Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic protocols.However,due to the stringent requirements of the quantum key generation env...Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic protocols.However,due to the stringent requirements of the quantum key generation environment,the generated quantum keys are considered valuable,and the slow key generation rate conflicts with the high-speed data transmission in traditional optical networks.In this paper,for the QKD network with a trusted relay,which is mainly based on point-to-point quantum keys and has complex changes in network resources,we aim to allocate resources reasonably for data packet distribution.Firstly,we formulate a linear programming constraint model for the key resource allocation(KRA)problem based on the time-slot scheduling.Secondly,we propose a new scheduling scheme based on the graded key security requirements(GKSR)and a new micro-log key storage algorithm for effective storage and management of key resources.Finally,we propose a key resource consumption(KRC)routing optimization algorithm to properly allocate time slots,routes,and key resources.Simulation results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate,among others.展开更多
In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET...In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET)phase first and then cooperatively transmit information to a hybrid access point(AP)in the wireless information transmission(WIT)phase,following which the IRS is deployed to enhance the system performance of theWET andWIT.We maximized the weighted sum-rate problem by jointly optimizing the transmit time slots,power allocations,and the phase shifts of the IRS.Due to the non-convexity of the original problem,a semidefinite programming relaxation-based approach is proposed to convert the formulated problem to a convex optimization framework,which can obtain the optimal global solution.Simulation results demonstrate that the weighted sum throughput of the proposed UC scheme outperforms the non-UC scheme whether equipped with IRS or not.展开更多
Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal ...Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.展开更多
With the rapid development of Network Function Virtualization(NFV),the problem of low resource utilizationin traditional data centers is gradually being addressed.However,existing research does not optimize both local...With the rapid development of Network Function Virtualization(NFV),the problem of low resource utilizationin traditional data centers is gradually being addressed.However,existing research does not optimize both localand global allocation of resources in data centers.Hence,we propose an adaptive hybrid optimization strategy thatcombines dynamic programming and neural networks to improve resource utilization and service quality in datacenters.Our approach encompasses a service function chain simulation generator,a parallel architecture servicesystem,a dynamic programming strategy formaximizing the utilization of local server resources,a neural networkfor predicting the global utilization rate of resources and a global resource optimization strategy for bottleneck andredundant resources.With the implementation of our local and global resource allocation strategies,the systemperformance is significantly optimized through simulation.展开更多
Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC a...Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.展开更多
With the development of artificial intelligence(AI)and 5G technology,the integration of sensing,communication and computing in the Internet of Vehicles(Io V)is becoming a trend.However,the large amount of data transmi...With the development of artificial intelligence(AI)and 5G technology,the integration of sensing,communication and computing in the Internet of Vehicles(Io V)is becoming a trend.However,the large amount of data transmission and the computing requirements of intelligent tasks lead to the complex resource management problems.In view of the above challenges,this paper proposes a tasks-oriented joint resource allocation scheme(TOJRAS)in the scenario of Io V.First,this paper proposes a system model with sensing,communication,and computing integration for multiple intelligent tasks with different requirements in the Io V.Secondly,joint resource allocation problems for real-time tasks and delay-tolerant tasks in the Io V are constructed respectively,including communication,computing and caching resources.Thirdly,a distributed deep Q-network(DDQN)based algorithm is proposed to solve the optimization problems,and the convergence and complexity of the algorithm are discussed.Finally,the experimental results based on real data sets verify the performance advantages of the proposed resource allocation scheme,compared to the existing ones.The exploration efficiency of our proposed DDQN-based algorithm is improved by at least about 5%,and our proposed resource allocation scheme improves the m AP performance by about 0.15 under resource constraints.展开更多
Cold-chain logistics system(CCLS)plays the role of collecting and managing the logistics data of frozen food.However,there always exist problems of information loss,data tampering,and privacy leakage in traditional ce...Cold-chain logistics system(CCLS)plays the role of collecting and managing the logistics data of frozen food.However,there always exist problems of information loss,data tampering,and privacy leakage in traditional centralized systems,which influence frozen food security and people’s health.The centralized management form impedes the development of the cold-chain logistics industry and weakens logistics data availability.This paper first introduces a distributed CCLS based on blockchain technology to solve the centralized management problem.This system aggregates the production base,storage,transport,detection,processing,and consumer to form a cold-chain logistics union.The blockchain ledger guarantees that the logistics data cannot be tampered with and establishes a traceability mechanism for food safety incidents.Meanwhile,to improve the value of logistics data,a Stackelberg game-based resource allocation model has been proposed between the logistics data resource provider and the consumer.The competition between resource price and volume balances the resource supplement and consumption.This model can help to achieve an optimal resource price when the Stackelberg game obtains Nash equilibrium.The two participants also can maximize their revenues with the optimal resource price and volume by utilizing the backward induction method.Then,the performance evaluations of transaction throughput and latency show that the proposed distributed CCLS is more secure and stable.The simulations about the variation trend of data price and amount,optimal benefits,and total benefits comparison of different forms show that the resource allocation model is more efficient and practical.Moreover,the blockchain-based CCLS and Stackelberg game-based resource allocation model also can promote the value of logistic data and improve social benefits.展开更多
