In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set f...In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.展开更多
This paper presents a distributed optimization strategy for large-scale traffic network based on fog computing. Different from the traditional cloud-based centralized optimization strategy, the fog-based distributed o...This paper presents a distributed optimization strategy for large-scale traffic network based on fog computing. Different from the traditional cloud-based centralized optimization strategy, the fog-based distributed optimization strategy distributes its computing tasks to individual sub-processors, thus significantly reducing computation time. A traffic model is built and a series of communication rules between subsystems are set to ensure that the entire transportation network can be globally optimized while the subsystem is achieving its local optimization. Finally, this paper numerically simulates the operation of the traffic network by mixed-Integer programming, also, compares the advantages and disadvantages of the two optimization strategies.展开更多
For the Asynchronous Transfer Mode （ATM） networks with time-varying multiple time-delays, a more realistic model for the available bit rate （ABR） traffic class with explicit rate feedback is introduced. A fuzzy-im...For the Asynchronous Transfer Mode （ATM） networks with time-varying multiple time-delays, a more realistic model for the available bit rate （ABR） traffic class with explicit rate feedback is introduced. A fuzzy-immune controller is designed, which can adjust the rates of ABR on-line, overcome the bad effect caused by the saturation nonlinearity and satisfy the weighted fairness. Also, the sufficient condition that guarantees the stability of the closed-loop system with a fuzzy-immune controller is presented in theory for the first time. The algorithm exhibits good performance, and most importantly, has a solid theoretical foundation and can be implemented in practice easily. Simulation results show that the control system is rapid, adaptive, robust, and meanwhile, the quality of service （QoS） is guaranteed.展开更多
Automated Routing Control System supersedes the prior approach to LAN redundancy which provides two or more LANs and each has a network (LAN) controller coupled to data communication devices. Devices require some soft...Automated Routing Control System supersedes the prior approach to LAN redundancy which provides two or more LANs and each has a network (LAN) controller coupled to data communication devices. Devices require some software to switch between the network (LAN) controllers to counter some network segment failures. This approach is proven to be very costly due to demands for “off-the-shelf” data communication devices with built-in LAN controller drivers [1]. Automated Routing Control System, is the ultimate solution to the growing demands for inexpensive (less costly) and less difficult approach which requires less modification rather than upgrade of network devices yet it minimizes data and information exchange losses and interruptions caused by connection failures within the network and lessens the task of managing a complex network having many segments rather than subnets under a centralized monitoring and management. Automated Routing Control System rather than mechanism is a realistic approach applied to meet the ever growing demands for reliability, high efficiency and availability within data and information exchange networks.展开更多
A bursty traffic model is introduced in this paper to describe the statistical characteristics of packet video. The performance of leady bucket algorithm with bursty traffic input is analyzed. The influences of variou...A bursty traffic model is introduced in this paper to describe the statistical characteristics of packet video. The performance of leady bucket algorithm with bursty traffic input is analyzed. The influences of various parameters on QOS (Quality of Service) are investigated. The analysis shows that although the loss probability decreases through expanding the buffer capacity, the delay and delay jitter increase, whose effect on QOS will not be negligible.展开更多
Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion with...Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.展开更多
More subtle and explicit QoS control mechanisms are required at the radio access level, even though the simple and scalable Differentiated Services (DiffServ) QoS control model is acceptable for the core of the networ...More subtle and explicit QoS control mechanisms are required at the radio access level, even though the simple and scalable Differentiated Services (DiffServ) QoS control model is acceptable for the core of the network. At the radio access level, available resources are severely limited and the degree of traffic aggregation is not significant, thus rendering the DiffServ principles less effective. In this paper we present a suitable hybrid QoS architecture framework to address the problem. At the wireless access end, the local QoS mechanism is designed in the context of IEEE 802.11 WLAN with 802.11e QoS extensions;so streams of those session-based applications are admitted, established according to the traffic profile they require, and guaranteed. As the core in the Admission Control of the hybrid QoS architecture, the Fair Intelligent Congestion Control (FICC) algorithm is applied to provide fairness among traffic aggregates and control congestion at the bottleneck interface between the wireless link and the network core via mechanisms of packet scheduling, buffer management, feedback and adjustments. It manages effectively the overloading scenario by preventing traffic violation from uncontrolled traffic, and providing guarantee to the priority traffic in terms of guaranteed bandwidth allocation and specified delay.展开更多
