Probability Distribution Of Delay Research Articles

Transit fares integrating alternative modes as a delay insurance

Public transport (PT) fare policy remains subject to innovations, not least evident in the Mobility as a Service discussion. Mode integration and related fare strategies can be used to increase the attractiveness of PT by compensating for potential delays. This study proposes “premium fares” as a novel pricing tool that can evaluate and improve the travel time reliability on a multimodal transportation network. The premium fare is higher than the standard fare but allows passengers to use an alternative service free of charge if waiting for the delayed public transport service is anticipated to be longer than a certain qualification threshold. Properties that guarantee monotonicity of the premium fare with respect to distance traveled are developed. The operator aims to find the premium fare price and qualification threshold that can maximize its profit, based on the probability distributions of delay and passengers’ value of time (VOT). We model this optimization problem for a railway line given a limited capacity of alternative mode services, e.g., the number of taxis. A two-stage approach using nonlinear and dynamic programming is developed to obtain the optimal decision variables and associated capacity allocation plan. Our results show that the introduction of the premium fare can benefit both operators and travelers with increased profits and improved travel time reliability. The interplay between fares and the qualification threshold is illustrated using various delay and VOT distributions. To attract enough customers the premium fare has to be set below a specific level dependent on the VOT distribution. Meanwhile, the operator adjusts the qualification threshold to control the cost paid to the alternative service provider.

The influence patterns of carbon flux in different climatic zones in China —Based on the complex network approach

Research on ecosystem carbon flux can provide important methodological and strategic support for climate change mitigation. The existing studies focus on the calculation of carbon flux, ignoring the intertwined effects between regions. The quantification and analysis of the interaction patterns of carbon flux is crucial for understanding the global carbon cycle process, forecasting and coping with climate change. In this study, carbon flux network model sequences are established based on complex network theory using carbon flux data spanning from December 1, 2005, to November 30, 2020. The time delay effect is introduced to accurately quantify the influence patterns of carbon flux within climate zones across China. The findings indicate that the probability distribution function of the link weights during the various seasons of each year exhibits a bimodal distribution with distinct positive and negative components. The delay probability distribution function reveals the significant impact of delay effects, which are especially pronounced and mostly significant long-term lag effects in nodes with negative weights. Further, the results of the interactions of carbon flux among climate zones demonstrate that changes in carbon flux in the plateau and southern temperate regions have either positive or negative impacts on other climate zones. Therefore, controlling carbon flux changes in these climatic zones can effectively optimize the distribution of carbon flux. The modeling framework and results presented in this paper provide useful insights for the regulation and distribution optimization of carbon flux in China.

A novel approach to enhancing the Klobuchar algorithm to mitigate the effect of ionospheric delay errors on static single-frequency receivers

Abstract The demand for real-time high-precision positioning for global navigation satellite system applications is difficult to satisfy. In this regard, a single-frequency receiver is found to play an important role in overcoming this challenge, especially in developing countries where economic factors are a major restriction. Hence, the development of built-in models, such as the Klobuchar model, is an important objective for single-frequency users to mitigate the effect of ionospheric delay errors in real-time applications. Accordingly, this study aims to devise a new approach to enhance the behavior of the Klobuchar model and increase its efficiency in resolving the aforementioned problem. The new approach seeks to enhance the behavior of the Klobuchar model without refining or increasing its coefficients. To eliminate the ionospheric delay disturbance, the proposed methodology applies normalization and filtration processes to the raw ionospheric delay probability distribution estimated by the unified least squares technique. A final assessment of the new method for enhancing the Klobuchar behavior in predicting the precise position of a single-frequency static receiver under different weather conditions around the globe is presented in this paper.

Periodic Transmissions in Random Access Networks: Stressed Period and Delay

Most IoT systems use random access protocols for wireless communication. This paper considers an IoT node that generates periodic traffic to be delivered to a destination over a random access network; each packet of the node is expected to be delivered before its deadline. We say that the node is in a stressed period if, within a time interval, its successive packets miss their deadlines. In many systems, the worst-case performance is significantly affected by stressed periods. Characterizing the stochastic properties of stressed periods is thus of fundamental importance. In this paper, we use a fluid flow model to approximate the evolution of the buffer occupancy (i.e., backlog) at the transmitting node. We derive a relationship between buffer occupancy and delay and formally define a stressed period via a time interval in which the buffer occupancy exceeds a certain threshold. With this model, we analyze the dynamics of the buffer occupancy evolution and obtain the probability distributions of stressed period duration and delay. Real network experiments show that our model can well approximate the distributions of stressed period duration and delay in practical WiFi networks. The theoretical results of this paper can be used to analyze the robustness and worst-case performance of IoT monitoring and control systems built on random access networks.