To solve the contradiction between limited spectrum resources and increasing communication demand,this paper proposes a wireless resource allocation scheme based on the Deep Q Network(DQN)to allocate radio resources i...To solve the contradiction between limited spectrum resources and increasing communication demand,this paper proposes a wireless resource allocation scheme based on the Deep Q Network(DQN)to allocate radio resources in a downlink multi-user cognitive radio(CR)network with slicing.Secondary users(SUs)are multiplexed using non-orthogonal multiple access(NOMA).The SUs use the hybrid spectrum access mode to improve the spectral efficiency(SE).Considering the demand for multiple services,the enhanced mobile broadband(eMBB)slice and ultrareliable low-latency communication(URLLC)slice were established.The proposed scheme can maximize the SE while ensuring Quality of Service(QoS)for the users.This study established a mapping relationship between resource allocation and the DQN algorithm in the CR-NOMA network.According to the signal-to-interference-plusnoise ratio(SINR)of the primary users(PUs),the proposed scheme can output the optimal channel selection and power allocation.The simulation results reveal that the proposed scheme can converge faster and obtain higher rewards compared with the Q-Learning scheme.Additionally,the proposed scheme has better SE than both the overlay and underlay only modes.展开更多
Heterogeneous base station deployment enables to provide high capacity and wide area coverage.Network slicing makes it possible to allocate wireless resource for heterogeneous services on demand.These two promising te...Heterogeneous base station deployment enables to provide high capacity and wide area coverage.Network slicing makes it possible to allocate wireless resource for heterogeneous services on demand.These two promising technologies contribute to the unprecedented service in 5G.We establish a multiservice heterogeneous network model,which aims to raise the transmission rate under the delay constraints for active control terminals,and optimize the energy efficiency for passive network terminals.A policygradient-based deep reinforcement learning algorithm is proposed to make decisions on user association and power control in the continuous action space.Simulation results indicate the good convergence of the algorithm,and higher reward is obtained compared with other baselines.展开更多
Mobile learning has evolved into a new format of education based on communication and computer technology that is favored by an increasing number of learning users thanks to the development of wireless communication n...Mobile learning has evolved into a new format of education based on communication and computer technology that is favored by an increasing number of learning users thanks to the development of wireless communication networks,mobile edge computing,artificial intelligence,and mobile devices.However,due to the constrained data processing capacity of mobile devices,efficient and effective interactive mobile learning is a challenge.Therefore,for mobile learning,we propose a"Cloud,Edge and End"fusion system architecture.Through task offloading and resource allocation for edge-enabled mobile learning to reduce the time and energy consumption of user equipment.Then,we present the proposed solutions that uses the minimum cost maximum flow(MCMF)algorithm to deal with the offloading problem and the deep Q network(DQN)algorithm to deal with the resource allocation problem respectively.Finally,the performance evaluation shows that the proposed offloading and resource allocation scheme can improve system performance,save energy,and satisfy the needs of learning users.展开更多
Network slicing is envisioned as one of the key techniques to meet the extremely diversified service requirements of the Internet of Things(IoT)as it provides an enhanced user experience and elastic resource configura...Network slicing is envisioned as one of the key techniques to meet the extremely diversified service requirements of the Internet of Things(IoT)as it provides an enhanced user experience and elastic resource configuration.In the context of slicing enhanced IoT networks,both the Service Provider(SP)and Infrastructure Provider(InP)face challenges of ensuring efficient slice construction and high profit in dynamic environments.These challenges arise from randomly generated and departed slice requests from end-users,uncertain resource availability,and multidimensional resource allocation.Admission and resource allocation for distinct demands of slice requests are the key issues in addressing these challenges and should be handled effectively in dynamic environments.To this end,we propose an Opportunistic Admission and Resource allocation(OAR)policy to deal with the issues of random slicing requests,uncertain resource availability,and heterogeneous multi-resources.The key idea of OAR is to allow the SP to decide whether to accept slice requests immediately or defer them according to the load and price of resources.To cope with the random slice requests and uncertain resource availability,we formulated this issue as a Markov Decision Process(MDP)to obtain the optimal admission policy,with the aim of maximizing the system reward.Furthermore,the buyer-seller game theory approach was adopted to realize the optimal resource allocation,while motivating each SP and InP to maximize their rewards.Our numerical results show that the proposed OAR policy can make reasonable decisions effectively and steadily,and outperforms the baseline schemes in terms of the system reward.展开更多
The rapid advancement of Internet of Things(IoT)technology has brought convenience to people’s lives;however further development of IoT faces serious challenges,such as limited energy and shortage of network spectrum...The rapid advancement of Internet of Things(IoT)technology has brought convenience to people’s lives;however further development of IoT faces serious challenges,such as limited energy and shortage of network spectrum resources.To address the above challenges,this study proposes a simultaneous wireless information and power transfer IoT adaptive time slot resource allocation(SIATS)algorithm.First,an adaptive time slot consisting of periods for sensing,information transmission,and energy harvesting is designed to ensure that the minimum energy harvesting requirement ismet while the maximumuplink and downlink throughputs are obtained.Second,the optimal transmit power and channel assignment of the system are obtained using the Lagrangian dual and gradient descent methods,and the optimal time slot assignment is determined for each IoT device such that the sum of the throughput of all devices is maximized.Simulation results show that the SIATS algorithm performs satisfactorily and provides an increase in the throughput by up to 14.4%compared with that of the fixed time slot allocation(FTS)algorithm.In the case of a large noise variance,the SIATS algorithm has good noise immunity,and the total throughput of the IoT devices obtained using the SIATS algorithm can be improved by up to 34.7%compared with that obtained using the FTS algorithm.展开更多
