Network Time Delay Research Articles

Best candidate routing algorithms integrated with minimum processing time and low blocking probability for modern parallel computing systems

<p>This study has clarified the best candidate routing algorithms integrated with minimum processing times and low blocking probabilities for modern parallel computing systems. Different methods were employed, such as the fast window method (FWM), fast bitwise window method (FBWM), and fast improved window method (FIWM), to upgrade the processing time and reduce the network delay time. In addition, different algorithms were studied such as the fast window ascending, the fast window descending, the fast window sequential algorithm, and the fast window sequential down algorithms; these were studied to show the numerical results of the networks’ blocking probabilities, processing times, and delay times.</p>

Robust High-Rate Secondary Control of Microgrids With Mitigation of Communication Impairments

The dynamic response speed of the secondary control (SC) in microgrids (MGs) is limited both by the communication network time delays and the bandwidth (BW) of the primary control (PC). By increasing the PC BW, which depends on how to design and tune the PC, the time delay issues will be multiplied due to the speed range of the communication modules and technologies. This article proposes a communication compensation block (CCB) to enhance the robustness of distributed SC against communication impairments in the MGs operated at the higher BW. The proposed method mitigates malicious time delays and communication non-ideality in distributed networked controls employed in the secondary layer of the MG by prediction, estimation and finally decision on transmitted data. A comprehensive mathematical model of the employed communication network is presented in details. Then, a robust data prediction algorithm based on the temporal and spatial correlation is applied into the SC to compensate for time delays and data packet loss. Furthermore, the effect of the number of stored packets and burst packet loss on the average success rate of the communication block, and the small signal stability analysis of the system in the presence of the CCB are investigated. Power hardware-in-the-loop (PHiL) experimental tests show the merits and applicability of the proposed method.

Distributed backstepping based control of multiple UAV formation flight subject to time delays

In this study, the authors propose a backstepping-based, distributed formation control method that is stable independent of time delays in communication among multiple unmanned aerial vehicles (UAVs). Centralised formation control of UAVs requires each agent to maintain a separation distance from other agents, which burdens the communication network of the UAVs. To overcome this problem, the authors consider a distributed control scheme wherein each agent updates its attitude and position based on the state information gathered through its neighbours. Instead of directly controlling the thrust generated by the propellers, they partition the mathematical model of the UAV into two subsystems, a linear attitude control loop and a non-linear position control loop. A backstepping-based outer position controller is then designed that interfaces seamlessly with the inner attitude controller of the cascaded control system. The closed-loop stability is established using a rigorous Lyapunov–Krasovskii analysis under the influence of distributed network time delays. Using the directed graph topology and a distributed backstepping structure, it is shown that the stability criterion is delay-independent. The proposed control algorithms are verified in simulation and then implemented in hardware, and actual flight test experiments prove the validity of these algorithms.

PCM: A Positioning Calibration Mechanism for Connected Autonomous Vehicles

Connected autonomous driving is considered as a potential way to improve traffic safety. However, when positioning information is delivered through network, the network time delay could result in additional positioning errors, which has been proved by the experiment done in this paper in XuChang automatic driving center that the influence of one-way time delay will lead to meter-scale positioning error. In this paper, we propose a positioning calibration mechanism for connected autonomous vehicles called PCM. PCM firstly estimates the network time delay and then saves the calibrated location information in the MEC server to reduce the positioning error caused by network time delay. Experiments in FangShan and XuChang automatic driving centers verify the performance of PCM, and the result shows that PCM can reduce the positioning error to centimeter-level. For example, under 100ms time delay, the average positioning error without PCM is 119.67cm, while the average positioning error with PCM is 3.03cm. Experimental results show that PCM can effectively reduce the positioning error caused by a one-way time delay to centimeter-level, which is strictly required for positioning autonomous vehicles in automatic driving.

Research on power regulation service data transmission path optimisation method

Owing to the large amount of transmission data and transmission paths between equipment and applications in the power grid control centre, it is easy to cause problems such as packet loss and incomplete data transmission. An improved Apriori algorithm is proposed to mine multi-dimensional data association rules when multiple service devices are running in parallel, then optimise the discontinuous data path transmission based on the ant colony algorithm. The ant colony migration probability is used to calculate the pheromone concentration value of the ant on the discontinuous data path in a fixed period, plan the ant travel path, use the direction factor to adjust the discontinuous data transmission path appropriately, determine the optimal path, and realise the mining optimisation. Simulation results show that the proposed method can effectively improve the convergence speed of discontinuous data transmission, reduce the delay time of the data transmission network, and ensure the integrity of data transmission.

