Satellite Internet,as a strategic public information infrastructure,can effectively bridge the limitations of traditional terrestrial network coverage,support global coverage and deep space exploration,and greatly enh...Satellite Internet,as a strategic public information infrastructure,can effectively bridge the limitations of traditional terrestrial network coverage,support global coverage and deep space exploration,and greatly enhance the range of network information services accessible to humans.With the transition of terrestrial mobile communication networks from the 5G era,which provides access to information anywhere,to the 6G era,which seeks to connect everything,the construction of satellite Internet,which promises a“network reaching everywhere and service is ubiquitous”,has become the consensus of the industry’s development and the focus of global scientific and technological innovation.展开更多
Satellite Internet(SI)provides broadband access as a critical information infrastructure in 6G.However,with the integration of the terrestrial Internet,the influx of massive terrestrial traffic will bring significant ...Satellite Internet(SI)provides broadband access as a critical information infrastructure in 6G.However,with the integration of the terrestrial Internet,the influx of massive terrestrial traffic will bring significant threats to SI,among which DDoS attack will intensify the erosion of limited bandwidth resources.Therefore,this paper proposes a DDoS attack tracking scheme using a multi-round iterative Viterbi algorithm to achieve high-accuracy attack path reconstruction and fast internal source locking,protecting SI from the source.Firstly,to reduce communication overhead,the logarithmic representation of the traffic volume is added to the digests after modeling SI,generating the lightweight deviation degree to construct the observation probability matrix for the Viterbi algorithm.Secondly,the path node matrix is expanded to multi-index matrices in the Viterbi algorithm to store index information for all probability values,deriving the path with non-repeatability and maximum probability.Finally,multiple rounds of iterative Viterbi tracking are performed locally to track DDoS attack based on trimming tracking results.Simulation and experimental results show that the scheme can achieve 96.8%tracking accuracy of external and internal DDoS attack at 2.5 seconds,with the communication overhead at 268KB/s,effectively protecting the limited bandwidth resources of SI.展开更多
The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-rel...The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.展开更多
Satellite Internet,as a strategic public information infrastructure,can effectively bridge the limitations of traditional terrestrial network coverage,support global coverage and deep space exploration,and greatly enh...Satellite Internet,as a strategic public information infrastructure,can effectively bridge the limitations of traditional terrestrial network coverage,support global coverage and deep space exploration,and greatly enhance the range of network information services accessible to humans.With the transition of terrestrial mobile communication networks from the 5G era,which provides access to information anywhere,to the 6G era,which seeks to connect everything,the construction of satellite Internet,which promises a"network reaching everywhere and service is ubiquitous",has become the consensus of the industry's development and the focus of global scientific and technological innovation.展开更多
The Internet of Things(IoT)connects objects to Internet through sensor devices,radio frequency identification devices and other information collection and processing devices to realize information interaction.IoT is w...The Internet of Things(IoT)connects objects to Internet through sensor devices,radio frequency identification devices and other information collection and processing devices to realize information interaction.IoT is widely used in many fields,including intelligent transportation,intelligent healthcare,intelligent home and industry.In these fields,IoT devices connected via high-speed internet for efficient and reliable communications and faster response times.展开更多
Internet-based interventions(IBIs)for behavioural health have been prevalent for over two decades,and a growing proportion of individuals with mental health concerns prefer these emerging digital alternatives.However,...Internet-based interventions(IBIs)for behavioural health have been prevalent for over two decades,and a growing proportion of individuals with mental health concerns prefer these emerging digital alternatives.However,the effectiveness and acceptability of IBIs for various mental health disorders continue to be subject to scholarly debate.We performed an umbrella review of meta-analyses(MAs),conducting literature searches in PubMed,Web of Science,Embase,Cochrane and Ovid Medline from their inception to 17 January 2023.A total of 87MAs,reporting on 1683 randomised controlled trials and 295589 patients,were included.The results indicated that IBIs had a moderate effect on anxiety disorder(standardised mean difference(SMD)=0.53,95%CI 0.44 to 0.62)and post-traumatic stress