针对无线和电力线通信混合组网的信道竞争接入问题,提出了一种基于深度强化学习的电力线与无线双模通信的MAC接入算法。双模节点根据网络广播信息和信道使用等数据自适应接入双媒质信道。首先建立了基于双模通信网络交互和统计信息的双...针对无线和电力线通信混合组网的信道竞争接入问题,提出了一种基于深度强化学习的电力线与无线双模通信的MAC接入算法。双模节点根据网络广播信息和信道使用等数据自适应接入双媒质信道。首先建立了基于双模通信网络交互和统计信息的双模通信节点数据采集模型;接着定义了基于协作信息的深度强化学习(deep reinforcement learning,DRL)状态空间、动作空间和奖励,设计了联合α-公平效用函数和P坚持接入机制的节点决策流程,实现基于双深度Q网络(double deep Q-network,DDQN)的双模节点自适应接入算法;最后进行算法性能仿真和对比分析。仿真结果表明,提出的接入算法能够在保证双模网络和信道接入公平性的条件下,有效提高双模通信节点的接入性能。展开更多
Mobile ad-hoc networks are wireless self-organized networks in which mobile nodes can connect directly to each other. This fact makes such networks highly susceptible to security risks and threats, as malicious nodes ...Mobile ad-hoc networks are wireless self-organized networks in which mobile nodes can connect directly to each other. This fact makes such networks highly susceptible to security risks and threats, as malicious nodes can easily disguise as new trusted nodes and start attacking the network after a certain period of time. Hence, the security of data transmission in MANET has been a hot topic in the past years. Several research works attempted to detect and stop various attacks on MANET nodes and packets. This paper presents an efficient mechanism for secure data dissemination in MANETs. Our approach is based on the identity based cryptography and Message Authentication Code (MAC). The proposed security mechanism prevents malicious nodes from tampering or replaying intermediate packets by means of signing and encrypting the packet at each intermediate trusted node. We tested the efficiency of our system using the ns2 simulator by comparing it to a similar security mechanism. The simulations illustrate that our approach obtains many advantages over other existing approaches for secure data dissemination in MANETs.展开更多
针对PTP(precise time protocol)协议在应用层获取软件时间戳导致时钟同步精度下降的问题,提出一种基于MAC(media access control)层获取硬件时间戳的PTP同步优化方案。设计了以STM32F407微处理器为核心的PTP时钟应用平台,在MAC层实现...针对PTP(precise time protocol)协议在应用层获取软件时间戳导致时钟同步精度下降的问题,提出一种基于MAC(media access control)层获取硬件时间戳的PTP同步优化方案。设计了以STM32F407微处理器为核心的PTP时钟应用平台,在MAC层实现了硬件时间戳获取,避免了由于协议栈软件处理延时产生的不确定性;针对PTP时钟晶振老化导致的时间同步偏差及网络延迟抖动问题,采用迭代方法优化了本地时钟频率调节算法,提高了频率校正精度。经实际测试,主从时钟偏差的RMS(root mean square)优于20 ns,提升了时钟同步精度。展开更多
低功耗广域网(Low Power Wide Area Network,LPWAN)技术的出现,能够在保证更远距离的通信传输的同时,最大限度地降低功耗,节约传输成本。LoRa(Long Range)技术作为其中的佼佼者,凭借其远距离、低功耗、大容量、强抗干扰、高接收灵敏度...低功耗广域网(Low Power Wide Area Network,LPWAN)技术的出现,能够在保证更远距离的通信传输的同时,最大限度地降低功耗,节约传输成本。LoRa(Long Range)技术作为其中的佼佼者,凭借其远距离、低功耗、大容量、强抗干扰、高接收灵敏度的特点,备受工业界和学术界的青睐。针对目前工业中主流使用的基于ALOHA的LoRaWAN协议无法很好地解决海量终端设备接入LoRa网络后所带来的严重数据包冲突以及LoRa CAD(Channel Activity Detection)功能带来的隐藏终端问题,提出了一种基于BTMA(Busy Tone Multiple Access)的LoRa网络MAC协议——BT-MAC协议。该协议利用了LoRa高接收灵敏度的特性,网关利用“忙音”信标来通知各个节点网关的工作情况,减少了无效包的发送。同时,节点端通过记录有“忙音”信息和本地信息的逻辑信道矩阵,结合最优信道选择算法,选出最优逻辑信道进行发送,降低了端节点上行数据包之间的冲突,有效缓解了LoRa网络中的隐藏终端问题以及阻塞问题。此外,搭建了LoRa网络MAC协议测试平台,并测试了BT-MAC的有效性,完成了室内和室外环境大规模的并发实验和能耗检测实验。实验结果表明,BT-MAC协议的吞吐量是LMAC-2协议的1.6倍,是ALOHA协议的5.1倍;同时其包接收率达到LMAC-2协议的1.53倍,ALOHA协议的17.2倍;其包接收平均能耗约为LMAC-2协议的64.1%,为ALOHA协议的14.2%。展开更多
Health information spreads rapidly,which can effectively control epidemics.However,the swift dissemination of information also has potential negative impacts,which increasingly attracts attention.Message fatigue refer...Health information spreads rapidly,which can effectively control epidemics.However,the swift dissemination of information also has potential negative impacts,which increasingly attracts attention.Message fatigue refers to the psychological response characterized by feelings of boredom and anxiety that occur after receiving an excessive amount of similar information.This phenomenon can alter individual behaviors related to epidemic prevention.Additionally,recent studies indicate that pairwise interactions alone are insufficient