Search

In the integrated energy system, the transmission delay of the cooling and heating pipeline network is long, which has an essential impact on the optimal scheduling of the integrated energy system. In this paper, a day-ahead optimal scheduling method of integrated energy systems considering the dynamic delay of the pipeline network is proposed. The method takes into account the impact of pipe network delay on day-ahead optimal scheduling of the system, solves the problem of considerable time lag of pipe network in the process of optimal scheduling of the system, and improves the real-time, safety, and economy of the system scheduling. The method uses the nodal method to establish a dynamic model of the pipeline network system. It analyses the impact of dynamic characteristics on the system equipment's output and system scheduling. A mixed integer linear programming method is used to establish an optimal scheduling model for the integrated energy system, and the system is optimized to maximize the daily revenue in combination with time-of-day tariffs. Finally, the proposed model is validated based on the energy station data in a park in Wuhan. The results show that the optimal scheduling strategy of the integrated energy system that considers the dynamic time-delay characteristics of the pipeline network can effectively improve the system's economy, the system scheduling's real-time performance, and the system operation's safety. [ABSTRACT FROM AUTHOR]

This paper proposes a novel memory state-feedback control (MSFC) strategy for multiagent systems (MASs) operating on time scales. The communication topology is modeled by a collection of directed graphs with switching edges. We analyze the exponential stability of the error dynamics within a leader-follower synchronization framework, crucial for MAS control. Employing linear matrix inequalities (LMIs) and Lyapunov-Krasovskii functionals (LKFs), we establish sufficient conditions to guarantee global exponential stability for the considered systems. Notably, the synchronization conditions for MASs on different time scales differ from those on discrete or continuous time scales alone. This approach allows for a unified framework encompassing both discrete and continuous-time global synchronization problems, Finally, a simulation study utilizing a neural network (NN)-based circuit model demonstrates the effectiveness of the proposed control design. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Synchronization is a key feature of the brain dynamics and is necessary for information transmission across brain regions and in higher brain functions like cognition, learning and memory. Experimental findings demonstrated that in cortical microcircuits there are multiple synapses between pairs of connected neurons. Synchronization of neurons in the presence of multiple synaptic connections may be relevant for optimal learning and memory, however, its effect on the dynamics of the neurons is not adequately studied. Here, we address the question that how changes in the strength of the synaptic connections and transmission delays between neurons impact synchronization in a two-neuron system with multiple synapses. To this end, we analytically and computationally investigated synchronization dynamics by considering both phase oscillator model and conductance-based Hodgkin–Huxley (HH) model. Our results show that symmetry/asymmetry of feedforward and feedback connections crucially determines stability of the phase locking of the system based on the strength of connections and delays. In both models, the two-neuron system with multiple synapses achieves in-phase synchrony in the presence of small and large delays, whereas an anti-phase synchronization state is favored for median delays. Our findings can expand the understanding of the functional role of multisynaptic contacts in neuronal synchronization and may shed light on the dynamical consequences of pathological multisynaptic connectivity in a number of brain disorders. [ABSTRACT FROM AUTHOR]

