vehicle applications provide convenience but raise privacy and security concerns.Misuse of sensitive data,including vehicle location,and facial recognition information,poses a threat to user privacy.Hence,traffic classification is vital for promptly overseeing and controlling applications with sensitive information.In this paper,we propose ETNet,a framework that combines multiple features and leverages self-attention mechanisms to learn deep relationships between packets.ET-Net employs a multisimilarity triplet network to extract features from raw bytes,and exploits self-attention to capture long-range dependencies within packets in a session and contextual information features.Additionally,we utilizing the loss function to more effectively integrate information acquired from both byte sequences and their corresponding lengths.Through simulated evaluations on datasets with similar attributes,ET-Net demonstrates the ability to finely distinguish between nine categories of applications,achieving superior results compared to existing methods.
基金supported in part by the Key Program of the National Natural Science Foundation of China under Grant 62436004in part by the National Key Research and Development Program of China under Grant 2022YFB3104903.
文摘This paper studies the use of unmannedaerial vehicles (UAV) equipped with radio frequency(RF) energy harvesting (EH) technology to quickly establishtemporary communication networks in disasterareas to provide artificial intelligence (AI) basedservices to users with limited resources. In particular,to ensure the quality of AI-based services and improvethe lifetime of emergency communication networks,we study how to reduce the service latency andenergy consumption when fine-tuning models of AIbasedservices in the resource-constrained emergencysystem. A joint optimization problem of model trainingand RF EH for UAV-based emergency communicationnetwork is formulated. Due to the nonlinear RFEH circuit characteristics, the optimization problemis non-convex. We transform the non-convex probleminto solvable subproblems and propose an energyefficientand low-latency federated learning algorithm(EL-FL) to solve these subproblems. Theoretical analysisof the convergence and computational complexityof EL-FL is provided. Simulation results show thatthe proposed scheme significantly outperforms otherbaseline methods in various network environments.
基金supported by the National High-Tech R&D Program(863Program)No.2015AA01A705the National Natural Science Foundation of China under Grant No.61202079+1 种基金the China Postdoctoral Science Foundation under Grant No.2014T70031the Fundamental Research Funds for the Central Universities of China No.2015JBM111
文摘Fog Computing is a new platform that can serve mobile devices in the local area. In Fog Computing, the resources need to be shared or cached in the widely deployed Fog clusters. In this paper, we propose a Steiner tree based caching scheme, in which the Fog servers, when caching resources, first produce a Steiner tree to minimize the total path weight(or cost) such that the cost of resource caching using this tree could be minimized. Then we give a running illustration to show how the Fog Computing works and we compare the traditional shortest path scheme with the proposed one. The outcome shows that the Steiner tree based scheme could work more efficiently.
基金supported by the National High Technology Research and Development Program of P.R.China under Grant No.2012 AA121604 the National Natural Science Foundation of China under Grants No.60902042,No.61170014,No.61202079+1 种基金 the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No.20090006110014 the Foundation for Key Program of Ministry of Education of China under Grant No.311007
文摘The architecture of cislunar multi-hop communication networks, which focuses on the requirements of lunar full-coverage and continuous cislunar communications, is presented on the basis of Geosynchronous Orbit (GEO) satellite network relays. According to the geographical distribution of the forthcoming Chinese Deep Space Measuring and Controlling Network (DSMCN), two networking schemes are proposed and two elevation angle optimization models are established for locating GEO relay satellites. To analyze the dynamic connectivity, a dynamic network model is constructed with respect to the time-varying characteristics of cislunar trunk links. The advantages of the two proposed schemes, in terms of the Connectivity Rate (CR), Interruption Frequency (IF), and Average Length of Connecting Duration (ALCD), are corroborated by several simulations. In the case of the lunar polar orbit constellation case, the gains in the performance of scheme I are observed to be 134.55%, 117.03%, and 217.47% compared with DSMCN for three evaluation indicators, and the gains in the performance of scheme II are observed to be 238. 22%, 240.40%, and 572.71%. The results validate that the connectivity of GEO satellites outperforms that of earth facilities significantly and schemes based on GEO satellite relays are promising options for cislunar multi-hop communication networking.
基金ACKNOWLEDGEMENT We gratefully acknowledge anonymous revie- wers who read drafts and made many helpful suggestions. This work was supported by the National Natural Science Foundation of China under Grant No. 61202079 the China Post- doctoral Science Foundation under Grant No. 2013M530526+2 种基金 the Foundation of Beijing En- gineering the Fundamental Research Funds for the Central Universities under Grant No. FRF-TP-13-015A and the Technology Centre for Convergence Networks and Ubiquitous Services.