to describe complex social transmission processes,and higher-order structures representing group interactions are crucial.To address this,we develop a novel epidemic model that investigates the interactions between information,behavioral responses,and epidemics.Our model incorporates the impact of message fatigue on the entire transmission system.The information layer is modeled using a static simplicial network to capture group interactions,while the disease layer uses a time-varying network based on activity-driven model with attractiveness to represent the self-protection behaviors of susceptible individuals and self-isolation behaviors of infected individuals.We theoretically describe the co-evolution equations using the microscopic Markov chain approach(MMCA)and get the epidemic threshold.Experimental results show that while the negative impact of message fatigue on epidemic transmission is limited,it significantly weakens the group interactions depicted by higher-order structures.Individual behavioral responses strongly inhibit the epidemic.Our simulations using the Monte Carlo(MC)method demonstrate that greater intensity in these responses leads to clustering of susceptible individuals in the disease layer.Finally,we apply the proposed model to real networks to verify its reliability.In summary,our research results enhance the understanding of the information-epidemic coupling dynamics,and we expect to provide valuable guidance for managing future emerging epidemics.展开更多
If 2024 has taught me anything,it’s that digital is an irrefutable force for unity—a much-needed catalyst for global cooperation in an increasingly fragmented world.This truth has been on display all year long,somet...If 2024 has taught me anything,it’s that digital is an irrefutable force for unity—a much-needed catalyst for global cooperation in an increasingly fragmented world.This truth has been on display all year long,sometimes against the odds.And it’s evident in the adoption of the Pact for the Future and Global Digital Compact at the United Nations General Assembly,in the outcomes of the World Telecommunication Standardization Assembly(WTSA-24),and in the wide endorsement of the COP29 Declaration on Green Digital Action.展开更多
The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained...The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained devices. Their energy consumption is typically correlated with the amount of data collection. The purpose of data aggregation is to reduce data transmission, lower energy consumption, and reduce network congestion. For large-scale WSN, data aggregation can greatly improve network efficiency. However, as many heterogeneous data is poured into a specific area at the same time, it sometimes causes data loss and then results in incompleteness and irregularity of production data. This paper proposes an information processing model that encompasses the Energy-Conserving Data Aggregation Algorithm (ECDA) and the Efficient Message Reception Algorithm (EMRA). ECDA is divided into two stages, Energy conservation based on the global cost and Data aggregation based on ant colony optimization. The EMRA comprises the Polling Message Reception Algorithm (PMRA), the Shortest Time Message Reception Algorithm (STMRA), and the Specific Condition Message Reception Algorithm (SCMRA). These algorithms are not only available for the regularity and directionality of sensor information transmission, but also satisfy the different requirements in small factory environments. To compare with the recent HPSO-ILEACH and E-PEGASIS, DCDA can effectively reduce energy consumption. Experimental results show that STMRA consumes 1.3 times the time of SCMRA. Both optimization algorithms exhibit higher time efficiency than PMRA. Furthermore, this paper also evaluates these three algorithms using AHP.展开更多