In a cloud-native era,the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes.However,when encountering continuous workflow requests and unexpected re...In a cloud-native era,the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes.However,when encountering continuous workflow requests and unexpected resource request spikes,the engine is limited to the current workflow load information for resource allocation,which lacks the agility and predictability of resource allocation,resulting in over and underprovisioning resources.This mechanism seriously hinders workflow execution efficiency and leads to high resource waste.To overcome these drawbacks,we propose an adaptive resource allocation scheme named adaptive resource allocation scheme(ARAS)for the Kubernetes-based workflow engines.Considering potential future workflow task requests within the current task pod’s lifecycle,the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios.The ARAS offers resource discovery,resource evaluation,and allocation functionalities and serves as a key component for our tailored workflow engine(KubeAdaptor).By integrating the ARAS into KubeAdaptor for workflow containerized execution,we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS.Compared with the baseline algorithm,experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows,time-saving of 26.4% to 79.86% in the average duration of individual workflow,and an increase of 1% to 16% in centrol processing unit(CPU)and memory resource usage rate.展开更多
Effective resource allocation can exploit the advantage of intelligent reflective surface(IRS)assisted mobile edge computing(MEC)fully.However,it is challenging to balance the limited energy of MTs and the strict dela...Effective resource allocation can exploit the advantage of intelligent reflective surface(IRS)assisted mobile edge computing(MEC)fully.However,it is challenging to balance the limited energy of MTs and the strict delay requirement of their tasks.In this paper,in order to tackle the challenge,we jointly optimize the offloading delay and energy consumption of mobile terminals(MTs)to realize the delay-energy tradeoff in an IRS-assisted MEC network,in which non-orthogonal multiple access(NOMA)and multiantenna are applied to improve spectral efficiency.To achieve the optimal delay-energy tradeoff,an offloading cost minimization model is proposed,in which the edge computing resource allocation,signal detecting vector,uplink transmission power,and IRS phase shift coefficient are needed to be jointly optimized.The optimization of the model is a multi-level fractional problem in complex fields with some coupled high dimension variables.To solve the intractable problem,we decouple the original problem into a computing subproblem and a wireless transmission subproblem based on the uncoupled relationship between different variable types.The computing subproblem is proved convex and the closed-form solution is obtained for the edge computing resource allocation.Further,the wireless transmission subproblem is solved iteratively through decoupling the residual variables.In each iteration,the closed-form solution of residual variables is obtained through different successive convex approximation(SCA)methods.We verify the proposed algorithm can converge to an optimum with polynomial complexity.Simulation results indicate the proposed method achieves average saved costs of 65.64%,11.24%,and 9.49%over three benchmark methods respectively.展开更多
In this paper,multi-UAV trajectory planning and resource allocation are jointly investigated to improve the information freshness for vehicular networks,where the vehicles collect time-critical traffic information by ...In this paper,multi-UAV trajectory planning and resource allocation are jointly investigated to improve the information freshness for vehicular networks,where the vehicles collect time-critical traffic information by on-board sensors and upload to the UAVs through their allocated spectrum resource.We adopt the expected sum age of information(ESAoI)to measure the network-wide information freshness.ESAoI is jointly affected by both the UAVs trajectory and the resource allocation,which are coupled with each other and make the analysis of ESAoI challenging.To tackle this challenge,we introduce a joint trajectory planning and resource allocation procedure,where the UAVs firstly fly to their destinations and then hover to allocate resource blocks(RBs)during a time-slot.Based on this procedure,we formulate a trajectory planning and resource allocation problem for ESAoI minimization.To solve the mixed integer nonlinear programming(MINLP)problem with hybrid decision variables,we propose a TD3 trajectory planning and Round-robin resource allocation(TTPRRA).Specifically,we exploit the exploration and learning ability of the twin delayed deep deterministic policy gradient algorithm(TD3)for UAVs trajectory planning,and utilize Round Robin rule for the optimal resource allocation.With TTP-RRA,the UAVs obtain their flight velocities by sensing the locations and the age of information(AoI)of the vehicles,then allocate the RBs to the vehicles in a descending order of AoI until the remaining RBs are not sufficient to support another successful uploading.Simulation results demonstrate that TTP-RRA outperforms the baseline approaches in terms of ESAoI and average AoI(AAoI).展开更多
We investigate the green resource allocation to minimize the energy consumption of the users in mobile edge computing systems,where task offloading decisions,transmit power,and computation resource allocation are join...We investigate the green resource allocation to minimize the energy consumption of the users in mobile edge computing systems,where task offloading decisions,transmit power,and computation resource allocation are jointly optimized.The considered energy consumption minimization problem is a non-convex mixed-integer nonlinear programming problem,which is challenging to solve.Therefore,we develop a joint search and Successive Convex Approximation(SCA)scheme to optimize the non-integer variables and integer variables in the inner loop and outer loop,respectively.Specifically,in the inner loop,we solve the optimization problem with fixed task offloading decisions.Due to the non-convex objective function and constraints,this optimization problem is still non-convex,and thus we employ the SCA method to obtain a solution satisfying the Karush-Kuhn-Tucker conditions.In the outer loop,we optimize the offloading decisions through exhaustive search.However,the computational complexity of the exhaustive search method is greatly high.To reduce the complexity,a heuristic scheme is proposed to obtain a sub-optimal solution.Simulation results demonstrate the effectiveness of the developed schemes.展开更多