Introduction: Neural interactions in the brain are affected by transmission delays which may critically alter signal propagation across different brain regions in both normal and pathological conditions. The effect of interaction delays on the dynamics of the generic neural networks has been extensively studied by theoretical and computational models. However, the role of transmission delays in the development of pathological oscillatory dynamics in the basal ganglia (BG) in Parkinson's disease (PD) is overlooked. Methods: Here, we investigate the effect of transmission delays on the discharge rate and oscillatory power of the BG networks in control (normal) and PD states by using a Wilson-Cowan (WC) mean-field firing rate model. We also explore how transmission delays affect the response of the BG to cortical stimuli in control and PD conditions. Results: Our results show that the BG oscillatory response to cortical stimulation in control condition is robust against the changes in the inter-population delays and merely depends on the phase of stimulation with respect to cortical activity. In PD condition, however, transmission delays crucially contribute to the emergence of abnormal alpha (8-13 Hz) and beta band (13-30 Hz) oscillations, suggesting that delays play an important role in abnormal rhythmogenesis in the parkinsonian BG. Discussion: Our findings indicate that in addition to the strength of connections within and between the BG nuclei, oscillatory dynamics of the parkinsonian BG may also be influenced by inter-population transmission delays. Moreover, phase-specificity of the BG response to cortical stimulation may provide further insight into the potential role of delays in the computational optimization of phase-specific brain stimulation therapies. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) has experienced extensive adoption in industrial environments, healthcare, smart cities, and more, playing a vital role in these domains. Within IoT-based systems, wireless sensor networks (WSNs) have emerged as a crucial method for collecting peripheral environmental data within industries, owing to their self-organizational attributes. Nevertheless, the enormous volume of heterogeneous data from various sensing devices presents many challenges for IoT-enabled WSNs, encompassing high transmission delay times (TD) and excessive battery energy consumption (EC). To address these challenges, it is imperative to prioritize efficiency and optimize energy utilization. Moreover, enhancing energy efficiency within the Industrial Internet of Things (IIoT) realm hinges significantly on factors such as data transmission modes and the allocation of cluster head nodes. Numerous researchers have proposed algorithms to minimize transmission time and energy consumption, specifically focusing on industrial environments. This paper introduces an inventive clustering-based data transmission algorithm for IIoT, LEACH-D, to enhance efficiency. The LEACH-D algorithm improves the transmission task duration while maintaining consistent battery energy consumption. It also seeks to elevate performance in metrics such as average transmission time during the first node death (FND). Numerous experimental results provide strong evidence that the algorithm introduced in this paper has effectively reduced the average transmission time by remarkable percentages: 51.32%, 12.12%, 12.96%, and 5.42%, while simultaneously increasing the number of FND rounds by significant margins: 222.43%, 36.63%, 33.72%, and 7.81%, respectively. These improvements stand in stark contrast to the performance of existing algorithms, including FREE_MODE, LEACH, EE-LEACH, and ETH-LEACH.

IntroductionNeural interactions in the brain are affected by transmission delays which may critically alter signal propagation across different brain regions in both normal and pathological conditions. The effect of interaction delays on the dynamics of the generic neural networks has been extensively studied by theoretical and computational models. However, the role of transmission delays in the development of pathological oscillatory dynamics in the basal ganglia (BG) in Parkinson's disease (PD) is overlooked.MethodsHere, we investigate the effect of transmission delays on the discharge rate and oscillatory power of the BG networks in control (normal) and PD states by using a Wilson-Cowan (WC) mean-field firing rate model. We also explore how transmission delays affect the response of the BG to cortical stimuli in control and PD conditions.ResultsOur results show that the BG oscillatory response to cortical stimulation in control condition is robust against the changes in the inter-population delays and merely depends on the phase of stimulation with respect to cortical activity. In PD condition, however, transmission delays crucially contribute to the emergence of abnormal alpha (8–13 Hz) and beta band (13–30 Hz) oscillations, suggesting that delays play an important role in abnormal rhythmogenesis in the parkinsonian BG.DiscussionOur findings indicate that in addition to the strength of connections within and between the BG nuclei, oscillatory dynamics of the parkinsonian BG may also be influenced by inter-population transmission delays. Moreover, phase-specificity of the BG response to cortical stimulation may provide further insight into the potential role of delays in the computational optimization of phase-specific brain stimulation therapies.

To store, analyse and process the large volume of data generated by IoT traditional cloud computing, is used everywhere. However, the traditional cloud data centres have their limitations to handle high latency issues in time-critical applications of IoT and cloud. Their applications are computer gaming, e-healthcare, telemedicine and robot surgery. The high latency in IoTs and cloud includes high computational, communication latency (service) and network latencies. The vital requirement of IoT is to have minimum network, service and computation latencies for real-time applications. Network latency causes a delay in transmitting a message or communication from one location to another. Services that require data in real-time are almost impossible to access the data via the cloud. Traditional cloud computing approaches are unable to fulfil the quality-of-service (QoS) requirements in IoT devices. Researches related to latency reduction techniques are still in infancy. Some new approaches to minimize the latency for transmitting time-sensitive data in real-time are discussed in this paper for cloud and IoT devices. This research will help the researchers and industries to identify the techniques and technologies to minimize the latencies in IoT and cloud. The paper also discusses the research trends and the technical differences between the various technologies and techniques. With the increasing interest in the literature on latency minimization and its requirements for time-sensitive applications; it is important to systematically review and synthesize the approaches, tools, challenges and techniques to minimize latencies in IoT and cloud. This paper aims at systematically reviewing the state of the art of latency minimization to classify approaches, and techniques. The paper uses a PRISMA technique for a systematic review. The paper further identifies challenges and gaps in this regard for future research. We have identified 23 approaches and 32 technologies associated with latencies in the cloud and IoT. A total of 112 papers on latency reduction have been examined under this study. The existing research gaps and works for latency reduction in IoTs are discussed in detail. There are several challenges and gaps, which requires future research work for improving the latency minimization techniques and technologies. Finally, we present some open issues which will determine the future research direction. [ABSTRACT FROM AUTHOR]