disorder(PTSD)(SMD=0.63,95%CI 0.38 to 0.89).In contrast,the efficacy on depression(SMD=0.45,95%CI 0.39 to 0.52),addiction(SMD=0.23,95%CI 0.16 to 0.31),suicidal ideation(SMD=0.23,95%CI 0.16 to 0.30),stress(SMD=0.41,95%CI 0.33 to 0.48)and obsessive-compulsive disorder(SMD=0.47,95%CI 0.22 to 0.73)was relatively small.However,no significant effects were observed for personality disorders(SMD=0.07,95%CI-0.13 to 0.26).Our findings suggest a significant association between IBIs and improved mental health outcomes,with particular effectiveness noted in treating anxiety disorders and PTSD.However,it is noteworthy that the effectiveness of IBIs was impacted by high dropout rates during treatment.Furthermore,our results indicated that guided IBIs proved to be more effective than unguided ones,playing a positive role in reducing dropout rates and enhancing patient adherence rates.展开更多
There are numerous terminals in the satellite Internet of Things(IoT).To save cost and reduce power consumption,the system needs terminals to catch the characteristics of low power consumption and light control.The re...There are numerous terminals in the satellite Internet of Things(IoT).To save cost and reduce power consumption,the system needs terminals to catch the characteristics of low power consumption and light control.The regular random access(RA)protocols may generate large amounts of collisions,which degrade the system throughout severally.The near-far effect and power control technologies are not applicable in capture effect to obtain power difference,resulting in the collisions that cannot be separated.In fact,the optimal design at the receiving end can also realize the condition of packet power domain separation,but there are few relevant researches.In this paper,an auxiliary beamforming scheme is proposed for power domain signal separation.It adds an auxiliary reception beam based on the conventional beam,utilizing the correlation of packets in time-frequency domain between the main and auxiliary beam to complete signal separation.The roll-off belt of auxiliary beam is used to create the carrier-to-noise ratio(CNR)difference.This paper uses the genetic algorithm to optimize the auxiliary beam direction.Simulation results show that the proposed scheme outperforms slotted ALOHA(SA)in terms of system throughput per-formance and without bringing terminals additional control burden.展开更多
One of the major challenges arising in internet of military things(IoMT)is accommodating massive connectivity while providing guaranteed quality of service(QoS)in terms of ultra-high reliability.In this regard,this pa...One of the major challenges arising in internet of military things(IoMT)is accommodating massive connectivity while providing guaranteed quality of service(QoS)in terms of ultra-high reliability.In this regard,this paper presents a class of code-domain nonorthogonal multiple accesses(NOMAs)for uplink ultra reliable networking of massive IoMT based on tactical datalink such as Link-16 and joint tactical information distribution system(JTIDS).In the considered scenario,a satellite equipped with Nr antennas servers K devices including vehicles,drones,ships,sensors,handset radios,etc.Nonorthogonal coded modulation,a special form of multiple input multiple output(MIMO)-NOMA is proposed.The discussion starts with evaluating the output signal to interference-plus-noise(SINR)of receiver filter,leading to the unveiling of a closed-form expression for overloading systems as the number of users is significantly larger than the number of devices admitted such that massive connectivity is rendered.The expression allows for the development of simple yet successful interference suppression based on power allocation and phase shaping techniques that maximizes the sum rate since it is equivalent to fixed-point programming as can be proved.The proposed design is exemplified by nonlinear modulation schemes such as minimum shift keying(MSK)and Gaussian MSK(GMSK),two pivotal modulation formats in IoMT standards such as Link-16 and JITDS.Numerical results show that near capacity performance is offered.Fortunately,the performance is obtained using simple forward error corrections(FECs)of higher coding rate than existing schemes do,while the transmit power is reduced by 6 dB.The proposed design finds wide applications not only in IoMT but also in deep space communications,where ultra reliability and massive connectivity is a keen concern.展开更多
The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initiall...The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.展开更多