Research on power regulation service data transmission path optimisation method

Owing to the large amount of transmission data and transmission paths between equipment and applications in the power grid control centre, it is easy to cause problems such as packet loss and incomplete data transmission. An improved Apriori algorithm is proposed to mine multi-dimensional data association rules when multiple service devices are running in parallel, then optimise the discontinuous data path transmission based on the ant colony algorithm. The ant colony migration probability is used to calculate the pheromone concentration value of the ant on the discontinuous data path in a fixed period, plan the ant travel path, use the direction factor to adjust the discontinuous data transmission path appropriately, determine the optimal path, and realise the mining optimisation. Simulation results show that the proposed method can effectively improve the convergence speed of discontinuous data transmission, reduce the delay time of the data transmission network, and ensure the integrity of data transmission.

Performance of SIMO Networked Time-Delay Systems With Encoding–Decoding and Quantization Constraints

In this paper, the performance of the single-input multiple-output (SIMO) networked time-delay systems is investigated. The performance is related to the internal factors, communication parameters and reference signals, and the boundary is related to the adjustment factors. Some new results are derived according to the inner-outer factorization and Cauchy’s Theorem of two degrees-of-freedom controller. The results show that the performance is in connection with the inner factor (unstable poles, non-minimum phase zeros of the system). It is also demonstrated that the modified performance will be badly degraded by feedback channel quantization and adjustment factor constraints, and encoding-decoding is beneficial to the modified performance.

Experimental verification of an accessible geographically distributed real‐time hybrid simulation platform

Real-time hybrid simulation (RTHS) has become a recognized methodology for isolating and testing complex, rate-dependent structural components and devices to understand their behavior and to evaluate their ability to improve the performance of structures exposed to severe dynamic loading. Although RTHS is efficient in its utilization of equipment and space compared with conventional testing techniques, the laboratory resources may not always be available in a single testing facility to conduct large-scale experiments. Consequently, distributed systems, capable of connecting multiple RTHS setups located at several geographically distributed facilities through appropriate information exchange, become desirable. This study presents a distributed RTHS (dRTHS) platform that enables the integration of geographically distributed physical and numerical components across the Internet. The essential capabilities needed to establish such a dRTHS platform are discussed, including the communication architecture, network components, and connection reliability. One significant challenge for conducting successful dRTHS is sustaining real-time communication between test sites. To accommodate realistic network delays due to variations in the Internet service, a Smith predictor-based delay compensation algorithm that includes a network time delay estimator is developed. A series of numerical and experimental studies is conducted to verify the platform and to quantify the impact of uncertainties present in a typical distributed system. Through an evaluation of the results, it is demonstrated that dRTHS is feasible for coupling laboratory capabilities and is a viable alternative to traditional testing techniques.

Review on big data application of medical system based on fog computing and IoT technology

From the current general situation of big data in medical systems, most of the medical system architectures of comprehensive related literature are based on cloud computing. With the deepening of big data application in the medical system, the drawbacks of medical network systems are apparent, for example, long network time delay, cloud computing overload, information leakage. A new intelligent medical system based on fog computing and IoT technology is proposed to effectively solve the problem of big data application through the comprehensive analysis of many works of literature. Compared with the traditional cloud computing model, the system which adopts a distributed fog computing model based on edge computing has the advantages of reducing network delay and plays its benefits of distributed, edge and decentralization. That the system takes advantage of IoT’s features of multi-node, low-energy consumption and big data technology is a new type of medical system that integrates intelligence and big data application. The system is effective through the application and practice of many medical institutions. In short, this system has several characteristics. For example, it can effectively improve medical intelligence and big data processing ability, significantly reduce network delay, effectively improve data security and effectiveness, largely improve the management level and service quality of medical institutions, play the important role of artificial intelligence and big data in the medical field, and greatly improve disease screening rate and reduce misdiagnosis rate. Ultimately it leads to reducing the contradiction between doctors and patients, providing patients with more convenience, faster and more efficient service quality, and alleviating the current situation of difficult and time-consuming medical treatment.