The China-ASEAN Expo(CAEXPO),held annually in Nanning City of Guangxi Zhuang Autonomous Region since 2004,has become a pivotal platform for economic and trade exchange between China,Vietnam,and other ASEAN member stat...The China-ASEAN Expo(CAEXPO),held annually in Nanning City of Guangxi Zhuang Autonomous Region since 2004,has become a pivotal platform for economic and trade exchange between China,Vietnam,and other ASEAN member states.Over the years,CAEXPO has proven to be a highly effective mechanism for fostering international cooperation,playing a vital role in establishing ASEAN as China’s largest trading partner and positioning China as the foremost trade partner of many ASEAN countries,including Vietnam.展开更多
In this paper,we focus on the channel estimation for multi-user MIMO-OFDM systems in rich scattering environments.We find that channel sparsity in the delay-angle domain is severely compromised in rich scattering envi...In this paper,we focus on the channel estimation for multi-user MIMO-OFDM systems in rich scattering environments.We find that channel sparsity in the delay-angle domain is severely compromised in rich scattering environments,so that most existing compressed sensing(CS)based techniques can harvest a very limited gain(if any)in reducing the channel estimation overhead.To address the problem,we propose the learning-based turbo message passing(LTMP)algorithm.Instead of exploiting the channel sparsity,LTMP is able to efficiently extract the channel feature via deep learning as well as to exploit the channel continuity in the frequency domain via block-wise linear modelling.More specifically,as a component of LTMP,we develop a multi-scale parallel dilated convolutional neural network(MPDCNN),which leverages frequency-space channel correlation in different scales for channel denoising.We evaluate the LTMP’s performance in MIMO-OFDM channels using the 3rd generation partnership project(3GPP)clustered delay line(CDL)channel models.Simulation results show that the proposed channel estimation method has more than 5 dB power gain than the existing algorithms when the normalized mean-square error of the channel estimation is-20 dB.The proposed algorithm also exhibits strong robustness in various environments.展开更多
文摘针对无线和电力线通信混合组网的信道竞争接入问题,提出了一种基于深度强化学习的电力线与无线双模通信的MAC接入算法。双模节点根据网络广播信息和信道使用等数据自适应接入双媒质信道。首先建立了基于双模通信网络交互和统计信息的双模通信节点数据采集模型;接着定义了基于协作信息的深度强化学习(deep reinforcement learning,DRL)状态空间、动作空间和奖励,设计了联合α-公平效用函数和P坚持接入机制的节点决策流程,实现基于双深度Q网络(double deep Q-network,DDQN)的双模节点自适应接入算法;最后进行算法性能仿真和对比分析。仿真结果表明,提出的接入算法能够在保证双模网络和信道接入公平性的条件下,有效提高双模通信节点的接入性能。
文摘Mobile ad-hoc networks are wireless self-organized networks in which mobile nodes can connect directly to each other. This fact makes such networks highly susceptible to security risks and threats, as malicious nodes can easily disguise as new trusted nodes and start attacking the network after a certain period of time. Hence, the security of data transmission in MANET has been a hot topic in the past years. Several research works attempted to detect and stop various attacks on MANET nodes and packets. This paper presents an efficient mechanism for secure data dissemination in MANETs. Our approach is based on the identity based cryptography and Message Authentication Code (MAC). The proposed security mechanism prevents malicious nodes from tampering or replaying intermediate packets by means of signing and encrypting the packet at each intermediate trusted node. We tested the efficiency of our system using the ns2 simulator by comparing it to a similar security mechanism. The simulations illustrate that our approach obtains many advantages over other existing approaches for secure data dissemination in MANETs.