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the integrated energy system, significant potential exists for the regulation of the heat storage capacity within the heating network. In relation to this attribute, the establishment of the quasi-dynamic model for the heating network is accomplished through the utilization of the fictitious node method. Additionally, a method is introduced to quantify the heat storage within the heating network. Moreover, a multi-time-scale scheduling approach is proposed for the integrated energy system, with consideration given to the heat storage of the heating network. During the day-ahead scheduling phase, the active regulation of the heat storage within the heating network is carried out to enhance the economy of system operation. Transitioning to the intra-day upper scheduling phase, the heat storage capacity of the heating network is utilized to eliminate the transmission delay effect, thereby achieving the coordinated scheduling of both electricity and heat. Shifting to the intra-day lower scheduling phase, the heat storage capacity of the heating network is utilized to enhance the operational flexibility of the power system. Simulation experiments demonstrate that the coordinated scheduling of electricity and heat in the integrated energy system can be effectively achieved through the utilization of the fictitious node method. Furthermore, the proposed multi-time-scale scheduling method, making full use of the heat storage characteristics of the heating network, can effectively suppress fluctuations in the new energy output and load demand while taking the economy into account. In this paper, it results in a 5.9% improvement in system operating economics and possesses the capacity to mitigate wind power fluctuations with an error rate of approximately 20%. This capability significantly enhances the integration of wind power as a sustainable energy source. [ABSTRACT FROM AUTHOR]

The threat application and data hacking systems rule today's digital world. Hence, securing digital information is the most needed to keep personal detail confidential. Several advanced crypto techniques were implemented in the past, resulting in the finest securing outcome. However, those models required additional features and maintenance based on the utilized applications. Considering these difficulties, Data-encryption-standard has been implemented, and the security functions were enriched by performing the Bernoulli mathematical model. So, the presented technique is named a novel Bernoulli Data-encryption-standard (B-DES). Here, the Bernoulli function has been performed after the XOR process, and then during the decryption process, the function of the novel B-DES has been reversed. Moreover, the designed encryption approach is checked with different attacks like brute force attack, Known plain-text Analysis, Chosen-Plaintext Analysis, Man-in-the-Middle Attack, and Ciphertext analysis. Here, the gained power usage by the designed scheme is 193 mW, compared to other models; it has diminished the power usage by 7%. Moreover, the earned memory utilization is 0.6 kB; compared to other models, it has reduced memory utilization by 3%. The delay rate recorded by the designed model is 3.43 ns; compared to other models, it has minimized the delay score by 3%. [ABSTRACT FROM AUTHOR]

Delay and stability are two key factors that affect the performance of multicast data transmission in a network. However, current algorithms of tree generation hardly meet the requirements of low delay and high stability simultaneously. Given a general network, the generation algorithm of a multicast tree with minimum delay and maximum stability is an NPhard problem, without a precise and efficient algorithm. To address these challenges, this paper studies the generation of low-delay and high-stability multicast trees under the model of spanning tree based on stability probability, degree-constrained, edge-weighted for multicast (T-SDE). A class of algorithms was proposed which creates the multicast tree greedy on the ratio of fan-out to delay (RFD) and probability of stability of terminal to obtain a high performance in multicast. The proposed algorithms greedily select terminals with a large RFD and a high probability of stability as forwarding nodes in the generation of the multicast tree, where the larger RFD and higher stability of upstream nodes are beneficial to achieve a low transmission delay and high stability in multicast. The proposed RFD can be compatible with the original model, which can take advantage of network connectivity during the generation of a multicast tree. This paper carries out simulation experiments on Matlab R2016b to measure the performance of the proposed algorithm. Experimental results show that the proposed algorithm can provide a smaller height, higher stability, and a lower transmission delay of the resulting multicast tree than other solutions. The spanning tree of the proposed algorithms can support low transmission delay and high stability in multicast transmission. [ABSTRACT FROM AUTHOR]