This paper presents an add-on Class of Service (CoS) layer for wireless mesh networks. The proposed protocol is applicable for contention-based MACs and is therefore compatible with most of the Wireless Local Area Net...This paper presents an add-on Class of Service (CoS) layer for wireless mesh networks. The proposed protocol is applicable for contention-based MACs and is therefore compatible with most of the Wireless Local Area Network (WLAN) and Wireless Sensor Network (WSN) protocols. The protocol has a locally centralized control for managing data flows, which either reserve a fixed bandwidth or are weighted by fair scheduling. The protocol reduces transmission collisions, thus improving the overall throughput. IEEE 802.11 adhoc WLAN has been taken as a platform for simulations and prototyping for evaluating the protocol performance. Network Simulator Version 2 (NS2) simulations show that the CoS protocol efficiently differentiates bandwidth, supports bandwidth reservations, and reaches less than 10 ms transfer delay on IEEE 802.11b WLAN. Testing with a full prototype implementation verified the performance of the protocol.展开更多
In wireless network, call completion probability accounts for users' satisfaction since the admitted ongoing call may be interrupted during hand-off process or even stay in the same cell when dynamically allocatin...In wireless network, call completion probability accounts for users' satisfaction since the admitted ongoing call may be interrupted during hand-off process or even stay in the same cell when dynamically allocating resource to calls because of the loss of resource. We focus on the relationship between call's completion probability and these interruptions and develop an analytical relationship model for homogeneous cellular networks based on probability analysis. Then assuming call's data source is modeled by on-off traffic model, a two dimensional Markov process is established to compute these blocking and dropping probabilities for call's completion probability. The impacts of different new call arrival rate, call's traffic characteristic, user's mobility, call's holding time and call's admission threshold on call's completion are evaluated and compared through numerical examples. These results show that call's completion reaches its maximum value if making no difference between hand-off call and new call in the case of light traffic load. But some resource should be reserved for the hand-off call in high traffic scenario. The analytical model provides a basis for helping to set the call admission threshold. Key words on-off traffic source - call completion probability - call admission control - wireless networks CLC number TN 919. 72 Foundation item: Supported by the National Natural Science Foundation of China (60172077)Biography: XUAN Xiao-ying (1972-), female, Ph. D candidate, research direction: radio resource management and scheduling in wireless networks.展开更多
近年交通拥堵已成为制约城市经济发展的重要问题,利用深度强化学习(Deep Reinforcement Learning,DRL)对交通信号灯进行自适应控制是缓解交通拥堵的研究热点。针对决斗双重深度Q网络(Dueling Double Deep Q-Network,D3QN)算法在交通信...近年交通拥堵已成为制约城市经济发展的重要问题,利用深度强化学习(Deep Reinforcement Learning,DRL)对交通信号灯进行自适应控制是缓解交通拥堵的研究热点。针对决斗双重深度Q网络(Dueling Double Deep Q-Network,D3QN)算法在交通信号控制中存在的样本利用率低、学习速度慢,以及路网状态信息复杂且灵活性差等问题,基于非均匀划分道路的离散交通状态编码(Discrete Traffic State Encode,DTSE)方法,提出一种D3PQN2交通信号控制算法。该算法在D3QN算法基础上引入噪声网络、优先级经验回放技术来提高样本的利用效率以及学习速度,通过噪声扰动代替传统的ε-贪婪策略,使得算法能够更快更好地收敛到全局最优解。以扬州市文昌路和扬子江路交叉口为例,在Weibull分布生成的车流下进行实验,结果表明,改进后的算法相较于对抗深度Q网络(Dueling Deep Q-Network,Dueling DQN)算法和固定配时的控制方法,车辆平均排队长度分别减少了12.11%和67.44%,累计延误时间分别减少了13.89%和42.88%,具有更好的控制效果。展开更多
基金National Natural Science Foundation of China(U2133208,U20A20161)National Natural Science Foundation of China(No.62273244)Sichuan Science and Technology Program(No.2022YFG0180).
文摘In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.
基金supported by the Natural Science Foundation of China under Grant 61873017 and Grant 61473016in part by the Beijing Natural Science Foundation under Grant Z180005supported in part by the National Research Foundation of South Africa under Grant 113340in part by the Oppenheimer Memorial Trust Grant
文摘This paper presents a distributed optimization strategy for large-scale traffic network based on fog computing. Different from the traditional cloud-based centralized optimization strategy, the fog-based distributed optimization strategy distributes its computing tasks to individual sub-processors, thus significantly reducing computation time. A traffic model is built and a series of communication rules between subsystems are set to ensure that the entire transportation network can be globally optimized while the subsystem is achieving its local optimization. Finally, this paper numerically simulates the operation of the traffic network by mixed-Integer programming, also, compares the advantages and disadvantages of the two optimization strategies.
基金the open subject for Key Laboratory of Process Industry Automation of Ministry of Education.
文摘For the Asynchronous Transfer Mode （ATM） networks with time-varying multiple time-delays, a more realistic model for the available bit rate （ABR） traffic class with explicit rate feedback is introduced. A fuzzy-immune controller is designed, which can adjust the rates of ABR on-line, overcome the bad effect caused by the saturation nonlinearity and satisfy the weighted fairness. Also, the sufficient condition that guarantees the stability of the closed-loop system with a fuzzy-immune controller is presented in theory for the first time. The algorithm exhibits good performance, and most importantly, has a solid theoretical foundation and can be implemented in practice easily. Simulation results show that the control system is rapid, adaptive, robust, and meanwhile, the quality of service （QoS） is guaranteed.