文摘针对PTP(precise time protocol)协议在应用层获取软件时间戳导致时钟同步精度下降的问题,提出一种基于MAC(media access control)层获取硬件时间戳的PTP同步优化方案。设计了以STM32F407微处理器为核心的PTP时钟应用平台,在MAC层实现了硬件时间戳获取,避免了由于协议栈软件处理延时产生的不确定性;针对PTP时钟晶振老化导致的时间同步偏差及网络延迟抖动问题,采用迭代方法优化了本地时钟频率调节算法,提高了频率校正精度。经实际测试,主从时钟偏差的RMS(root mean square)优于20 ns,提升了时钟同步精度。
文摘低功耗广域网(Low Power Wide Area Network,LPWAN)技术的出现,能够在保证更远距离的通信传输的同时,最大限度地降低功耗,节约传输成本。LoRa(Long Range)技术作为其中的佼佼者,凭借其远距离、低功耗、大容量、强抗干扰、高接收灵敏度的特点,备受工业界和学术界的青睐。针对目前工业中主流使用的基于ALOHA的LoRaWAN协议无法很好地解决海量终端设备接入LoRa网络后所带来的严重数据包冲突以及LoRa CAD(Channel Activity Detection)功能带来的隐藏终端问题,提出了一种基于BTMA(Busy Tone Multiple Access)的LoRa网络MAC协议——BT-MAC协议。该协议利用了LoRa高接收灵敏度的特性,网关利用“忙音”信标来通知各个节点网关的工作情况,减少了无效包的发送。同时,节点端通过记录有“忙音”信息和本地信息的逻辑信道矩阵,结合最优信道选择算法,选出最优逻辑信道进行发送,降低了端节点上行数据包之间的冲突,有效缓解了LoRa网络中的隐藏终端问题以及阻塞问题。此外,搭建了LoRa网络MAC协议测试平台,并测试了BT-MAC的有效性,完成了室内和室外环境大规模的并发实验和能耗检测实验。实验结果表明,BT-MAC协议的吞吐量是LMAC-2协议的1.6倍,是ALOHA协议的5.1倍;同时其包接收率达到LMAC-2协议的1.53倍,ALOHA协议的17.2倍;其包接收平均能耗约为LMAC-2协议的64.1%,为ALOHA协议的14.2%。
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72171136 and 72134004)Humanities and Social Science Research Project,Ministry of Education of China(Grant No.21YJC630157)+1 种基金the Natural Science Foundation of Shandong Province(Grant No.ZR2022MG008)Shandong Provincial Colleges and Universities Youth Innovation Technology of China(Grant No.2022RW066)。
文摘Health information spreads rapidly,which can effectively control epidemics.However,the swift dissemination of information also has potential negative impacts,which increasingly attracts attention.Message fatigue refers to the psychological response characterized by feelings of boredom and anxiety that occur after receiving an excessive amount of similar information.This phenomenon can alter individual behaviors related to epidemic prevention.Additionally,recent studies indicate that pairwise interactions alone are insufficient to describe complex social transmission processes,and higher-order structures representing group interactions are crucial.To address this,we develop a novel epidemic model that investigates the interactions between information,behavioral responses,and epidemics.Our model incorporates the impact of message fatigue on the entire transmission system.The information layer is modeled using a static simplicial network to capture group interactions,while the disease layer uses a time-varying network based on activity-driven model with attractiveness to represent the self-protection behaviors of susceptible individuals and self-isolation behaviors of infected individuals.We theoretically describe the co-evolution equations using the microscopic Markov chain approach(MMCA)and get the epidemic threshold.Experimental results show that while the negative impact of message fatigue on epidemic transmission is limited,it significantly weakens the group interactions depicted by higher-order structures.Individual behavioral responses strongly inhibit the epidemic.Our simulations using the Monte Carlo(MC)method demonstrate that greater intensity in these responses leads to clustering of susceptible individuals in the disease layer.Finally,we apply the proposed model to real networks to verify its reliability.In summary,our research results enhance the understanding of the information-epidemic coupling dynamics,and we expect to provide valuable guidance for managing future emerging epidemics.