With the intimate integration of power grids and cyber networks, limited bandwidth and packet delay have a rapidly expanding negative impact on power system performance. The presented multi-area interconnected power system consists of four areas, each including thermal and hydro-generation plants. This paper investigates the stability analysis problem for cyber-physical systems with a round-robin communication protocol under mixed cyberattacks and load changes. The objective is to stabilize a multi-area interconnected power system (MAIPS) using a static feedback controller while minimizing the defined performance function. Then, the stability of the MAIPS is characterized when the system is subjected to a transmission delay while considering predetermined limits for the duration and the frequency of the delay. Our findings indicate that time delays can influence system stability and that choosing an appropriate sampling interval is necessary to ensure the stability of the system. Finally, an illustrative example of three areas of interconnected power systems with several scenarios is presented to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the development and deployment of new applications and services over the Internet, it is necessary to determine the impact that they have on availability, losses and errors, delay, and bandwidth. The quality of service and the traffic engineering applied in the design of the networks require performance to be guaranteed, and the most important parameter is the one-way delay (OWD). Properly estimating the OWD and its components is not an easy task, because there are critical factors such as synchronization, operating systems, network topology, among others. In this work we analyze the OWD components in a point-to-point scenario, with emphasis on queuing delay, by implementing QoS policies that allow obtaining delay data using real traffic patterns. Finally, we model the predictive behavior of OWD; the results show that it adjusts to a Polynomial Regression. [ABSTRACT FROM AUTHOR]

Synchronization plays a key role in learning and memory by facilitating the communication between neurons promoted by synaptic plasticity. Spike-timing-dependent plasticity (STDP) is a form of synaptic plasticity that modifies the strength of synaptic connections between neurons based on the coincidence of pre- and postsynaptic spikes. In this way, STDP simultaneously shapes the neuronal activity and synaptic connectivity in a feedback loop. However, transmission delays due to the physical distance between neurons affect neuronal synchronization and the symmetry of synaptic coupling. To address the question that how transmission delays and STDP can jointly determine the emergent pairwise activity-connectivity patterns, we studied phase synchronization properties and coupling symmetry between two bidirectionally coupled neurons using both phase oscillator and conductance-based neuron models. We show that depending on the range of transmission delays, the activity of the two-neuron motif can achieve an in-phase/anti-phase synchronized state and its connectivity can attain a symmetric/asymmetric coupling regime. The coevolutionary dynamics of the neuronal system and the synaptic weights due to STDP stabilizes the motif in either one of these states by transitions between in-phase/anti-phase synchronization states and symmetric/asymmetric coupling regimes at particular transmission delays. These transitions crucially depend on the phase response curve (PRC) of the neurons, but they are relatively robust to the heterogeneity of transmission delays and potentiation-depression imbalance of the STDP profile. [ABSTRACT FROM AUTHOR]

This paper investigates the state prediction problems for uncertain Euler–Lagrange systems with large time delays during data transmissions. A set of sequential predictors is proposed to estimate the actual real-time states of the systems by using the delayed information of measurements. The arbitrarily large delays are handled by applying adequate numbers of serial sub-predictors. Meanwhile, the novel prediction structure of each subsystem is designed to deal with nonlinearities and unknown dynamics in the systems. Then, the predictor design is extended to the case without using delayed velocity measurements by updating the structure of the first sub-predictor. Sufficient conditions for the design of predictor gains, ensuring the boundness of prediction errors, are obtained through Lyapunov–Krasovskii functionals. The effectiveness and robustness of the uncertainties of the proposed method are verified by comparative results in simulations. [ABSTRACT FROM AUTHOR]

Network Function Virtualization (NFV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of next generation communication. Resource scheduling for virtual network function service chain (VNF-SC) mapping is the key issue of the NFV. A virtual network function service chain placement multi-objective optimization model and algorithm based on improved genetic algorithm is proposed. Firstly, a multi-objective optimization model, which minimizes deployment cost, transmission delay and maximizes the load balance, is established. On this basis, an improved genetic algorithm based on MOEA/D is proposed to solve the established multiobjective model. In this algorithm, the combination scheme and mapping scheme of the service request are coded by the two-level coding method in the mapping process, and then the improved sparrow search algorithm is used to obtain the service function chain deployment scheme of the request and calculate the mapping weight. In addition, an efficient individual generation strategy is proposed to generate some superior individual. Finally, some simulation experiments are conducted and the experimental results show that the algorithm can effectively reduce deployment cost and transmission delay than the compared algorithm. [ABSTRACT FROM AUTHOR]

Abstract Military tactical networks (MTNs) have grown in size and complexity as the volume of battlefield communications business has increased dramatically. There is a critical need for a quantitative evaluation method to assess the performance of MTNs. Based on the transmission characters of battlefield communication business and the availability of communication equipment, this paper proposed a Markov chain-based network steady-state availability model. Using this model and the analysis of the CSMA/CA transmission protocol as preconditions, a delay-based quantitative evaluation model of network business availability is proposed based on statistical principles. The relationship between network business availability and crucial metrics such as node size and node failure/repair ratio is investigated in simulation experiments to ensure the validity and correctness of the proposed model. The work done in this paper contributes to effectively evaluating the overall availability of MTNs in a complex environment.