The computational complexity of resource allocation processes,in cognitive radio networks(CRNs),is a major issue to be managed.Furthermore,the complicated solution of the optimal algorithm for handling resource alloca...The computational complexity of resource allocation processes,in cognitive radio networks(CRNs),is a major issue to be managed.Furthermore,the complicated solution of the optimal algorithm for handling resource allocation in CRNs makes it unsuitable to adopt in real-world applications where both cognitive users,CRs,and primary users,PUs,exist in the identical geographical area.Hence,this work offers a primarily price-based power algorithm to reduce computational complexity in uplink scenarioswhile limiting interference to PUs to allowable threshold.Hence,this paper,compared to other frameworks proposed in the literature,proposes a two-step approach to reduce the complexity of the proposed mathematical model.In the first step,the subcarriers are assigned to the users of the CRN,while the cost function includes a pricing scheme to provide better power control algorithm with improved reliability proposed in the second stage.The main contribution of this paper is to lessen the complexity of the proposed algorithm and to offer flexibility in controlling the interference produced to the users of the primary networks,which has been achieved by including a pricing function in the proposed cost function.Finally,the performance of the proposed power and subcarrier algorithm is confirmed for orthogonal frequency-division multiplexing(OFDM).Simulation results prove that the performance of the proposed algorithm is better than other algorithms,albeit with a lesser complexity of O(NM)+O(Nlog(N)).展开更多
Mobile Edge Computing(MEC)and 5G technology allow clients to access computing resources at the network frontier,which paves the way for applying Mobile Augmented Reality(MAR)applications.Under the MEC paradigm,MAR cli...Mobile Edge Computing(MEC)and 5G technology allow clients to access computing resources at the network frontier,which paves the way for applying Mobile Augmented Reality(MAR)applications.Under the MEC paradigm,MAR clients can offload complex tasks to the MEC server and enhance the human perception of the world by merging the received virtual information with the real environment.However,the resource allocation problem arises as a critical challenge in circumstances where several MAR clients compete for limited resources at the network frontier.In this paper,we aim to design an online resource allocation scheme on the MEC server that takes both high quality of experience and good fairness performance for MAR clients into consideration.We first formulate this problem as a Markov decision process and tackle the challenge of applying the deep reinforcement learning paradigm.Then,we propose DRAM,a Deep reinforcement learning-based Resource allocation scheme for mobile Augmented reality service in MEC.We also propose a self-adaptive algorithm on the MAR client that is derived based on the analysis of the MAR service to tackle client adaptation problems.The simulation results demonstrated that DRAM can provide high quality of experience and simultaneously achieve good fairness performance by coordinating with clients’adaptation algorithms.展开更多
With the continuous progress of information technique,assisted driving technology has become an effective technique to avoid traffic accidents.Due to the complex road conditions and the threat of vehicle information b...With the continuous progress of information technique,assisted driving technology has become an effective technique to avoid traffic accidents.Due to the complex road conditions and the threat of vehicle information being attacked and tampered with,it is difficult to ensure information security.This paper uses blockchain to ensure the safety of driving information and introduces mobile edge computing technology to monitor vehicle information and road condition information in real time,calculate the appropriate speed,and plan a reasonable driving route for the driver.To solve these problems,this paper proposes a trusted edge resource allocation framework for assisted driving service,which includes two stages:the blockchain generation stage(the first stage)and assisted driving service stage(the second stage).Furthermore,in the first stage,a delay-and-throughput-oriented block generation model for the mobile terminal is designed.In the second stage,a balanced offloading algorithm for assisted driving service based on edge collaboration is proposed to solve the problems of unbalanced load of cluster mobile edge computing(MEC)servers and low resource utilization of the system.And this paper optimizes the throughput of blockchain and delay of the transportation network through deep reinforcement learning(DRL)algorithm.Finally,compared with joint computation and communication resources’allocation(JCCR)and resource allocation method based on binary offloading(RAB),our proposed scheme can optimize the delay by 7.4%and 26.7%,and support various application services of the vehicular networks more effectively.展开更多
The number of mobile devices accessing wireless networks isskyrocketing due to the rapid advancement of sensors and wireless communicationtechnology. In the upcoming years, it is anticipated that mobile datatraffic wo...The number of mobile devices accessing wireless networks isskyrocketing due to the rapid advancement of sensors and wireless communicationtechnology. In the upcoming years, it is anticipated that mobile datatraffic would rise even more. The development of a new cellular networkparadigm is being driven by the Internet of Things, smart homes, and moresophisticated applications with greater data rates and latency requirements.Resources are being used up quickly due to the steady growth of smartphonedevices andmultimedia apps. Computation offloading to either several distantclouds or close mobile devices has consistently improved the performance ofmobile devices. The computation latency can also be decreased by offloadingcomputing duties to edge servers with a specific level of computing power.Device-to-device (D2D) collaboration can assist in processing small-scaleactivities that are time-sensitive in order to further reduce task delays. The taskoffloading performance is drastically reduced due to the variation of differentperformance capabilities of edge nodes. Therefore, this paper addressed thisproblem and proposed a new method for D2D communication. In thismethod, the time delay is reduced by enabling the edge nodes to exchangedata samples. Simulation results show that the proposed algorithm has betterperformance than traditional algorithm.展开更多
基金Project supported by the Natural Science Foundation of Jilin Province of China(Grant No.20210101417JC).
