End-to-end Delay Analysis for Mixed-criticality WirelessHART Networks

WirelessHART,as a robust and reliable wireless protocol,has been widely-used in industrial wireless sensoractuator networks.Its real-time performance has been extensively studied,but limited to the single criticality case.Many advanced applications have mixed-criticality communications,where different data flows come with different levels of importance or criticality.Hence,in this paper,we study the real-time mixedcriticality communication using WirelessHART protocol,and propose an end-to-end delay analysis approach based on fixed priority scheduling.To the best of our knowledge,this is the first work that introduces the concept of mixed-criticality into wireless sensor-actuator networks.Evaluation results show the effectiveness and efficacy of our approach.

Explosive synchronization of multi-layer complex networks based on star connection between layers with delay

Explosive synchronization(ES)is a kind of first-order jump phenomenon that exists in physical and biological systems.In recent years,researchers have focused on ES between single-layer and multi-layer networks.Most research on complex networks with delay has focused on single-layer or double-layer networks,multi-layer networks are seldom explored.In this paper,we propose a Kuramoto model of frequency weights in multi-layer complex networks with delay and star connections between layers.Through theoretical analysis and numerical verification,the factors affecting the backward critical coupling strength are analyzed.The results show that the interaction between layers and the average node degree has a direct effect on the backward critical coupling strength of each layer network.The location of the delay,the size of the delay,the number of network layers,the number of nodes,and the network topology are revealed to have no direct impact on the backward critical coupling strength of the network.Delay is introduced to explore the influence of delay and other related parameters on ES.

Synchronization of stochastic complex networks with time-delayed coupling

Noise and time delay are inevitable in real-world networks. In this article, the framework of master stability function is generalized to stochastic complex networks with time-delayed coupling. The focus is on the effects of noise, time delay,and their inner interactions on the network synchronization. It is found that when there exists time-delayed coupling in the network and noise diffuses through all state variables of nodes, appropriately increasing the noise intensity can effectively improve the network synchronizability;otherwise, noise can be either beneficial or harmful. For stochastic networks, large time delays will lead to desynchronization. These findings provide valuable references for designing optimal complex networks in practical applications.

Cascading Delays for the High-Speed Rail Network Under Different Emergencies:A Double Layer Network Approach

High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.

Attention-based neural network for end-to-end music separation

The end-to-end separation algorithm with superior performance in the field of speech separation has not been effectively used in music separation.Moreover,since music signals are often dual channel data with a high sampling rate,how to model longsequence data and make rational use of the relevant information between channels is also an urgent problem to be solved.In order to solve the above problems,the performance of the end-to-end music separation algorithm is enhanced by improving the network structure.Our main contributions include the following:(1)A more reasonable densely connected U-Net is designed to capture the long-term characteristics of music,such as main melody,tone and so on.(2)On this basis,the multi-head attention and dualpath transformer are introduced in the separation module.Channel attention units are applied recursively on the feature map of each layer of the network,enabling the network to perform long-sequence separation.Experimental results show that after the introduction of the channel attention,the performance of the proposed algorithm has a stable improvement compared with the baseline system.On the MUSDB18 dataset,the average score of the separated audio exceeds that of the current best-performing music separation algorithm based on the time-frequency domain(T-F domain).

Adaptive Fixed-Time Synchronization of Delayed Memristor-Based Neural Networks with Discontinuous Activations

Fixed-time synchronization(FTS)of delayed memristor-based neural networks(MNNs)with discontinuous activations is studied in this paper.Both continuous and discontinuous activations are considered forMNNs.And the mixed delays which are closer to reality are taken into the system.Besides,two kinds of control schemes are proposed,including feedback and adaptive control strategies.Based on some lemmas,mathematical inequalities and the designed controllers,a few synchronization criteria are acquired.Moreover,the upper bound of settling time(ST)which is independent of the initial values is given.Finally,the feasibility of our theory is attested by simulation examples.