With the proportion of intelligent services in the industrial internet of things(IIoT)rising rapidly,its data dependency and decomposability increase the difficulty of scheduling computing resources.In this paper,we p...With the proportion of intelligent services in the industrial internet of things(IIoT)rising rapidly,its data dependency and decomposability increase the difficulty of scheduling computing resources.In this paper,we propose an intelligent service computing framework.In the framework,we take the long-term rewards of its important participants,edge service providers,as the optimization goal,which is related to service delay and computing cost.Considering the different update frequencies of data deployment and service offloading,double-timescale reinforcement learning is utilized in the framework.In the small-scale strategy,the frequent concurrency of services and the difference in service time lead to the fuzzy relationship between reward and action.To solve the fuzzy reward problem,a reward mapping-based reinforcement learning(RMRL)algorithm is proposed,which enables the agent to learn the relationship between reward and action more clearly.The large time scale strategy adopts the improved Monte Carlo tree search(MCTS)algorithm to improve the learning speed.The simulation results show that the strategy is superior to popular reinforcement learning algorithms such as double Q-learning(DDQN)and dueling Q-learning(dueling-DQN)in learning speed,and the reward is also increased by 14%.展开更多
Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve it...Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve its reliability.A data enhancement module(DEM)is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC.Multimodal network is designed to have multiple residual blocks,where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction.Moreover,a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM.Since different model may output different results,cooperative classifier is designed to avoid the randomness of single model and improve the reliability.Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.展开更多
Effective control of time-sensitive industrial applications depends on the real-time transmission of data from underlying sensors.Quantifying the data freshness through age of information(AoI),in this paper,we jointly...Effective control of time-sensitive industrial applications depends on the real-time transmission of data from underlying sensors.Quantifying the data freshness through age of information(AoI),in this paper,we jointly design sampling and non-slot based scheduling policies to minimize the maximum time-average age of information(MAoI)among sensors with the constraints of average energy cost and finite queue stability.To overcome the intractability involving high couplings of such a complex stochastic process,we first focus on the single-sensor time-average AoI optimization problem and convert the constrained Markov decision process(CMDP)into an unconstrained Markov decision process(MDP)by the Lagrangian method.With the infinite-time average energy and AoI expression expended as the Bellman equation,the singlesensor time-average AoI optimization problem can be approached through the steady-state distribution probability.Further,we propose a low-complexity sub-optimal sampling and semi-distributed scheduling scheme for the multi-sensor scenario.The simulation results show that the proposed scheme reduces the MAoI significantly while achieving a balance between the sampling rate and service rate for multiple sensors.展开更多
Puncturing has been recognized as a promising technology to cope with the coexistence problem of enhanced mobile broadband(eMBB) and ultra-reliable low latency communications(URLLC)traffic. However, the steady perform...Puncturing has been recognized as a promising technology to cope with the coexistence problem of enhanced mobile broadband(eMBB) and ultra-reliable low latency communications(URLLC)traffic. However, the steady performance of eMBB traffic while meeting the requirements of URLLC traffic with puncturing is a major challenge in some realistic scenarios. In this paper, we pay attention to the timely and energy-efficient processing for eMBB traffic in the industrial Internet of Things(IIoT), where mobile edge computing(MEC) is employed for data processing. Specifically, the performance of eMBB traffic and URLLC traffic in a MEC-based IIoT system is ensured by setting the threshold of tolerable delay and outage probability, respectively. Furthermore,considering the limited energy supply, an energy minimization problem of eMBB device is formulated under the above constraints, by jointly optimizing the resource blocks(RBs) punctured by URLLC traffic, data offloading and transmit power of eMBB device. With Markov's inequality, the problem is reformulated by transforming the probabilistic outage constraint into a deterministic constraint. Meanwhile, an iterative energy minimization algorithm(IEMA) is proposed.Simulation results demonstrate that our algorithm has a significant reduction in the energy consumption for eMBB device and achieves a better overall effect compared to several benchmarks.展开更多
文摘Satellite Internet,as a strategic public information infrastructure,can effectively bridge the limitations of traditional terrestrial network coverage,support global coverage and deep space exploration,and greatly enhance the range of network information services accessible to humans.With the transition of terrestrial mobile communication networks from the 5G era,which provides access to information anywhere,to the 6G era,which seeks to connect everything,the construction of satellite Internet,which promises a“network reaching everywhere and service is ubiquitous”,has become the consensus of the industry’s development and the focus of global scientific and technological innovation.