文摘Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic protocols.However,due to the stringent requirements of the quantum key generation environment,the generated quantum keys are considered valuable,and the slow key generation rate conflicts with the high-speed data transmission in traditional optical networks.In this paper,for the QKD network with a trusted relay,which is mainly based on point-to-point quantum keys and has complex changes in network resources,we aim to allocate resources reasonably for data packet distribution.Firstly,we formulate a linear programming constraint model for the key resource allocation(KRA)problem based on the time-slot scheduling.Secondly,we propose a new scheduling scheme based on the graded key security requirements(GKSR)and a new micro-log key storage algorithm for effective storage and management of key resources.Finally,we propose a key resource consumption(KRC)routing optimization algorithm to properly allocate time slots,routes,and key resources.Simulation results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate,among others.
基金This work was supported in part by the open research fund of National Mobile Communications Research Laboratory,Southeast University(No.2023D11)in part by Sponsored by program for Science&Technology Innovation Talents in Universities of Henan Province(23HASTIT019)+2 种基金in part by Natural Science Foundation of Henan Province(20232300421097)in part by the project funded by China Postdoctoral Science Foundation(2020M682345)in part by the Henan Postdoctoral Foundation(202001015).
文摘In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET)phase first and then cooperatively transmit information to a hybrid access point(AP)in the wireless information transmission(WIT)phase,following which the IRS is deployed to enhance the system performance of theWET andWIT.We maximized the weighted sum-rate problem by jointly optimizing the transmit time slots,power allocations,and the phase shifts of the IRS.Due to the non-convexity of the original problem,a semidefinite programming relaxation-based approach is proposed to convert the formulated problem to a convex optimization framework,which can obtain the optimal global solution.Simulation results demonstrate that the weighted sum throughput of the proposed UC scheme outperforms the non-UC scheme whether equipped with IRS or not.
基金supported by National Key Research and Development Program of China(2018YFC1504502).
文摘Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.
基金the Fundamental Research Program of Guangdong,China,under Grants 2020B1515310023 and 2023A1515011281in part by the National Natural Science Foundation of China under Grant 61571005.
文摘With the rapid development of Network Function Virtualization(NFV),the problem of low resource utilizationin traditional data centers is gradually being addressed.However,existing research does not optimize both localand global allocation of resources in data centers.Hence,we propose an adaptive hybrid optimization strategy thatcombines dynamic programming and neural networks to improve resource utilization and service quality in datacenters.Our approach encompasses a service function chain simulation generator,a parallel architecture servicesystem,a dynamic programming strategy formaximizing the utilization of local server resources,a neural networkfor predicting the global utilization rate of resources and a global resource optimization strategy for bottleneck andredundant resources.With the implementation of our local and global resource allocation strategies,the systemperformance is significantly optimized through simulation.
基金supported by the Key Research and Development Project in Anhui Province of China(Grant No.202304a05020059)the Fundamental Research Funds for the Central Universities of China(Grant No.PA2023GDSK0055)the Project of Anhui Province Economic and Information Bureau(Grant No.JB20099).
文摘Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.
基金supported by The Fundamental Research Funds for the Central Universities(No.2021XD-A01-1)The National Natural Science Foundation of China(No.92067202)。
文摘With the development of artificial intelligence(AI)and 5G technology,the integration of sensing,communication and computing in the Internet of Vehicles(Io V)is becoming a trend.However,the large amount of data transmission and the computing requirements of intelligent tasks lead to the complex resource management problems.In view of the above challenges,this paper proposes a tasks-oriented joint resource allocation scheme(TOJRAS)in the scenario of Io V.First,this paper proposes a system model with sensing,communication,and computing integration for multiple intelligent tasks with different requirements in the Io V.Secondly,joint resource allocation problems for real-time tasks and delay-tolerant tasks in the Io V are constructed respectively,including communication,computing and caching resources.Thirdly,a distributed deep Q-network(DDQN)based algorithm is proposed to solve the optimization problems,and the convergence and complexity of the algorithm are discussed.Finally,the experimental results based on real data sets verify the performance advantages of the proposed resource allocation scheme,compared to the existing ones.The exploration efficiency of our proposed DDQN-based algorithm is improved by at least about 5%,and our proposed resource allocation scheme improves the m AP performance by about 0.15 under resource constraints.
基金supported by the National Natural Science Foundation of China under Grant 92046001,61962009the Doctor Scientific Research Fund of Zhengzhou University of Light Industry underGrant 2021BSJJ033Key ScientificResearch Project of Colleges andUniversities in Henan Province(CN)under Grant No.22A413010.
文摘Cold-chain logistics system(CCLS)plays the role of collecting and managing the logistics data of frozen food.However,there always exist problems of information loss,data tampering,and privacy leakage in traditional centralized systems,which influence frozen food security and people’s health.The centralized management form impedes the development of the cold-chain logistics industry and weakens logistics data availability.This paper first introduces a distributed CCLS based on blockchain technology to solve the centralized management problem.This system aggregates the production base,storage,transport,detection,processing,and consumer to form a cold-chain logistics union.The blockchain ledger guarantees that the logistics data cannot be tampered with and establishes a traceability mechanism for food safety incidents.Meanwhile,to improve the value of logistics data,a Stackelberg game-based resource allocation model has been proposed between the logistics data resource provider and the consumer.The competition between resource price and volume balances the resource supplement and consumption.This model can help to achieve an optimal resource price when the Stackelberg game obtains Nash equilibrium.The two participants also can maximize their revenues with the optimal resource price and volume by utilizing the backward induction method.Then,the performance evaluations of transaction throughput and latency show that the proposed distributed CCLS is more secure and stable.The simulations about the variation trend of data price and amount,optimal benefits,and total benefits comparison of different forms show that the resource allocation model is more efficient and practical.Moreover,the blockchain-based CCLS and Stackelberg game-based resource allocation model also can promote the value of logistic data and improve social benefits.