Efficient Energy and Delay Reduction Model for Wireless Sensor Networks

In every network,delay and energy are crucial for communication and network life.In wireless sensor networks,many tiny nodes create networks with high energy consumption and compute routes for better communication.Wireless Sensor Networks(WSN)is a very complex scenario to compute minimal delay with data aggregation and energy efficiency.In this research,we compute minimal delay and energy efficiency for improving the quality of service of any WSN.The proposed work is based on energy and distance parameters as taken dependent variables with data aggregation.Data aggregation performs on different models,namely Hybrid-Low Energy Adaptive Clustering Hierarchy(H-LEACH),Low Energy Adaptive Clustering Hierarchy(LEACH),and Multi-Aggregator-based Multi-Cast(MAMC).The main contribution of this research is to a reduction in delay and optimized energy solution,a novel hybrid model design in this research that ensures the quality of service in WSN.This model includes a whale optimization technique that involves heterogeneous functions and performs optimization to reach optimized results.For cluster head selection,Stable Election Protocol(SEP)protocol is used and Power-Efficient Gathering in Sensor Information Systems(PEGASIS)is used for driven-path in routing.Simulation results evaluate that H-LEACH provides minimal delay and energy consumption by sensor nodes.In the comparison of existing theories and our proposed method,HLEACH is providing energy and delay reduction and improvement in quality of service.MATLAB 2019 is used for simulation work.

Energy Based Random Repeat Trust Computation in Delay Tolerant Network

As the use of mobile devices continues to rise,trust administration will significantly improve security in routing the guaranteed quality of service(QoS)supply in Mobile Ad Hoc Networks(MANET)due to the mobility of the nodes.There is no continuance of network communication between nodes in a delay-tolerant network(DTN).DTN is designed to complete recurring connections between nodes.This approach proposes a dynamic source routing protocol(DSR)based on a feed-forward neural network(FFNN)and energybased random repetition trust calculation in DTN.If another node is looking for a node that swerved off of its path in this situation,routing will fail since it won’t recognize it.However,in the suggested strategy,nodes do not stray from their pathways for routing.It is only likely that the message will reach the destination node if the nodes encounter their destination or an appropriate transitional node on their default mobility route,based on their pattern of mobility.The EBRRTC-DTN algorithm(Energy based random repeat trust computation)is based on the time that has passed since nodes last encountered the destination node.Compared to other existing techniques,simulation results show that this process makes the best decision and expertly determines the best and most appropriate route to send messages to the destination node,which improves routing performance,increases the number of delivered messages,and decreases delivery delay.Therefore,the suggested method is better at providing better QoS(Quality of Service)and increasing network lifetime,tolerating network system latency.

End-to-End Auto-Encoder System for Deep Residual Shrinkage Network for AWGN Channels

With the rapid development of deep learning methods, the data-driven approach has shown powerful advantages over the model-driven one. In this paper, we propose an end-to-end autoencoder communication system based on Deep Residual Shrinkage Networks (DRSNs), where neural networks (DNNs) are used to implement the coding, decoding, modulation and demodulation functions of the communication system. Our proposed autoencoder communication system can better reduce the signal noise by adding an “attention mechanism” and “soft thresholding” modules and has better performance at various signal-to-noise ratios (SNR). Also, we have shown through comparative experiments that the system can operate at moderate block lengths and support different throughputs. It has been shown to work efficiently in the AWGN channel. Simulation results show that our model has a higher Bit-Error-Rate (BER) gain and greatly improved decoding performance compared to conventional modulation and classical autoencoder systems at various signal-to-noise ratios.

Adaptive Stochastic Synchronization of Uncertain Delayed Neural Networks

This paper considers adaptive synchronization of uncertain neural networks with time delays and stochastic perturbation. A general adaptive controller is designed to deal with the difficulties deduced by uncertain parameters and stochastic perturbations, in which the controller is less conservative and optimal since its control gains can be automatically adjusted according to some designed update laws. Based on Lyapunov stability theory and Barbalat lemma, sufficient condition is obtained for synchronization of delayed neural networks by strict mathematical proof. Moreover, the obtained results of this paper are more general than most existing results of certainly neural networks with or without stochastic disturbances. Finally, numerical simulations are presented to substantiate our theoretical results.