Transmission system model in the track-vehicle relationship based on Long Term Evolution technology

Because of degradations of the wired infrastructure connecting the element of railroad control systems, and related primarily to their destruction, the use of the radio transmission medium for independent management of rail-road traffic control devices is increasingly being considered. An undoubted problem in implying such solutions is the security of transmission in such systems. It should be noted that security at a certain level of transmission is currently already offered by the radio transmission systems themselves, which has also been used in the GSM-R standard. The creation of a separate dedicated system for railroads involves huge expenditures for the design, testing, certification and, finally, construction and implementation of such technology. Therefore, in the opinion of the author of this dissertation, it is possible to use public open radio networks for the needs of railroads, which significantly reduces costs, since such a system is based on existing infrastructure. It is necessary to develop a way of transmitting information that meets the requirements of secure transmission in the sense of railroad traffic control systems. The task is to develop a general model for open radio transmission in traffic control systems based on the latest public radio standard, which is LTE, 5G or Future Railway Mobile Communications System (FRMCS). The article will present the concept of data transmission in the track vehicle relationship with the use of LTE (Long Term Evolution) technology. It will show the concept of transmission based on the PN-EN50159:2011 standard and the results of the tests conducted during control trips on the railway route. The main element that will be subject to the research will include the transmission safety and its delay. The impact of a type of transmission encryption on its delay will be estimated with the assumed blocks of data sent during the transmission. A probability distribution and density functions of the transmission delay probability distribution with the message sizes in the range from 16B to 10 kB with four ways of the signal encoding according to the PN-EN50159:2011 standard will be also analysed.

Synchronization of Delayed Fuzzy Neural Networks with Probabilistic Communication Delay and Its Application to Image Encryption

This article studies the design of fuzzy synchronization controller of Takagi—Sugeno (T-S) fuzzy neural networks with distributed time-varying delay and probabilistic network communication delay. Compared with the existing model of neural networks with distributed time-varying delay without kernel, a more general model containing a distributed delay kernel is considered. By utilizing the probability distribution of random communication delays, a distributed but deterministic delay model is established. In this model, the delay probability density function is treated as the distributed delay kernel. By developing a new Lyapunov-Krasovskii functional (LKF) related to the distributed delay kernels and using an integral inequality, new sufficient conditions for the existence of a synchronization controller are presented. Finally, a numerical simulation and an application of encrypting the image are carried out to illustrate the effectiveness of the developed strategy.

H∞control for networked semi‐Markovian jump systems with generally incomplete transition probabilities and distributed delays

AbstractThe control problem for networked semi‐Markovian jump systems (S‐MJSs) with generally incomplete time‐varying transition probabilities (GITTPs) and distributed delays is addressed in this article. Firstly, the TPs considered herein may be exactly known, merely known with lower and upper bounds, or unknown, meanwhile the distributed delays own a probability density function as its kernel. Secondly, the closed‐loop networked S‐MJSs is established with GITTPSs and distributed delays. Thirdly, to make full use of the characteristic of delay probability distribution, a generalized discrete‐Bessel summation inequality and a Lyapunov–Krasovskii functional (LKF) are developed by distributed kernel. Then, by applying the Lyapunov method with generalized summation inequality and utilizing an equivalent transformation method to deal with the unknown TPs, several sufficient conditions that guarantee a prescribed performance for networked S‐MJSs are established. Eventually, two simulation examples including a single machine infinite bus power systems are presented to illustrate the effectiveness of the proposed theoretical findings.

Fault Detection Method for Wi-Fi-Based Smart Home Devices

At present, the dynamic nature and unstable network connections in the deployment environments of Wi-Fi-based smart home devices make them susceptible to component damage, crashes, network disconnections, etc. To solve these problems, researchers have used various fault detection methods, such as alarming when monitored fault parameters exceed the preset values, model-based mathematical methods, device signal processing-based methods, and artificial intelligence-based methods. However, these methods require large numbers of fault parameters, the model are complex, and their fault detection accuracy is relatively poor. To more quickly and accurately detect faults in smart home devices and ensure the continuity of people’s daily work and lives, this paper analyzes both the Wi-Fi traffic characteristics of smart home devices and the complexity and difficulty of traditional fault detection methods and proposes a fault detection method based on TDD (Throughput and Delay Distribution). This method obtains throughput and data packet delay distribution by capturing Wi-Fi communication and sending test data. By dividing the throughput into heartbeat data and command information, we can calculate the real-time throughput and further calculate the similarity between the real-time throughput and the throughput in database. Also, the resulting delay distribution is compared with the probability distribution of delay in the database. When the throughput values are sufficiently similar and the delays are all in the normal range, the smart home secure devices are functioning properly. The experimental results show that the proposed TDD method can detect faults in household devices in real time and that it achieves high recall and good detection accuracy in Wi-Fi communication environment.