基金the National Natural Science Foundation of China(Grant No.61971057).
文摘To solve the contradiction between limited spectrum resources and increasing communication demand,this paper proposes a wireless resource allocation scheme based on the Deep Q Network(DQN)to allocate radio resources in a downlink multi-user cognitive radio(CR)network with slicing.Secondary users(SUs)are multiplexed using non-orthogonal multiple access(NOMA).The SUs use the hybrid spectrum access mode to improve the spectral efficiency(SE).Considering the demand for multiple services,the enhanced mobile broadband(eMBB)slice and ultrareliable low-latency communication(URLLC)slice were established.The proposed scheme can maximize the SE while ensuring Quality of Service(QoS)for the users.This study established a mapping relationship between resource allocation and the DQN algorithm in the CR-NOMA network.According to the signal-to-interference-plusnoise ratio(SINR)of the primary users(PUs),the proposed scheme can output the optimal channel selection and power allocation.The simulation results reveal that the proposed scheme can converge faster and obtain higher rewards compared with the Q-Learning scheme.Additionally,the proposed scheme has better SE than both the overlay and underlay only modes.
基金supported by the National Natural Science Foundation of China under Grant No.61971057。
文摘Heterogeneous base station deployment enables to provide high capacity and wide area coverage.Network slicing makes it possible to allocate wireless resource for heterogeneous services on demand.These two promising technologies contribute to the unprecedented service in 5G.We establish a multiservice heterogeneous network model,which aims to raise the transmission rate under the delay constraints for active control terminals,and optimize the energy efficiency for passive network terminals.A policygradient-based deep reinforcement learning algorithm is proposed to make decisions on user association and power control in the continuous action space.Simulation results indicate the good convergence of the algorithm,and higher reward is obtained compared with other baselines.
文摘Mobile learning has evolved into a new format of education based on communication and computer technology that is favored by an increasing number of learning users thanks to the development of wireless communication networks,mobile edge computing,artificial intelligence,and mobile devices.However,due to the constrained data processing capacity of mobile devices,efficient and effective interactive mobile learning is a challenge.Therefore,for mobile learning,we propose a"Cloud,Edge and End"fusion system architecture.Through task offloading and resource allocation for edge-enabled mobile learning to reduce the time and energy consumption of user equipment.Then,we present the proposed solutions that uses the minimum cost maximum flow(MCMF)algorithm to deal with the offloading problem and the deep Q network(DQN)algorithm to deal with the resource allocation problem respectively.Finally,the performance evaluation shows that the proposed offloading and resource allocation scheme can improve system performance,save energy,and satisfy the needs of learning users.
基金This work was supported in part by the Chongqing Technological Innovation and Application Development Projects under Grant cstc2019jscx-msxm1322,in part by the Zhejiang Lab under Grant 2021KF0AB03in part by the National Natural Science Foundation of China under Grant 62071091.
文摘Network slicing is envisioned as one of the key techniques to meet the extremely diversified service requirements of the Internet of Things(IoT)as it provides an enhanced user experience and elastic resource configuration.In the context of slicing enhanced IoT networks,both the Service Provider(SP)and Infrastructure Provider(InP)face challenges of ensuring efficient slice construction and high profit in dynamic environments.These challenges arise from randomly generated and departed slice requests from end-users,uncertain resource availability,and multidimensional resource allocation.Admission and resource allocation for distinct demands of slice requests are the key issues in addressing these challenges and should be handled effectively in dynamic environments.To this end,we propose an Opportunistic Admission and Resource allocation(OAR)policy to deal with the issues of random slicing requests,uncertain resource availability,and heterogeneous multi-resources.The key idea of OAR is to allow the SP to decide whether to accept slice requests immediately or defer them according to the load and price of resources.To cope with the random slice requests and uncertain resource availability,we formulated this issue as a Markov Decision Process(MDP)to obtain the optimal admission policy,with the aim of maximizing the system reward.Furthermore,the buyer-seller game theory approach was adopted to realize the optimal resource allocation,while motivating each SP and InP to maximize their rewards.Our numerical results show that the proposed OAR policy can make reasonable decisions effectively and steadily,and outperforms the baseline schemes in terms of the system reward.
基金supported in part by Sub Project of National Key Research and Development Plan in 2020.No.2020YFC1511704Beijing Information Science&Technology University.Nos.2020KYNH212,2021CGZH302+1 种基金Beijing Science and Technology Project(Grant No.Z211100004421009)in part by the National Natural Science Foundation of China(Grant No.61971048).
文摘The rapid advancement of Internet of Things(IoT)technology has brought convenience to people’s lives;however further development of IoT faces serious challenges,such as limited energy and shortage of network spectrum resources.To address the above challenges,this study proposes a simultaneous wireless information and power transfer IoT adaptive time slot resource allocation(SIATS)algorithm.First,an adaptive time slot consisting of periods for sensing,information transmission,and energy harvesting is designed to ensure that the minimum energy harvesting requirement ismet while the maximumuplink and downlink throughputs are obtained.Second,the optimal transmit power and channel assignment of the system are obtained using the Lagrangian dual and gradient descent methods,and the optimal time slot assignment is determined for each IoT device such that the sum of the throughput of all devices is maximized.Simulation results show that the SIATS algorithm performs satisfactorily and provides an increase in the throughput by up to 14.4%compared with that of the fixed time slot allocation(FTS)algorithm.In the case of a large noise variance,the SIATS algorithm has good noise immunity,and the total throughput of the IoT devices obtained using the SIATS algorithm can be improved by up to 34.7%compared with that obtained using the FTS algorithm.