Predictive Controller Design for Networked Control System

A networked control system where sensor ( ), controller ( ) and an actuator ( ) are interacting to the process model or system through a communication channel. The model reliability depends on time delay of data transmission from to , to , controller processing time and packet dropouts. In this proposed work, model based network system is designed to achieve the desired response of the controlled system by minimizing the network time delay and improves con-troller performance in term of stability. Time delay mainly occurs due to time sharing of the medium as well extra overload of signal coding and processing phase. The time delays could be steady, limited, or even random, depends upon protocol used in the network and hardware. The network predictive control tech-nique effectively works on random delay. The predictive controller is used to analyses the future output conditions of the system based on the previous values. The simulated result shows that if time delay increases, the response of the system degraded and therefore for improving it the correct choice of prediction and control horizon is needed. The designed networked control system based on predictive functional control is simulated using True Time tool on Matlab. The ob-tained outcomes from the proposed technique accomplishes that the technique is effective and feasible.

Addressing Application Latency Requirements through Edge Scheduling

Latency-sensitive and data-intensive applications, such as IoT or mobile services, are leveraged by Edge computing, which extends the cloud ecosystem with distributed computational resources in proximity to data providers and consumers. This brings significant benefits in terms of lower latency and higher bandwidth. However, by definition, edge computing has limited resources with respect to cloud counterparts; thus, there exists a trade-off between proximity to users and resource utilization. Moreover, service availability is a significant concern at the edge of the network, where extensive support systems as in cloud data centers are not usually present. To overcome these limitations, we propose a score-based edge service scheduling algorithm that evaluates network, compute, and reliability capabilities of edge nodes. The algorithm outputs the maximum scoring mapping between resources and services with regard to four critical aspects of service quality. Our simulation-based experiments on live video streaming services demonstrate significant improvements in both network delay and service time. Moreover, we compare edge computing with cloud computing and content delivery networks within the context of latency-sensitive and data-intensive applications. The results suggest that our edge-based scheduling algorithm is a viable solution for high service quality and responsiveness in deploying such applications.

Research of NCS Random Time Delay Prediction Based on FNN-PSO

Networked control systems have been attracting considerable attention during the last decades, not only because of their theoretical value, but also of their widespread practical applications. In practice, networked control systems are inevitably affected by the random time delays. Aiming at the problem of system performance degradation caused by random time delay in networked control systems, a method of combining particle swarm optimization with fuzzy neural network is used to establish a networked control system time delay prediction model and predicts the future time delay. According to the simulation, we verify the effectiveness and feasibility of the proposed method. The results of simulation show that the fuzzy neural network optimized by particle swarm optimization has a high prediction accuracy for random time delay and is suitable for practical applications.

A NOVEL APPROACH TO ENHANCE THE PERFORMANCE OF MOBILE AD HOC NETWORK (MANET) THROUGH A NEW BANDWIDTH OPTIMIZATION TECHNIQUE

Now is the age of information technology. World is advancing day by day. At present in this progressing world communication from one place to another has become so easy, less costly, and faster. This modern life is almost impossible with the help of these communication technologies. People need to talk, need to share data, need to express their emotion from long distance. So they need to use technologies to communicate with one another. Nowadays the fields of MANET have yielded more and more popularity and thus MANET have become a subject of great interest for the researchers to enforce research activities. Mobile Ad Hoc Network (MANET) is the cooperative engagement of a collection of mobile nodes without the required intervention of any centralized access point or existing infrastructure. There is an increasing trend to adopt mobile ad hoc networking for commercial uses. Mobile Ad Hoc Network (MANET) is an emerging area of research to provide various communication services to the end users. But these communication services of Mobile Ad Hoc Network (MANET) use high capacity of bandwidth and a big amount of internet speed. Bandwidth optimization is indispensable in various communications for successful acceptance and deployment of such a technology. Thinking of this, I propose a New Bandwidth Optimization Technique that Enhance the Performance of Mobile Ad Hoc Network (MANET). The new Bandwidth optimization technique which is more efficient in terms of time delay in Mobile Ad Hoc Network (MANET) can redirect a new way towards optimization development in network communication and device junction technology.

ENERGY EFFICIENT LEACH AND IMPROVED LEACH: A REVIEW

Energy Efficiency is the most crucial requirement of sensor nodes in WSN networks. Sensor nodes are the communicating nodes of wireless network which lost their energies with package of time due to having small battery life. So, for sustaining the wireless network to work for longer period of time there requires the need of energy efficient routing protocols [1]. Here in this paper we are presenting LEACH (Low Energy Adaptive Clustering Hierarchy) and PEGASIS (Power Efficient Gathering in Sensor Information System) protocols [2] as most energy efficient protocols to prolong the higher lifetime and small delay time of WSN network .