Timeliness-Aware Incentive Mechanism for Vehicular Crowdsourcing in Smart Cities

Vehicular crowdsourcing is a promising paradigm that takes advantage of powerful onboard capabilities of vehicles to perform various tasks in smart cities. To fulfill this vision, a well-designed incentive mechanism is essential to stimulate the participation of vehicles. In this paper, we propose a timeliness-aware incentive mechanism for vehicular crowdsourcing by taking vehicle’s uncertain travel time into account. In view of the stochastic nature of traffic conditions, we derive a tractable expression for the probability distribution of task delay based on a discrete-time traffic model. By leveraging reverse auction framework, we model the utility of a service requester as a function in terms of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uncertain</i> task delay and incurred payment. To maximize the requester’s utility under a budget constraint, we cast the mechanism design as a non-monotone submodular maximization problem over a knapsack constraint. Based on this formulation, we develop a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</u> ruthful <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</u> udgeted <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u</u> tility <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</u> aximization <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> uction (TBUMA), which is truthful, budget feasible, profitable, individually rational and computationally efficient. Through extensive trace-based simulations, we demonstrate the effectiveness of our proposed incentive mechanism.

A survey of security and smart home automation based on internet of things technology

The &lt;span&gt;internet of things (IoT) refers to the physical tools that are embedded with Internet, software, electronics, sensors and network connectivity. This involves many different systems, for example, healthcare, smart home and so on. The security problem of the Internet of Things and the Smart Home infrastructure is considered, which has become an urgent issue due to the high popularity, low systemic research and the growth of threats to "us" (people seeking comfort) from "them" (surrounding things, which becoming increasingly intelligent, automated). The research is based both on a systemic, infrastructure understanding, and on an architectural, problem-oriented level. Key security risks for the infrastructure of various types-software and technical, technological, socio-psychological and others-were analyzed. The evolutionary problems of the internet of things and factors influencing the vulnerability of infrastructures have been investigated. In addition to traditional network security tasks, specific tasks (direct and reverse, for identification) are highlighted in IoT interactions and environments. This paper provides an overview of the related work in IoT, together with the open challenges and future research directions using Arduino platform (such as "thick server-thin client") to simulation modeling of time delay probability distribution based on identified simple model.&lt;/span&gt;

A robust survival model for emergency medical services in Delhi, India

We propose a Double Standard Robust Survival Model (DSRSM) for efficient utilization of ambulances to maximize the survivability of high priority calls under uncertain travel times. The DSRSM provides dual coverage using primary and secondary ambulances to maximize the expected survival probability. A mixed-integer linear program equivalent of the quadratic DSRSM is proposed and solved using a combination of Variable Neighbourhood Search metaheuristic and Local Branching technique. The model was applied to the real-world conditions of Delhi, India, one of the largest metropolitan areas in the world. First, a survival probability function was estimated for emergency cases using empirical data from Delhi's public emergency medical services. Next, probability distributions of pre-trip delay, travel time to the demand site, and travel time to the nearest hospital were estimated using field data. The DSRSM utilized survival functions and the travel time distributions to locate ambulances optimally to maximize survivability. Results show that using multiple scenarios to capture varying travel times produces a more realistic performance evaluation for survival outcomes than a single scenario with average travel time. The model produces a single set of ambulance locations each for weekdays and weekends, which is more economically feasible than relocating ambulances across scenarios while providing survival outcomes comparable to the relocation approach.