基金supported by the National Key R&D Program of China(Grant No.2022YFA1005000)the National Natural Science Foundation of China(Grant No.62025110 and 62101308).
文摘Satellite Internet(SI)provides broadband access as a critical information infrastructure in 6G.However,with the integration of the terrestrial Internet,the influx of massive terrestrial traffic will bring significant threats to SI,among which DDoS attack will intensify the erosion of limited bandwidth resources.Therefore,this paper proposes a DDoS attack tracking scheme using a multi-round iterative Viterbi algorithm to achieve high-accuracy attack path reconstruction and fast internal source locking,protecting SI from the source.Firstly,to reduce communication overhead,the logarithmic representation of the traffic volume is added to the digests after modeling SI,generating the lightweight deviation degree to construct the observation probability matrix for the Viterbi algorithm.Secondly,the path node matrix is expanded to multi-index matrices in the Viterbi algorithm to store index information for all probability values,deriving the path with non-repeatability and maximum probability.Finally,multiple rounds of iterative Viterbi tracking are performed locally to track DDoS attack based on trimming tracking results.Simulation and experimental results show that the scheme can achieve 96.8%tracking accuracy of external and internal DDoS attack at 2.5 seconds,with the communication overhead at 268KB/s,effectively protecting the limited bandwidth resources of SI.
基金supported by National Natural Science Foundation of China(Grant No.62071377,62101442,62201456)Natural Science Foundation of Shaanxi Province(Grant No.2023-YBGY-036,2022JQ-687)The Graduate Student Innovation Foundation Project of Xi’an University of Posts and Telecommunications under Grant CXJJDL2022003.
文摘The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.
文摘Satellite Internet,as a strategic public information infrastructure,can effectively bridge the limitations of traditional terrestrial network coverage,support global coverage and deep space exploration,and greatly enhance the range of network information services accessible to humans.With the transition of terrestrial mobile communication networks from the 5G era,which provides access to information anywhere,to the 6G era,which seeks to connect everything,the construction of satellite Internet,which promises a"network reaching everywhere and service is ubiquitous",has become the consensus of the industry's development and the focus of global scientific and technological innovation.
文摘The Internet of Things(IoT)connects objects to Internet through sensor devices,radio frequency identification devices and other information collection and processing devices to realize information interaction.IoT is widely used in many fields,including intelligent transportation,intelligent healthcare,intelligent home and industry.In these fields,IoT devices connected via high-speed internet for efficient and reliable communications and faster response times.
基金supported by Anhui Province University Scientific Research Projects(2023AH040086)Key Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention(SYS2023B08).
文摘Internet-based interventions(IBIs)for behavioural health have been prevalent for over two decades,and a growing proportion of individuals with mental health concerns prefer these emerging digital alternatives.However,the effectiveness and acceptability of IBIs for various mental health disorders continue to be subject to scholarly debate.We performed an umbrella review of meta-analyses(MAs),conducting literature searches in PubMed,Web of Science,Embase,Cochrane and Ovid Medline from their inception to 17 January 2023.A total of 87MAs,reporting on 1683 randomised controlled trials and 295589 patients,were included.The results indicated that IBIs had a moderate effect on anxiety disorder(standardised mean difference(SMD)=0.53,95%CI 0.44 to 0.62)and post-traumatic stress disorder(PTSD)(SMD=0.63,95%CI 0.38 to 0.89).In contrast,the efficacy on depression(SMD=0.45,95%CI 0.39 to 0.52),addiction(SMD=0.23,95%CI 0.16 to 0.31),suicidal ideation(SMD=0.23,95%CI 0.16 to 0.30),stress(SMD=0.41,95%CI 0.33 to 0.48)and obsessive-compulsive disorder(SMD=0.47,95%CI 0.22 to 0.73)was relatively small.However,no significant effects were observed for personality disorders(SMD=0.07,95%CI-0.13 to 0.26).Our findings suggest a significant association between IBIs and improved mental health outcomes,with particular effectiveness noted in treating anxiety disorders and PTSD.However,it is noteworthy that the effectiveness of IBIs was impacted by high dropout rates during treatment.Furthermore,our results indicated that guided IBIs proved to be more effective than unguided ones,playing a positive role in reducing dropout rates and enhancing patient adherence rates.