文摘If 2024 has taught me anything,it’s that digital is an irrefutable force for unity—a much-needed catalyst for global cooperation in an increasingly fragmented world.This truth has been on display all year long,sometimes against the odds.And it’s evident in the adoption of the Pact for the Future and Global Digital Compact at the United Nations General Assembly,in the outcomes of the World Telecommunication Standardization Assembly(WTSA-24),and in the wide endorsement of the COP29 Declaration on Green Digital Action.
基金Funds for High-Level Talents Programof Xi’an International University(Grant No.XAIU202411).
文摘The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained devices. Their energy consumption is typically correlated with the amount of data collection. The purpose of data aggregation is to reduce data transmission, lower energy consumption, and reduce network congestion. For large-scale WSN, data aggregation can greatly improve network efficiency. However, as many heterogeneous data is poured into a specific area at the same time, it sometimes causes data loss and then results in incompleteness and irregularity of production data. This paper proposes an information processing model that encompasses the Energy-Conserving Data Aggregation Algorithm (ECDA) and the Efficient Message Reception Algorithm (EMRA). ECDA is divided into two stages, Energy conservation based on the global cost and Data aggregation based on ant colony optimization. The EMRA comprises the Polling Message Reception Algorithm (PMRA), the Shortest Time Message Reception Algorithm (STMRA), and the Specific Condition Message Reception Algorithm (SCMRA). These algorithms are not only available for the regularity and directionality of sensor information transmission, but also satisfy the different requirements in small factory environments. To compare with the recent HPSO-ILEACH and E-PEGASIS, DCDA can effectively reduce energy consumption. Experimental results show that STMRA consumes 1.3 times the time of SCMRA. Both optimization algorithms exhibit higher time efficiency than PMRA. Furthermore, this paper also evaluates these three algorithms using AHP.
文摘The China-ASEAN Expo(CAEXPO),held annually in Nanning City of Guangxi Zhuang Autonomous Region since 2004,has become a pivotal platform for economic and trade exchange between China,Vietnam,and other ASEAN member states.Over the years,CAEXPO has proven to be a highly effective mechanism for fostering international cooperation,playing a vital role in establishing ASEAN as China’s largest trading partner and positioning China as the foremost trade partner of many ASEAN countries,including Vietnam.
基金supported in part by the National Key Research and Development Program of China under Grant 2020YFB1804800.
文摘In this paper,we focus on the channel estimation for multi-user MIMO-OFDM systems in rich scattering environments.We find that channel sparsity in the delay-angle domain is severely compromised in rich scattering environments,so that most existing compressed sensing(CS)based techniques can harvest a very limited gain(if any)in reducing the channel estimation overhead.To address the problem,we propose the learning-based turbo message passing(LTMP)algorithm.Instead of exploiting the channel sparsity,LTMP is able to efficiently extract the channel feature via deep learning as well as to exploit the channel continuity in the frequency domain via block-wise linear modelling.More specifically,as a component of LTMP,we develop a multi-scale parallel dilated convolutional neural network(MPDCNN),which leverages frequency-space channel correlation in different scales for channel denoising.We evaluate the LTMP’s performance in MIMO-OFDM channels using the 3rd generation partnership project(3GPP)clustered delay line(CDL)channel models.Simulation results show that the proposed channel estimation method has more than 5 dB power gain than the existing algorithms when the normalized mean-square error of the channel estimation is-20 dB.The proposed algorithm also exhibits strong robustness in various environments.