Adaptive output feedback control for nonlinear time-delay systems using neural network

This paper extends the adaptive neural network （NN） control approaches to a class of unknown output feedback nonlinear time-delay systems. An adaptive output feedback NN tracking controller is designed by backstepping technique. NNs are used to approximate unknown functions dependent on time delay, Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the NN approximation errors. Based on Lyapunov- Krasovskii functional, the semi-global uniform ultimate boundedness of all the signals in the closed-loop system is proved, The feasibility is investigated by two illustrative simulation examples.

Internet end-to-end delay dynamics

End-to-end delay is one of the most important characteristics of Internet end-to-end packet dynamics, which can be applied to quality of services （OoS） management, service level agreement （SLA） management, congestion control algorithm development, etc. Nonstationarity and nonlinearity are found by the analysis of various delay series measured from different links. The fact that different types of links have different degree of Self-Similarity is also obtained. By constructing appropriate network architecture and neural functions, functional networks can be used to model the Internet end-to-end nonlinear delay time series. Furthermore, by using adaptive parameter studying algorithm, the nonstationarity can also be well modeled. The numerical results show that the provided functional network architecture and adaptive algorithm can precisely characterize the Internet end-to-end delay dynamics.

Real-time multi-step prediction control for BP network with delay

Real time multi step prediction of BP network based on dynamical compensation of system characteristics is suggested by introducing the first and second derivatives of the system and network outputs into the network input layer, and real time multi step prediction control is proposed for the BP network with delay on the basis of the results of real time multi step prediction, to achieve the simulation of real time fuzzy control of the delayed time system.

Analysis of PROFIBUS-DP Network Delay and Its Influence on the Performance of Control Systems

This paper analyzes PROFIBUS-DP network delay in detail and presents the calculational formula of its maximum time-delay, which is significant to the research of PROFIBUS-DP. At the same time, the paper puts forward a method of simplifying the network induced time-varying indeterminate system according to the features of the network. Through the analysis of a pump-control-motor system which is composed of PROFIBUS-DP network, it illustrates the network＇s influence on the performance of control systems. This method helps to design and analyze the network＇s influence on the performance of control systems, which is of considerable practical value in a time when network control systern is widely used.

Decoupled Delay and Bandwidth Centralized Queue-Based QoS Scheme in OpenFlow Networks

There are an increasing of scenarios that require the independent bandwidth and delay demands. For instance, in a data center, the interactive message would not occupy much bandwidth, but it requires the rigorous demands for the delay. However, the existing QoS approaches are mainly bandwidth based, which are inappropriate for these scenarios. Hence, we propose the decoupled scheme in the OpenFlow networks to provide the centralized differential bandwidth and delay control. We leverage the mature HTB to manage the bandwidth. And we design the Queue Delay Management Scheme (QDMS) for queuing delay arrangement, as well as the Comprehensive Parameters based Dijkstra Route algorithm (CPDR) for the propagation delay control. The evaluation results verify the decoupling effectiveness. And the decoupled scheme can reduce the delay for high priority flows.

A Spatial-Temporal Network Perspective for the Propagation Dynamics of Air Traffic Delays

Intractable delays occur in air traffic due to the imbalance between ever-increasing air traffic demand and limited airspace capacity.As air traffic is associated with complex air transport systems,delays can be magnified and propagated throughout these systems,resulting in the emergent behavior known as delay propagation.An understanding of delay propagation dynamics is pertinent to modern air traffic management.In this work,we present a complex network perspective of delay propagation dynamics.Specifically,we model air traffic scenarios using spatial–temporal networks with airports as the nodes.To establish the dynamic edges between the nodes,we develop a delay propagation method and apply it to a given set of air traffic schedules.Based on the constructed spatial-temporal networks,we suggest three metrics-magnitude,severity,and speed-to gauge delay propagation dynamics.To validate the effectiveness of the proposed method,we carry out case studies on domestic flights in the Southeastern Asia region(SAR)and the United States.Experiments demonstrate that the propagation magnitude in terms of the number of flights affected by delay propagation and the amount of propagated delays for the US traffic are respectively five and ten times those of the SAR.Experiments further reveal that the propagation speed for US traffic is eight times faster than that of the SAR.The delay propagation dynamics reveal that about six hub airports in the SAR have significant propagated delays,while the situation in the United States is considerably worse,with a corresponding number of around 16.This work provides a potent tool for tracing the evolution of air traffic delays.