基金supported by the National Natural Science Foundation of China(61873030,62002019).
文摘In a cloud-native era,the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes.However,when encountering continuous workflow requests and unexpected resource request spikes,the engine is limited to the current workflow load information for resource allocation,which lacks the agility and predictability of resource allocation,resulting in over and underprovisioning resources.This mechanism seriously hinders workflow execution efficiency and leads to high resource waste.To overcome these drawbacks,we propose an adaptive resource allocation scheme named adaptive resource allocation scheme(ARAS)for the Kubernetes-based workflow engines.Considering potential future workflow task requests within the current task pod’s lifecycle,the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios.The ARAS offers resource discovery,resource evaluation,and allocation functionalities and serves as a key component for our tailored workflow engine(KubeAdaptor).By integrating the ARAS into KubeAdaptor for workflow containerized execution,we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS.Compared with the baseline algorithm,experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows,time-saving of 26.4% to 79.86% in the average duration of individual workflow,and an increase of 1% to 16% in centrol processing unit(CPU)and memory resource usage rate.
基金funded by 2020 Industrial Technology Foundation Public Service Platform Project(2020-0105-2-1)Foshan Science and Technology Innovation Special Fund Project(No.BK20AF004).
文摘Effective resource allocation can exploit the advantage of intelligent reflective surface(IRS)assisted mobile edge computing(MEC)fully.However,it is challenging to balance the limited energy of MTs and the strict delay requirement of their tasks.In this paper,in order to tackle the challenge,we jointly optimize the offloading delay and energy consumption of mobile terminals(MTs)to realize the delay-energy tradeoff in an IRS-assisted MEC network,in which non-orthogonal multiple access(NOMA)and multiantenna are applied to improve spectral efficiency.To achieve the optimal delay-energy tradeoff,an offloading cost minimization model is proposed,in which the edge computing resource allocation,signal detecting vector,uplink transmission power,and IRS phase shift coefficient are needed to be jointly optimized.The optimization of the model is a multi-level fractional problem in complex fields with some coupled high dimension variables.To solve the intractable problem,we decouple the original problem into a computing subproblem and a wireless transmission subproblem based on the uncoupled relationship between different variable types.The computing subproblem is proved convex and the closed-form solution is obtained for the edge computing resource allocation.Further,the wireless transmission subproblem is solved iteratively through decoupling the residual variables.In each iteration,the closed-form solution of residual variables is obtained through different successive convex approximation(SCA)methods.We verify the proposed algorithm can converge to an optimum with polynomial complexity.Simulation results indicate the proposed method achieves average saved costs of 65.64%,11.24%,and 9.49%over three benchmark methods respectively.
基金supported in part by the Project of International Cooperation and Exchanges NSFC under Grant No.61860206005in part by the Joint Funds of the NSFC under Grant No.U22A2003.
文摘In this paper,multi-UAV trajectory planning and resource allocation are jointly investigated to improve the information freshness for vehicular networks,where the vehicles collect time-critical traffic information by on-board sensors and upload to the UAVs through their allocated spectrum resource.We adopt the expected sum age of information(ESAoI)to measure the network-wide information freshness.ESAoI is jointly affected by both the UAVs trajectory and the resource allocation,which are coupled with each other and make the analysis of ESAoI challenging.To tackle this challenge,we introduce a joint trajectory planning and resource allocation procedure,where the UAVs firstly fly to their destinations and then hover to allocate resource blocks(RBs)during a time-slot.Based on this procedure,we formulate a trajectory planning and resource allocation problem for ESAoI minimization.To solve the mixed integer nonlinear programming(MINLP)problem with hybrid decision variables,we propose a TD3 trajectory planning and Round-robin resource allocation(TTPRRA).Specifically,we exploit the exploration and learning ability of the twin delayed deep deterministic policy gradient algorithm(TD3)for UAVs trajectory planning,and utilize Round Robin rule for the optimal resource allocation.With TTP-RRA,the UAVs obtain their flight velocities by sensing the locations and the age of information(AoI)of the vehicles,then allocate the RBs to the vehicles in a descending order of AoI until the remaining RBs are not sufficient to support another successful uploading.Simulation results demonstrate that TTP-RRA outperforms the baseline approaches in terms of ESAoI and average AoI(AAoI).
基金This work was supported by National Key Research and Development Program of China under Grant 2021YFF0307602National Natural Science Foundation of China under Grant 61941104Beijing Nova Program under Grant Z211100002121161.
文摘We investigate the green resource allocation to minimize the energy consumption of the users in mobile edge computing systems,where task offloading decisions,transmit power,and computation resource allocation are jointly optimized.The considered energy consumption minimization problem is a non-convex mixed-integer nonlinear programming problem,which is challenging to solve.Therefore,we develop a joint search and Successive Convex Approximation(SCA)scheme to optimize the non-integer variables and integer variables in the inner loop and outer loop,respectively.Specifically,in the inner loop,we solve the optimization problem with fixed task offloading decisions.Due to the non-convex objective function and constraints,this optimization problem is still non-convex,and thus we employ the SCA method to obtain a solution satisfying the Karush-Kuhn-Tucker conditions.In the outer loop,we optimize the offloading decisions through exhaustive search.However,the computational complexity of the exhaustive search method is greatly high.To reduce the complexity,a heuristic scheme is proposed to obtain a sub-optimal solution.Simulation results demonstrate the effectiveness of the developed schemes.