文摘Internet of Things （IoT） refers to an infrastructure which enables the forms of com- munication and collaboration between people and things, and between things themselves. In order to improve its performance, we present a tradeoff between bandwidth and energy con- sumption in the loT in this paper. A service providing model is built to find the relation- ship between bandwidth and energy consump- tion using a cooperative differential game mo- del. The game solution is gotten in the condi- tion of grand coalition, feedback Nash equili- brium and intermediate coalitions and an allo- cation policy is obtain by Shapley theory. The results are shown as follows. Firstly, the per- formance of IoT decreases with the increasing of bandwidth cost or with the decreasing of en- ergy cost; secondly, all the nodes in the IoT com- posing a grand coalition can save bandwidth and energy consumption; thirdly, when the fac- tors of bandwidth cost and energy cost are eq- ual, the obtained number of provided services is an optimised value which is the trade-off between energy and bandwidth consumption.
基金supported by the National High-Tech R&D Program (863 Program) No. 2015AA01A705the National Natural Science Foundation of China under Grant No. 61572072+1 种基金the National Science and Technology Major Project No. 2015ZX03001041Fundamental Research Funds for the Central Universities "Research on the System of Personalized Education using Big Data"
文摘Resource allocation is crucial for satellite networks. In this paper, we propose a multi-resource fair allocation scheme, namely Dominant and Max-min Fair(DMMF), to efficiently and fairly allocate resources. It consists of two allocation stages, dominant resource fair(DRF) allocation stage and max-min fairness(MMF) allocation stage. The proposed DMMF scheme exhibits desirable properties, including share incentive, strategy proofness, envy freeness and Pareto optimality. Meanwhile, DMMF can improve the allocation efficiency and reach 100% allocation efficiency.
文摘While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. In this paper, we firstly reviewed the enormous benefits and challenges of security and privacy in Big Data. Then, we present some possible methods and techniques to ensure Big Data security and privacy.
基金the National Natural Science Foundation of China under Grant No.61202079,61308018，the China Postdoctoral Science Foundation under Grant No.2014T70031，the Fundamental Research Funds for the Central Universities under Grant No.FRF-TP-14-046A2
文摘In peer-assisted streaming network, service provider needs to pre-pushing resources to some users for caching. This scheme can increase the resource availability. In traditional strategy, in order to guarantee the user＇s quality of service （QoS）, the pre-pushing action can only start at the time point that the certain user has not used the computer for more than 20 minutes. If the user comes back, the pre-pushing action will be stopped. We claim that this is not an efficient scheme. In this paper, we propose a novel pre-pushing scheme to improve the pre-pushing efficiency, while meeting the user＇s QoS requirements. The basic idea is using the user＇s available bandwidth as much as possible in the condition of meeting the user＇s QoS requirements. Then we design an available bandwidth calculating strategy. The numerical simulation demonstrates that our proposed scheme outperforms the traditional one.
基金National Science Foundation Project of P.R.China，China Postdoctoral Science Foundation
文摘Low Earth Orbit （LEO） satellites networks can provide multimedia service and plays an increasingly important role in the exploitation of space. However, one of the challenges in LEO satellites networks is that the services are suffered from high symbol error, limited storage space and limited available energy. To analyze the performance of the service in LEO satellites networks, a model, based on differential game, is proposed for satisfying the QoS requirements of multimedia applications. The controller of our model is the transmitting rate and the object is to maximize the payoff depending on the error symbol rate, the available energy, the bandwidth and the process ability so as to guarantee the QoS service. In order to solve our built model, we use the Bellman theorem to make formulas on the trance of the optimal transmitting rate. Furthermore, simulation results verify that the service can be maximized by using our derived transmitting rate trance.
基金National Science Foundation Project of China(62436004,62372317)National Key Research and Development Program of China(2023YFC3331702)。
文摘In the dispersed computing environment driven by intelligent networks,intrusion detection faces significant challenges.This paper proposes a multilayer decentralized federated learning framework based on mean field game theory(MFG-DFL).The framework organizes networked computing points(NCPs)into a three-layer collaborative architecture,and innovatively introduces MFG theory to model the complex dynamic interactions,which among large-scale NCPs as a game between a representative NCP and the mean field.By solving the coupled HJB and FPK equations,we design a dynamic incentive mechanism to fairly quantify and reward NCP contributions,thus aligning individual rationality with the global objectives of the system.The simulation results on the CICIoT2023 data set demonstrate the outstanding performance of the proposed framework.Specifically,it achieves an intrusion detection accuracy of 81.09%in highly non-IID scenarios,showcasing a well-balanced trade-off between computational efficiency and performance enhancement.