Physical layer security (PLS) is considered one of the most promising solutions to solve the security problems of massive Internet of Things (IoTs) devices because of its lightweight and high efficiency. Significantly, the recent physical layer key generation (PLKG) scheme based on transmission delay proposed by Huang et al. (2021) does not have any restrictions on communication methods and can extend the traditional physical layer security based on wireless channels to the whole Internet scene. However, the secret-sharing strategy adopted in this scheme has hidden dangers of collusion attack, which may lead to security problems such as information tampering and privacy disclosure. By establishing a probability model, this article quantitatively analyzes the relationship between the number of malicious collusion nodes and the probability of key exposure, which proves the existence of this security problem. In order to solve the problem of collusion attack in Huang et al.’s scheme, this article proposes an anti-collusion attack defense method, which minimizes the influence of collusion attack on key security by optimizing parameters including the number of the middle forwarding nodes, the random forwarding times, the time delay measurement times and the out-of-control rate of forwarding nodes. Finally, based on the game model, we prove that the defense method proposed in this article can reduce the risk of key leakage to zero under the scenario of the “Careless Defender” and “Cautious Defender” respectively.

The ambient temperature and the time delay of signal transmission are very important influences on the synchronization behavior of neuronal networks. In this paper, a neuronal network with the power-law degree distribution is constructed using a Hodgkin–Huxley model containing a temperature modulation factor and noise, and neurons at each node of the scale-free network are interconnected by electrical and chemical synapses, respectively. In scale-free networks with different ambient temperatures, the absence of time delay causes the synchronization of networks connected by both synaptic types to increase with coupling strength at lower temperatures, while the opposite is shown for networks connected by chemical synapses at higher temperatures. Networks connected by both synaptic types show multiple synchronization transitions when there is the time delay. Surprisingly, there is a temperature threshold for scale-free networks connected by chemical synapses, beyond which synchronization becomes very poor. By introducing the coefficient of variation and the mean inter-spikes intervals, it is found that the emergence of temperature thresholds for networks connected by chemical synapses is caused by a further increase in the difference in firing frequency of neurons due to increasing temperature. Finally, the generality of the results and mechanisms studied in scale-free networks is verified by investigating the effects of different network scales on synchronization. [ABSTRACT FROM AUTHOR]

Due to the limited energy in underwater sensor networks, underwater nodes need to be deployed sparsely. However, sparse USNs will lead to poor tracking coverage and detection capability. To solve these problems, the mobility of nodes in depth can be utilized to optimize the node topology to achieve data fusion more reliably and effectively. In this paper, for underwater target tracking, a node depth adjustment algorithm is proposed. Firstly, after introducing the sound velocity profile on acoustic signal transmission, the asynchronous particle filter algorithm based on delay estimation is improved, which makes the filter more suitable for an underwater environment. Secondly, the influence of node topology on the tracking accuracy is analyzed, and the optimization problem of node depth adjustment is constructed, in which the depth-related Fisher Information Matrix is designed as the optimization criterion. Thirdly, for scenarios in which the target depth is either known or unknown, the analytical method and the interior point method are employed to solve the problem, respectively, and the optimal depth adjustment strategies in corresponding scenarios are obtained. The simulation results show that the proposed algorithm can fully adjust the node depth and achieve a more accurate tracking performance. [ABSTRACT FROM AUTHOR]

Copyright of Railway Signalling & Communication Engineering is the property of Railway Signalling & Communication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A group coupling model for a system with large-scale nodes is investigated. The model is formulated as a system of functional differential equations. It incorporates two additional factors that exist in the evolution of flocking behavior, but are often ignored in modeling: (i) the diversity of interactions, including inter-group and intra-group interactions and (ii) the delayed response of particles to signals from the environment or neighbors, including transmission and processing delays. Theoretically, using the divide-and- conquer method and under different delay factors, sufficient conditions for self-organizing flocking are derived by constructing a dissipative differential inequalities with continuous parameters respectively, which involve some analytical expressions of the upper bound of the delay that the system can tolerate. Results of systematic numerical simulations are presented. They not only validate the analytical results, but hint at a somehow surprising behavior of system, that is, weak flocking behavior occurs when two types of delays coexist. [ABSTRACT FROM AUTHOR]