基金supported by the National Science Foundation of China(No.U21A20450)Natural Science Foundation of Jiangsu Province Major Project(No.BK20192002)+1 种基金National Natural Science Foundation of China(No.61971440)National Natural Science Foundation of China(No.62271266).
文摘There are numerous terminals in the satellite Internet of Things(IoT).To save cost and reduce power consumption,the system needs terminals to catch the characteristics of low power consumption and light control.The regular random access(RA)protocols may generate large amounts of collisions,which degrade the system throughout severally.The near-far effect and power control technologies are not applicable in capture effect to obtain power difference,resulting in the collisions that cannot be separated.In fact,the optimal design at the receiving end can also realize the condition of packet power domain separation,but there are few relevant researches.In this paper,an auxiliary beamforming scheme is proposed for power domain signal separation.It adds an auxiliary reception beam based on the conventional beam,utilizing the correlation of packets in time-frequency domain between the main and auxiliary beam to complete signal separation.The roll-off belt of auxiliary beam is used to create the carrier-to-noise ratio(CNR)difference.This paper uses the genetic algorithm to optimize the auxiliary beam direction.Simulation results show that the proposed scheme outperforms slotted ALOHA(SA)in terms of system throughput per-formance and without bringing terminals additional control burden.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.61601346 and 62377039)the Natural Science Basic Research Plan in Shaanxi Province of China(Grant No.2018JQ6044)+2 种基金the Ministry of Industry and Information Technology of the People's Republic of China(Grant No.2023-276-1-1)the Fundamental Research Funds for the Central Universities,Northwestern Polytechnical University(Grant No.31020180QD089)the Aeronautical Science Foundation of China(Grant Nos.20200043053004 and 20200043053005)。
文摘One of the major challenges arising in internet of military things(IoMT)is accommodating massive connectivity while providing guaranteed quality of service(QoS)in terms of ultra-high reliability.In this regard,this paper presents a class of code-domain nonorthogonal multiple accesses(NOMAs)for uplink ultra reliable networking of massive IoMT based on tactical datalink such as Link-16 and joint tactical information distribution system(JTIDS).In the considered scenario,a satellite equipped with Nr antennas servers K devices including vehicles,drones,ships,sensors,handset radios,etc.Nonorthogonal coded modulation,a special form of multiple input multiple output(MIMO)-NOMA is proposed.The discussion starts with evaluating the output signal to interference-plus-noise(SINR)of receiver filter,leading to the unveiling of a closed-form expression for overloading systems as the number of users is significantly larger than the number of devices admitted such that massive connectivity is rendered.The expression allows for the development of simple yet successful interference suppression based on power allocation and phase shaping techniques that maximizes the sum rate since it is equivalent to fixed-point programming as can be proved.The proposed design is exemplified by nonlinear modulation schemes such as minimum shift keying(MSK)and Gaussian MSK(GMSK),two pivotal modulation formats in IoMT standards such as Link-16 and JITDS.Numerical results show that near capacity performance is offered.Fortunately,the performance is obtained using simple forward error corrections(FECs)of higher coding rate than existing schemes do,while the transmit power is reduced by 6 dB.The proposed design finds wide applications not only in IoMT but also in deep space communications,where ultra reliability and massive connectivity is a keen concern.