The role of network structure and time delay in a metapopulation Wilson--Cowan model

We study the dynamics of a network Wilson--Cowan model (a system of connected Wilson--Cowan oscillators) for interacting excitatory and inhibitory neuron populations with time delays. Each node in this model corresponds to a population of neurons, including excitatory and inhibitory subpopulations, and hence it can be viewed as a metapopulation model. It is known that information transfer within each cortical area is not instantaneous, and therefore we consider a system of delay differential equations with two different kinds of discrete delays. We account for the time delay in information propagation between individual excitatory and inhibitory subpopulations at each node via intra-node time delays, and we account for time delay in information propagation between neuron populations at different nodes with inter-node time delays. The biologically relevant resting state solutions are oscillatory (stable limit cycles). After determining the influence of the coupling parameters between nodes, the intra-node delays, and the inter-node delays on the dynamics of the two coupled Wilson--Cowan oscillators, we then explore a variety of larger networks of 16 and 100 nodes, in order to determine how the network topology will influence time delayed Wilson--Cowan dynamics. We find that network structure can regularize or deregularize the dynamics, with networks of higher mean degree permitting stable limit cycles and networks with smaller mean degree yielding less regular dynamics (which may range from chaotic solutions, to solutions for which limit cycles collapse into steady states, which are biologically undesirable compared with the preferred stable limit cycles). Furthermore, heterogeneity in the degree distribution of the network (resulting from networks with nodes of varying degree) can result in asynchronous dynamics, even if at each node the local dynamics are that of a limit cycle, in contrast to the synchronization of dynamics between nodes seen when the degree of all nodes is equal. This suggests that homogeneous and well-connected networks permit robust limit cycles under time-delayed Wilson--Cowan dynamics, whereas heterogeneous or poorly connected networks may fail to provide such desirable dynamics, a phenomena akin to structural loss of neuron connections in neurodegenerative diseases.

A cell-based dynamic congestion pricing scheme considering travel distance and time delay

This study introduces the dynamic congestion pricing (DCP) problem with the consideration of the actual travel distance and time delay (i.e. a joint distance and time-delay toll, JDTDT) in a dynamic network, which is more equitable and effective compared with existing tolling scheme. The system dynamics can be reflected in two aspects: (a) travelers' path choice decisions follow the dynamic user equilibrium principle and (b) the joint distance and time-delay toll takes a time-varying pattern. A multiperiod demand scheme is adopted during the entire modeling horizon and a bi-level programming model for the DCP is formulated to obtain the optimal toll value. Numerical results indicate that the percentage reductions of the minimum total system travel time in the dynamic JDTDT scheme are 6.28%, 4.30% and 7.45% compared to that obtained by the static joint JDTDT, the dynamic joint distance and time toll, and the dynamic pure distance toll, respectively.

An Influencing Parameters Analysis of District Heating Network Time Delays Based on the CFD Method

With the expansion of cities, district heating (DH) networks are playing an increasingly important role. The energy consumption due to the time delay caused by the transport of the medium in the DH network is enormous, especially in large networks. The study of time delay is necessary for the operation and optimization of DH networks. Compared with previous studies of constant flow rates and ideal pipeline (without regard to branches, elbows, variable pipe diameters, etc), this paper simulates a DH network in Tianjin University, China, by establishing the actual engineering model in a Computational Fluid Dynamics (CFD) method to analyze the time delay. The CFD model has great advantages in terms of computational cost and application range compared to theoretical calculations. The peak-valley method was used to verify the correctness of the time delay simulation model. Results show that the time delay calculated by the CFD model is consistent with the actual time delay obtained from the measured data. Based on this model, the parameters that affect the time delay are furtherly analyzed. Four key parameters, including flow rate, pipe length, pipe diameter, and water supply temperature are summarized. The results show that the flow rate, pipe length and pipe diameter have a great influence on the time delay of the DH network, while the temperature has little effect on the time delay. The time delay of the DH network system has a significant impact and can provide services for optimal control of the system.