Sensor data exchanges in IoT applications can experience a variable delay due to changes in the communication environment and sharing of processing capabilities. This variability can impact the performance and effectiveness of the systems being controlled, and is especially reflected on Age of Information (AoI), a performance metric that quantifies the freshness of updates in remote sensing. In this work, we discuss the quantitative impact of activation and propagation delays, both taken as random variables, on AoI. In our analysis we consider an offline scheduling over a finite horizon, we derive a closed form solution to evaluate the average AoI, and we validate our results through numerical simulation. We also provide further analysis on which type of delay has more influence on the system, as well as the probability that the system fails to deliver all the scheduled updates due to excessive delays of either kind. [ABSTRACT FROM AUTHOR]

The convergence of Artificial Intelligence (AI) can overcome the complexity of network defects and support a sustainable and green system. AI has been used in the Cognitive Internet of Things (CIoT), improving a large volume of data, minimizing energy consumption, managing traffic, and storing data. However, improving smart packet transmission scheduling (TS) in CIoT is dependent on choosing an optimum channel with a minimum estimated Packet Error Rate (PER), packet delays caused by channel errors, and the subsequent retransmissions. Therefore, we propose a Generative Adversarial Network and Deep Distribution Q Network (GAN-DDQN) to enhance smart packet TS by reducing the distance between the estimated and target action-value particles. Furthermore, GAN-DDQN training based on reward clipping is used to evaluate the value of each action for certain states to avoid large variations in the target action value. The simulation results show that the proposed GAN-DDQN increases throughput and transmission packet while reducing power consumption and Transmission Delay (TD) when compared to fuzzy Radial Basis Function (fuzzy-RBF) and Distributional Q-Network (DQN). Furthermore, GAN-DDQN provides a high rate of 38 Mbps, compared to actor-critic fuzzy-RBF’s rate of 30 Mbps and the DQN algorithm’s rate of 19 Mbps.

In order to solve the problems of transmission delay and data loss caused by network congestion and strengthen the quality of service of transmission delay and throughput, a method of flow control in network media information transmission based on differential evolution algorithm was presented. By analyzing the delay effect of buffer on network instability, the problems of flow control at the receiving end were clarified. Moreover, the jitter between packet and end-to-end time delay parameters in dynamic caching mechanism were analyzed. The maximum and minimum values were set to calculate the balance point of real-time communication and smooth network information transmission flow. In addition, the differential evolution algorithm based on adaptive quadratic mutation of population fitness variance was used to improve the adaptive global optimization structure, organize the minimum evolution and input the minimum sample. Through reasonable selection of controller parameters and adaptive calculation, the poles of the closed-loop control system were obtained. Finally, the transmission performance index could be optimized. Thus, the stable control of transmission flow was completed. Simulation results show that the proposed algorithm can effectively improve the transmission performance of heterogeneous networks. Meanwhile, it has obvious advantages when the asymmetric parameters are high. [ABSTRACT FROM AUTHOR]

This article studies the quantized control problem of a scalar continuous‐time linear system over a digital network. The network is subject to bounded transmission delay and independent and identically distributed packet dropouts. Event‐triggered sampling is implemented in the system. We first derive a lower bound on the necessary bit rate for any event‐triggered strategy by analyzing the evolution of the uncertainty set of the system state. Then we propose a control strategy which combines both event‐triggering and time‐triggering to stabilize the system. Note that our control strategy implements a dynamic encoding policy, under which the number of quantization bits is dependent on dropouts. Although the average bit rate occupied by our control strategy is higher than the obtained lower stabilizing bit rate bound, their gap can arbitrarily approach zero by relaxing the integer requirement of the number of quantization bits. Numerical examples are given to illustrate the derived results. [ABSTRACT FROM AUTHOR]