基金Authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under Grant Number RGP.2/111/43supported in part by the Agencia Estatal de Investigación,Ministerio de Ciencia e Innovación(MCIN/AEI/10.13039/501100011033)+1 种基金the R+D+i Project under Grant PID2020-115323RB-C31in part by the Grant from the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU under Grant UNICO-5G I+D/AROMA3D-Hybrid TSI-063000-2021-71.
文摘The computational complexity of resource allocation processes,in cognitive radio networks(CRNs),is a major issue to be managed.Furthermore,the complicated solution of the optimal algorithm for handling resource allocation in CRNs makes it unsuitable to adopt in real-world applications where both cognitive users,CRs,and primary users,PUs,exist in the identical geographical area.Hence,this work offers a primarily price-based power algorithm to reduce computational complexity in uplink scenarioswhile limiting interference to PUs to allowable threshold.Hence,this paper,compared to other frameworks proposed in the literature,proposes a two-step approach to reduce the complexity of the proposed mathematical model.In the first step,the subcarriers are assigned to the users of the CRN,while the cost function includes a pricing scheme to provide better power control algorithm with improved reliability proposed in the second stage.The main contribution of this paper is to lessen the complexity of the proposed algorithm and to offer flexibility in controlling the interference produced to the users of the primary networks,which has been achieved by including a pricing function in the proposed cost function.Finally,the performance of the proposed power and subcarrier algorithm is confirmed for orthogonal frequency-division multiplexing(OFDM).Simulation results prove that the performance of the proposed algorithm is better than other algorithms,albeit with a lesser complexity of O(NM)+O(Nlog(N)).
基金supported by the National Key R&D Program of China(2020YFB1807804,2020YFB1807800)the National Nat-ural Science Foundation of China(NSFC)(62001087,62072079,U20A20156).
文摘Mobile Edge Computing(MEC)and 5G technology allow clients to access computing resources at the network frontier,which paves the way for applying Mobile Augmented Reality(MAR)applications.Under the MEC paradigm,MAR clients can offload complex tasks to the MEC server and enhance the human perception of the world by merging the received virtual information with the real environment.However,the resource allocation problem arises as a critical challenge in circumstances where several MAR clients compete for limited resources at the network frontier.In this paper,we aim to design an online resource allocation scheme on the MEC server that takes both high quality of experience and good fairness performance for MAR clients into consideration.We first formulate this problem as a Markov decision process and tackle the challenge of applying the deep reinforcement learning paradigm.Then,we propose DRAM,a Deep reinforcement learning-based Resource allocation scheme for mobile Augmented reality service in MEC.We also propose a self-adaptive algorithm on the MAR client that is derived based on the analysis of the MAR service to tackle client adaptation problems.The simulation results demonstrated that DRAM can provide high quality of experience and simultaneously achieve good fairness performance by coordinating with clients’adaptation algorithms.
基金supported by State Grid Corporation of China Science and Technology Project“Key Technology and Application of New Multi-Mode Intelligent Network for State Grid”(5700-202024176A-0-0-00).
文摘With the continuous progress of information technique,assisted driving technology has become an effective technique to avoid traffic accidents.Due to the complex road conditions and the threat of vehicle information being attacked and tampered with,it is difficult to ensure information security.This paper uses blockchain to ensure the safety of driving information and introduces mobile edge computing technology to monitor vehicle information and road condition information in real time,calculate the appropriate speed,and plan a reasonable driving route for the driver.To solve these problems,this paper proposes a trusted edge resource allocation framework for assisted driving service,which includes two stages:the blockchain generation stage(the first stage)and assisted driving service stage(the second stage).Furthermore,in the first stage,a delay-and-throughput-oriented block generation model for the mobile terminal is designed.In the second stage,a balanced offloading algorithm for assisted driving service based on edge collaboration is proposed to solve the problems of unbalanced load of cluster mobile edge computing(MEC)servers and low resource utilization of the system.And this paper optimizes the throughput of blockchain and delay of the transportation network through deep reinforcement learning(DRL)algorithm.Finally,compared with joint computation and communication resources’allocation(JCCR)and resource allocation method based on binary offloading(RAB),our proposed scheme can optimize the delay by 7.4%and 26.7%,and support various application services of the vehicular networks more effectively.
文摘The number of mobile devices accessing wireless networks isskyrocketing due to the rapid advancement of sensors and wireless communicationtechnology. In the upcoming years, it is anticipated that mobile datatraffic would rise even more. The development of a new cellular networkparadigm is being driven by the Internet of Things, smart homes, and moresophisticated applications with greater data rates and latency requirements.Resources are being used up quickly due to the steady growth of smartphonedevices andmultimedia apps. Computation offloading to either several distantclouds or close mobile devices has consistently improved the performance ofmobile devices. The computation latency can also be decreased by offloadingcomputing duties to edge servers with a specific level of computing power.Device-to-device (D2D) collaboration can assist in processing small-scaleactivities that are time-sensitive in order to further reduce task delays. The taskoffloading performance is drastically reduced due to the variation of differentperformance capabilities of edge nodes. Therefore, this paper addressed thisproblem and proposed a new method for D2D communication. In thismethod, the time delay is reduced by enabling the edge nodes to exchangedata samples. Simulation results show that the proposed algorithm has betterperformance than traditional algorithm.