LanePost:Lane-Based Optimal Routing Protocol for Delay-Tolerant Maritime Networks

Ship.to.ship, ship.to.shore radio links empowered by Wi Fi, Wi MAX etc have been recently exploited to build maritime multi.hop mesh networks to provide internet services to on.ship users. However, because of the mobility of the vessels/ships and the large inter.ship distances, nodes in the maritime network are frequently disconnected, forcing data communication in the maritime mesh networks to be opportunistic and delay.tolerant. In this paper, we present Lane Post, an optimization approach for maritime delay.tolerant routing protocol. We exploit the shipping lane information to predict the rendezvous opportunities of the ships to optimize the route selection in delay.tolerant routing. In particular, we show that when the shipping lane information is available, an opportunistic routing graph(ORG) for each ship can be constructed to predict its multi.hop data routing opportunities to the other ships or to the shore. Based on the ORG, we develop an optimal route protocol(i.e., Lane Post) for each ship to minimize its delay of multi.hop packet delivery via dynamic programming. We discussed the ways of collecting shipping lane information by centralized method or distributed method.The proposed Lane Post protocol was evaluated by ONE, an open.source delay.tolerant network simulator, which shows its dramatic performance improvement in terms of delay reduction compared to the state.of.the.art opportunistic routing protocols.

Uniformed model of networked control systems with long time delay

Feedback control systems wherein the control loops are closed through a real-time network are called networked control systems （NCS）. The defining feature of an NCS is that information is exchanged using a network among control system components. Two new concepts including long time delay and short time delay are proposed. The sensor is almost always clock driven. The controller or the actuator is either clock driven or event driven. Four possible driving modes of networked control systems are presented. The open loop mathematic models of networked control systems with long time delay are developed when the system is driven by anyone of the four different modes. The uniformed modeling method of networked control systems with long time delay is proposed. The simulation results are given in the end.

Parameter Estimation and Topology Identification of Uncertain General Fractional-order Complex Dynamical Networks with Time Delay

Complex networks have attracted much attention from various fields of sciences and engineering in recent years.However, many complex networks have various uncertain information, such as unknown or uncertain system parameters and topological structure, which greatly affects the system dynamics.Thus, the parameter estimation and structure identification problem has theoretical and practical importance for uncertain complex dynamical networks. This paper investigates identification of unknown system parameters and network topologies in uncertain fractional-order complex network with time delays(including coupling delay and node delay). Based on the stability theorem of fractional-order differential system and the adaptive control technique, a novel and general method is proposed to address this challenge. Finally two representative examples are given to verify the effectiveness of the proposed approach.

Taming desynchronized bursting with delays in the Macaque cortical network

Inhibitory coupled bursting Hindmarsh-Rose neurons are considered as constitutive units of the Macaque corti- cal network. In the absence of information transmission delay the bursting activity is desynchronized, giving rise to spatiotemporally disordered dynamics. This paper shows that the introduction of finite delays can lead to the synchro- nization of bursting and thus to the emergence of coherent propagating fronts of excitation in the space-time domain. Moreover, it shows that the type of synchronous bursting is uniquely determined by the delay length, with the transi- tions from one type to the other occurring in a step-like manner depending on the delay. Interestingly, as the delay is tuned close to the transition points, the synchronization deteriorates, which implies the coexistence of different bursting attractors. These phenomena can be observed by different but fixed coupling strengths, thus indicating a new role for information transmission delays in realistic neuronal networks.