To improve the performance of IEEE 802.11 wireless local area (WLAN) networks, different frame-aggregation algorithms are proposed by IEEE 802.11n/ac standards to improve the throughput performance of WLANs. However, this improvement will also have a related cost in terms of increasing delay. The traffic load generated by mixed types of applications in current modern networks demands different network performance requirements in terms of maintaining some form of an optimal trade-off between maximizing throughput and minimizing delay. However, the majority of existing researchers have only attempted to optimize either one (to maximize throughput or minimize the delay). Both the performance of throughput and delay can be affected by several factors such as a heterogeneous traffic pattern, target aggregate frame size, channel condition, competing stations, etc. However, under the effect of uncertain conditions of heterogeneous traffic patterns and channel conditions in a network, determining the optimal target aggregate frame size is a significant approach that can be controlled to manage both throughput and delay. The main contribution of this study was to propose an adaptive aggregation algorithm that allows an adaptive optimal trade-off between maximizing system throughput and minimizing system delay in the WLAN downlink MU-MIMO channel. The proposed approach adopted different aggregation policies to adaptively select the optimal aggregation policy that allowed for achieving maximum system throughput by minimizing delay. Both queue delay and transmission delay, which have a significant impact when frame-aggregation algorithms are adopted, were considered. Different test case scenarios were considered such as channel error, traffic pattern, and number of competing stations. Through system-level simulation, the performance of the proposed approach was validated over the FIFO aggregation algorithm and earlier adaptive aggregation approaches, which only focused on achieving maximum throughput at the expense of delay. The performance of the proposed approach was evaluated under the effects of heterogenous traffic patterns for VoIP and video traffic applications, channel conditions, and number of STAs for WLAN downlink MU-MIMO channels. [ABSTRACT FROM AUTHOR]

NDN is a band new Internet architecture, which aims to cope with the increasing growth of data traffic. NDN brings native support for consumer mobility due to its receiver-driven content retrieval model. However, producer mobility is still a challenging issue, which needs additional mechanisms to improve the data availability during the producer’s movement. Therefore, this paper proposed a scalable mobility-management mechanism, which further extends the stateful forwarding plane of NDN to support producer mobility. The mechanism built a temporary forwarding path on the name-based NDN forwarding plane through network terminals and designed a buffering and retransmission mechanism to support both delay-tolerant and delay-sensitive data traffic. Finally, this paper set up a comprehensive simulation environment in ndnSIM for the proposed evaluation and comparison against the existing classes of solutions. The simulation results show that the proposed can fully support producer mobility in the wireless environment. When the speed is 30 m/s, the packet loss rate is only 3.0 %, and the average transmission delay is 352.1 ms. Additionally, the additional consumption required to support producer mobility is reduced by 49.18 % compared to the comparison scheme. [ABSTRACT FROM AUTHOR]

This article proposes a robust $\mathcal {H}_\infty $ output feedback control strategy to improve vehicle lateral stability through active front wheel steering with quantization error, transmission delay and data dropouts. Since vehicle lateral dynamics presents inherently uncertain challenges, the polytopic technique is used and the variation of tire cornering stiffness is compensated via norm-bounded uncertainty. Meanwhile, considering that the measurement outputs are inevitably suffer from network-induced constrains, i.e. the quantization error, transmission delay and data dropouts, which degrade the stability and handling performance of vehicle, a robust gain-scheduling $\mathcal {H}_\infty $ output feedback controller design strategy is proposed and the stability conditions are presented in the form of linear matrix inequalities that are derived from Lyapunov asymptotic stability and quadratic $\mathcal {H}_\infty $ performance. Finally, two simulation cases are carried out with MATLAB/Simulink-Carsim to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Military tactical networks (MTNs) have grown in size and complexity as the volume of battlefield communications business has increased dramatically. There is a critical need for a quantitative evaluation method to assess the performance of MTNs. Based on the transmission characters of battlefield communication business and the availability of communication equipment, this paper proposed a Markov chain-based network steady-state availability model. Using this model and the analysis of the CSMA/CA transmission protocol as preconditions, a delay-based quantitative evaluation model of network business availability is proposed based on statistical principles. The relationship between network business availability and crucial metrics such as node size and node failure/repair ratio is investigated in simulation experiments to ensure the validity and correctness of the proposed model. The work done in this paper contributes to effectively evaluating the overall availability of MTNs in a complex environment. [ABSTRACT FROM AUTHOR]