文摘Automated Routing Control System supersedes the prior approach to LAN redundancy which provides two or more LANs and each has a network (LAN) controller coupled to data communication devices. Devices require some software to switch between the network (LAN) controllers to counter some network segment failures. This approach is proven to be very costly due to demands for “off-the-shelf” data communication devices with built-in LAN controller drivers [1]. Automated Routing Control System, is the ultimate solution to the growing demands for inexpensive (less costly) and less difficult approach which requires less modification rather than upgrade of network devices yet it minimizes data and information exchange losses and interruptions caused by connection failures within the network and lessens the task of managing a complex network having many segments rather than subnets under a centralized monitoring and management. Automated Routing Control System rather than mechanism is a realistic approach applied to meet the ever growing demands for reliability, high efficiency and availability within data and information exchange networks.
基金Supported by Foundation of Electronic Science Institutethe National Natural Science Foundation of China
文摘A bursty traffic model is introduced in this paper to describe the statistical characteristics of packet video. The performance of leady bucket algorithm with bursty traffic input is analyzed. The influences of various parameters on QOS (Quality of Service) are investigated. The analysis shows that although the loss probability decreases through expanding the buffer capacity, the delay and delay jitter increase, whose effect on QOS will not be negligible.
文摘Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.
文摘More subtle and explicit QoS control mechanisms are required at the radio access level, even though the simple and scalable Differentiated Services (DiffServ) QoS control model is acceptable for the core of the network. At the radio access level, available resources are severely limited and the degree of traffic aggregation is not significant, thus rendering the DiffServ principles less effective. In this paper we present a suitable hybrid QoS architecture framework to address the problem. At the wireless access end, the local QoS mechanism is designed in the context of IEEE 802.11 WLAN with 802.11e QoS extensions;so streams of those session-based applications are admitted, established according to the traffic profile they require, and guaranteed. As the core in the Admission Control of the hybrid QoS architecture, the Fair Intelligent Congestion Control (FICC) algorithm is applied to provide fairness among traffic aggregates and control congestion at the bottleneck interface between the wireless link and the network core via mechanisms of packet scheduling, buffer management, feedback and adjustments. It manages effectively the overloading scenario by preventing traffic violation from uncontrolled traffic, and providing guarantee to the priority traffic in terms of guaranteed bandwidth allocation and specified delay.
文摘This paper presents an add-on Class of Service (CoS) layer for wireless mesh networks. The proposed protocol is applicable for contention-based MACs and is therefore compatible with most of the Wireless Local Area Network (WLAN) and Wireless Sensor Network (WSN) protocols. The protocol has a locally centralized control for managing data flows, which either reserve a fixed bandwidth or are weighted by fair scheduling. The protocol reduces transmission collisions, thus improving the overall throughput. IEEE 802.11 adhoc WLAN has been taken as a platform for simulations and prototyping for evaluating the protocol performance. Network Simulator Version 2 (NS2) simulations show that the CoS protocol efficiently differentiates bandwidth, supports bandwidth reservations, and reaches less than 10 ms transfer delay on IEEE 802.11b WLAN. Testing with a full prototype implementation verified the performance of the protocol.
文摘In wireless network, call completion probability accounts for users' satisfaction since the admitted ongoing call may be interrupted during hand-off process or even stay in the same cell when dynamically allocating resource to calls because of the loss of resource. We focus on the relationship between call's completion probability and these interruptions and develop an analytical relationship model for homogeneous cellular networks based on probability analysis. Then assuming call's data source is modeled by on-off traffic model, a two dimensional Markov process is established to compute these blocking and dropping probabilities for call's completion probability. The impacts of different new call arrival rate, call's traffic characteristic, user's mobility, call's holding time and call's admission threshold on call's completion are evaluated and compared through numerical examples. These results show that call's completion reaches its maximum value if making no difference between hand-off call and new call in the case of light traffic load. But some resource should be reserved for the hand-off call in high traffic scenario. The analytical model provides a basis for helping to set the call admission threshold. Key words on-off traffic source - call completion probability - call admission control - wireless networks CLC number TN 919. 72 Foundation item: Supported by the National Natural Science Foundation of China (60172077)Biography: XUAN Xiao-ying (1972-), female, Ph. D candidate, research direction: radio resource management and scheduling in wireless networks.
文摘近年交通拥堵已成为制约城市经济发展的重要问题,利用深度强化学习(Deep Reinforcement Learning,DRL)对交通信号灯进行自适应控制是缓解交通拥堵的研究热点。针对决斗双重深度Q网络(Dueling Double Deep Q-Network,D3QN)算法在交通信号控制中存在的样本利用率低、学习速度慢,以及路网状态信息复杂且灵活性差等问题,基于非均匀划分道路的离散交通状态编码(Discrete Traffic State Encode,DTSE)方法,提出一种D3PQN2交通信号控制算法。该算法在D3QN算法基础上引入噪声网络、优先级经验回放技术来提高样本的利用效率以及学习速度,通过噪声扰动代替传统的ε-贪婪策略,使得算法能够更快更好地收敛到全局最优解。以扬州市文昌路和扬子江路交叉口为例,在Weibull分布生成的车流下进行实验,结果表明,改进后的算法相较于对抗深度Q网络(Dueling Deep Q-Network,Dueling DQN)算法和固定配时的控制方法,车辆平均排队长度分别减少了12.11%和67.44%,累计延误时间分别减少了13.89%和42.88%,具有更好的控制效果。