Research on water and fertilizer irrigation system of tea plantation

In this article, water and fertilizer irrigation system of tea plantation was developed to ensure stable yield, quality of tea, and appropriate irrigation and fertilization. Wireless sensor network node and gateway node are designed and deployed in the tea plantation for collecting soil moisture in real time. Each of the sensor nodes was composed of a STM32F103ZET6 microprocessor and a CC2420 transceiver module. Microcontroller S3C2410X is the kernel of hardware platform in the gateway nodes. All data were transmitted by wireless sensor network. The images of the tea leaves are collected by the high-speed camera loaded onto the unmanned aerial vehicle for analysis of the tea deficiency. The monitoring system is mainly used to display the humidity information about each node, the switch status of the water pump, the battery valve, and so on. It is convenient for user to manage the tea plantation. System configuration and parameter modification can be configured through the monitoring system. The dynamic time warping algorithm is used to judge the abnormal situation of the system. Diamond deployment is used in the network, and the networking experiments were conducted comparing with random deployment. Result showed that both time delay and congestion of network increased as the network scale varied from 5, 10, and 15 to 20 nodes. The topology stabilization time gets prolonged simultaneously. Packet loss rate decreased while data transmission interval varied from 10, 20, and 30 to 40 s. Packet loss rate values in diamond deployment are lower than those in random deployment. In order to improve the accuracy of tea deficiency detection, data fusion technology was adopted. A block-based histogram is employed and similarity distance is adopted to confirm the diagnosis of absence of one or more nutrients of tea. The image information and the spectral information are acquired. The principal component factor is extracted and input into the back propagation neural network to judge the quality of the tea. The number of principal component factors of image information and spectral information is set to six and three; the overall recognition rate reached 97.8%. Therefore, the level of the system abnormality can be determined by the dynamic time warping distance. Test results indicate that it is possible to accurately determine whether tea is deficient and accurately determine whether the system data acquisition is abnormal.

An Adaptive MAC Protocol Based on IEEE802.15.6 for Wireless Body Area Networks

The application carrier of wireless body area network (WBAN) is human; due to changes in people’s sports status or physical health and other reasons, the business traffic fluctuates greatly, which requires the network to have good adaptability. In addition, the energy consumption problem is also a key factor restricting the applications of the WBAN. At present, the proposed MAC protocol is not highly adaptive and has low energy efficiency. To solve this problem, this paper proposes an adaptive MAC protocol based on IEEE802.15.6 for WBAN (A-MAC). The protocol sets the data to three priorities according to the type of service; the superframe structure of IEEE802.15.6 is improved and reorganized into four phases: the beacon phase, the contention access phase, the noncontention access phase, and the inactive phase. The length of the contention access phase and the noncontention access phase is adjusted according to the proportion of nodes that generate each priority data. The contention access phase is further divided into three subphases, and the length of the subphase is dynamically adjusted according to the data priority. In the contention access phase, all nodes compete for access channel according to the channel access policy. The random data that competes successfully transmits data directly, and the periodic data that competes successfully transmits data in the allocated time slots of the noncontention access phase. Finally through the simulation of the proposed A-MAC protocol and IEEE 802.15.6 MAC protocol and CA-MAC protocol in network performance which were compared, the results show that in terms of throughput, power consumption, and the network time delay, the network performance using A-mac protocol is better than the network performance using IEEE802.15.6 MAC and CA-MAC protocols.

Traffic and Computation Co-Offloading With Reinforcement Learning in Fog Computing for Industrial Applications

In the past decade, network data communication has experienced a rapid growth, which has led to explosive congestion in heterogeneous networks. Moreover, the emerging industrial applications, such as automatic driving put forward higher requirements on both networks and devices. On the contrary, running computation-intensive industrial applications locally are constrained by the limited resources of devices. Correspondingly, fog computing has recently emerged to reduce the congestion of content-centric networks. It has proven to be a good way in industry and traffic for reducing network delay and processing time. In addition, device-to-device offloading is viewed as a promising paradigm to transmit network data in mobile environment, especially for autodriving vehicles. In this paper, jointly taking both the network traffic and computation workload of industrial traffic into consideration, we explore a fundamental tradeoff between energy consumption and service delay when provisioning mobile services in vehicular networks. In particular, when the available resource in mobile vehicles becomes a bottleneck, we propose a novel model to depict the users' willingness of contributing their resources to the public. We then formulate a cost minimization problem by exploiting the framework of Markov decision progress (MDP) and propose the dynamic reinforcement learning scheduling algorithm and the deep dynamic scheduling algorithm to solve the offloading decision problem. By adopting different mobile trajectory traces, we conduct extensive simulations to evaluate the performance of the proposed algorithms. The results show that our proposed algorithms outperform other benchmark schemes in the mobile edge networks.