Caching technique is a promising approach to reduce the heavy traffic load and improve user latency experience for the Internet of Things (IoT). In this paper, by exploiting edge cache resources and communication opportunities in device-to-device (D2D) networks and broadcast networks, two novel coded caching schemes are proposed that greatly reduce transmission latency for the centralized and decentralized caching settings, respectively. In addition to the multicast gain, both schemes obtain an additional cooperation gain offered by user cooperation and an additional parallel gain offered by the parallel transmission among the server and users. With a newly established lower bound on the transmission delay, we prove that the centralized coded caching scheme is order-optimal, i.e., achieving a constant multiplicative gap within the minimum transmission delay. The decentralized coded caching scheme is also order-optimal if each user's cache size is larger than a threshold which approaches zero as the total number of users tends to infinity. Moreover, theoretical analysis shows that to reduce the transmission delay, the number of users sending signals simultaneously should be appropriately chosen according to the user's cache size, and always letting more users send information in parallel could cause high transmission delay. [ABSTRACT FROM AUTHOR]

Copyright of Railway Signalling & Communication Engineering is the property of Railway Signalling & Communication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article concerns the distributed sampled-data nonfragile consensus filter design for a continuous-time linear system with respect to exogenous disturbance. A network of sensor nodes is employed to monitor the plant. The information from each sensor node and its neighbors is aperiodically sampled while collaboratively transmitted in the network, where the phenomena of switching directed topologies and network-induced transmission delay are unavoidably existent. The estimated state of the plant at each node is updated by a consensus of state estimates from its neighbors. By acquiring the sensor output measurement, a Luenberger-type filter is constructed not only to provide robustness against some level of filter gain perturbations, but also to guarantee the asymptotic stability of the resultant filtering error system with an $H_{\infty }$ performance requirement. Based on algebraic graph theory, nonfragile synthesis technique and Lyapunov–Krasovskii functional method, the filter parameters are characterized in terms of some feasible solutions to certain linear matrix inequalities. The theoretical analysis is validated by numerical simulations. [ABSTRACT FROM AUTHOR]

To meet the requirements of 5G system with low delay and high reliability, for a single-caching D2D cooperative edge caching system, an optimization of the caching strategy based on transmission delay was proposed.The dynamic distributions of requesting users and idle users were modeled as the independent homogeneous poisson point process (HPPP) utilizing the stochastic geometry theory.Comprehensively considering the content popularity, user location information, device transmission power and interference, the relationship between the average transmission delay of user and the cache probability distribution was derived.Taking the average transmission delay as the objective function, the optimization problem was established, and a low complexity iterative algorithm was proposed to obtain the cache strategy with sub-optimal average transmission delay.Simulation results demonstrate that the proposed optimization cache strategy is superior to several common cache strategies in terms of transmission delay.

This paper addresses the impulsive observers design problem of perturbed Lipschitz nonlinear systems subject to noisy delayed discrete measurements. The transmission delay, state perturbation, and measurement noise simultaneously hinder the convergence of impulsive observer. To deal with the difficulty, the observation error system with delayed impulses is represented as an augmented system with switching delay-free impulses. By taking into account the switching structure of the impulse dynamics, the impulse observation gain matrix is modelled as a function of the time interval between two consecutive sampling instant and update instant. Such a structure of the observation gain matrix is able to adapt to the variation of sampling period and transmission delay. To obtain a tractable design condition, the idea of piecewise linear interpolation is applied to represent the impulse observation gain matrix as a piecewise linear form, which is determined by finite static impulse gains. A piecewise Lyapunov function which matches the piecewise linear structure of the observation gain is constructed to analyze the exponential input-to-state stability (EISS) property of the observation error system. The EISS gain quantifies the robustness performance of the designed impulsive observer. Sufficient conditions for the existence of the proposed variable gain impulsive observers are presented. Two numerical examples are provided to illustrate the effectiveness of the developed impulsive observation strategy. • A methodology for designing impulsive observers with delayed measurements and disturbances is proposed. • Based on the augmented representation of the observation error system, a piecewise linear gain function is constructed. • The robustness performance of the proposed observers against external disturbance is characterized by the notion of EISS. [ABSTRACT FROM AUTHOR]

This paper investigates the state prediction problems for uncertain Euler–Lagrange systems with large time delays during data transmissions. A set of sequential predictors is proposed to estimate the actual real-time states of the systems by using the delayed information of measurements. The arbitrarily large delays are handled by applying adequate numbers of serial sub-predictors. Meanwhile, the novel prediction structure of each subsystem is designed to deal with nonlinearities and unknown dynamics in the systems. Then, the predictor design is extended to the case without using delayed velocity measurements by updating the structure of the first sub-predictor. Sufficient conditions for the design of predictor gains, ensuring the boundness of prediction errors, are obtained through Lyapunov–Krasovskii functionals. The effectiveness and robustness of the uncertainties of the proposed method are verified by comparative results in simulations.

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)