基金supported by the National Key Research and Development Program of China(grant number 2019YFE0123600)。
文摘The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.
基金supported by the National Natural Science Foundation of China(No.62171051)。
文摘With the proportion of intelligent services in the industrial internet of things(IIoT)rising rapidly,its data dependency and decomposability increase the difficulty of scheduling computing resources.In this paper,we propose an intelligent service computing framework.In the framework,we take the long-term rewards of its important participants,edge service providers,as the optimization goal,which is related to service delay and computing cost.Considering the different update frequencies of data deployment and service offloading,double-timescale reinforcement learning is utilized in the framework.In the small-scale strategy,the frequent concurrency of services and the difference in service time lead to the fuzzy relationship between reward and action.To solve the fuzzy reward problem,a reward mapping-based reinforcement learning(RMRL)algorithm is proposed,which enables the agent to learn the relationship between reward and action more clearly.The large time scale strategy adopts the improved Monte Carlo tree search(MCTS)algorithm to improve the learning speed.The simulation results show that the strategy is superior to popular reinforcement learning algorithms such as double Q-learning(DDQN)and dueling Q-learning(dueling-DQN)in learning speed,and the reward is also increased by 14%.
基金supported in part by National Key Research and Development Program of China under Grant 2021YFB2900404.
文摘Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve its reliability.A data enhancement module(DEM)is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC.Multimodal network is designed to have multiple residual blocks,where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction.Moreover,a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM.Since different model may output different results,cooperative classifier is designed to avoid the randomness of single model and improve the reliability.Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.
基金supported in part by the National Key R&D Program of China(No.2021YFB3300100)the National Natural Science Foundation of China(No.62171062)。
文摘Effective control of time-sensitive industrial applications depends on the real-time transmission of data from underlying sensors.Quantifying the data freshness through age of information(AoI),in this paper,we jointly design sampling and non-slot based scheduling policies to minimize the maximum time-average age of information(MAoI)among sensors with the constraints of average energy cost and finite queue stability.To overcome the intractability involving high couplings of such a complex stochastic process,we first focus on the single-sensor time-average AoI optimization problem and convert the constrained Markov decision process(CMDP)into an unconstrained Markov decision process(MDP)by the Lagrangian method.With the infinite-time average energy and AoI expression expended as the Bellman equation,the singlesensor time-average AoI optimization problem can be approached through the steady-state distribution probability.Further,we propose a low-complexity sub-optimal sampling and semi-distributed scheduling scheme for the multi-sensor scenario.The simulation results show that the proposed scheme reduces the MAoI significantly while achieving a balance between the sampling rate and service rate for multiple sensors.
基金supported by the Natural Science Foundation of China (No.62171051)。
文摘Puncturing has been recognized as a promising technology to cope with the coexistence problem of enhanced mobile broadband(eMBB) and ultra-reliable low latency communications(URLLC)traffic. However, the steady performance of eMBB traffic while meeting the requirements of URLLC traffic with puncturing is a major challenge in some realistic scenarios. In this paper, we pay attention to the timely and energy-efficient processing for eMBB traffic in the industrial Internet of Things(IIoT), where mobile edge computing(MEC) is employed for data processing. Specifically, the performance of eMBB traffic and URLLC traffic in a MEC-based IIoT system is ensured by setting the threshold of tolerable delay and outage probability, respectively. Furthermore,considering the limited energy supply, an energy minimization problem of eMBB device is formulated under the above constraints, by jointly optimizing the resource blocks(RBs) punctured by URLLC traffic, data offloading and transmit power of eMBB device. With Markov's inequality, the problem is reformulated by transforming the probabilistic outage constraint into a deterministic constraint. Meanwhile, an iterative energy minimization algorithm(IEMA) is proposed.Simulation results demonstrate that our algorithm has a significant reduction in the energy consumption for eMBB device and achieves a better overall effect compared to several benchmarks.