Delay-probability-dependent state estimation for neural networks with hybrid delays

This dissertation studies the delay-probability-dependent H∞ state estimation issue of neural networks (NNs) with hybrid delays. First, more general system model and state estimator are established by considering discrete delay, distributed delay and probability distribution of time delays. Second, a innovative Lyapunov-Krasovskii functional (LKF) containing augmented non-integral and single-integral quadratic terms is put forward, which can inflect internal connections of multiple functional terms. Meanwhile, in order to handle the infinitesimal operators of LKF effectively, generalized free-weighting-matrix integral inequality (GFWMII) is chosen to cooperate with wirtinger-based inequality. As a consequence, less conservative criteria are obtained, which ensure that the considered system is asymptotically mean-square stable with a desired H∞ performance. Finally, two simulated examples are displayed to bring out the advantage of the achieved approach.

A novel particle filtering for nonlinear systems with multi-step randomly delayed measurements

For nonlinear discrete-time systems where measurements can be randomly delayed by multiple sampling periods, measurements are dependent conditioned on the state trajectory, and the dependence becomes more complicated with the increase of step of random delay. A particle filtering for this system is developed, which is novel in that the likelihood is computed allowing multi step of delay and dependence of measurements. Multi step of delay is dealt with through utilizing the formula of total probability skillfully, and dependence is dealt with through estimating the filtering probability distribution of random delay. The novel particle filtering is applied to two examples to validate its effectiveness and superiority.

Reliable sampled‐data control for nonlinear switched systems with packet dropout and multiple sampling periods

This paper addresses the problems of exponential mean‐square stability and reliable sampling data control design for nonlinear switched systems. First, a common model of actuator fault is used to give a description of the nonlinear of controller. The packet loss is assumed to be affected by multiple sampling periods. By input delay method, probability distribution of stochastic delay which takes value in m (m ≥ 2) given intervals is obtained. Second, using average dwell time method, sufficient conditions in the form of linear matrix inequality (LMI) are obtained to ensure the stability of considered switched systems. Moreover, an illustration example is used to demonstrate the validity of the sampling controller designed.

Probabilistic approximation of effective reproduction number of COVID-19 using daily death statistics.

The effective reproduction number (R) which signifies the number of secondary cases infected by one infectious individual, is an important measure of the spread of an infectious disease. Due to the dynamics of COVID-19 where many infected people are not showing symptoms or showing mild symptoms, and where different countries are employing different testing strategies, it is quite difficult to calculate the R, while the pandemic is still widespread. This paper presents a probabilistic methodology to evaluate the effective reproduction number by considering only the daily death statistics of a given country. The methodology utilizes a linearly constrained Quadratic Programming scheme to estimate the daily new infection cases from the daily death statistics, based on the probability distribution of delays associated with symptom onset and to reporting a death. The proposed methodology is validated in-silico by simulating an infectious disease through a Susceptible-Infectious-Recovered (SIR) model. The results suggest that with a reasonable estimate of distribution of delay to death from the onset of symptoms, the model can provide accurate estimates of R. The proposed method is then used to estimate the R values for two countries.

Mean-square consensus for heterogeneous multi-agent systems with probabilistic time delay

This paper studies the delay-dependent consensus problem of heterogeneous multi-agent systems over directed topology. The heterogeneous dynamics consisting of both first-order and second-order agents with random time delay are considered. New distributed control protocols based on the probability distribution of time delay are proposed for the leader-following and leaderless systems. By adopting matrix theory, Lyapunov-Krasovskii function and stochastic analysis, some less conservative conditions for the mean-square consensus are established over directed fixed topology and switching topologies. Moreover, the larger upper bounds of time delay are obtained. Finally, several simulations are presented to illustrate the obtained results.

On stability and stabilization of T-S fuzzy systems with multiple random variables dependent time-varying delay

This paper is concerned with the problems of stability and stabilization for Takagi-Sugeno (T-S) fuzzy systems with multiple random variables dependent time-varying delay. Different from the previous works, we assume that probability distributions of delay in N intervals can be observed in advance. (N-1) Bernoulli distributed random variables are utilized to indicate which interval the time-varying delay falls into at a certain time instant. Then the original T-S fuzzy systems are transformed into a new model of T-S fuzzy systems with multiple random variables dependent time-varying delay, which includes the existed ones as its special cases. Based on the new model, an appropriate Lyapunov-Krasovskii (L-K) functional is constructed. Generalized Finsler’s lemma is introduced to avoid directly dealing with the interrelationship between these correlated random variables, and Reciprocally convex inequality is used to estimate integral terms from the infinitesimal operator of L-K functional. Less conservative stability criteria and stabilization conditions are proposed in the form of linear matrix inequalities (LMIs). Finally, two examples are given to demonstrate the effectiveness of the proposed methods.

NEIGHBOR ROUTING CONTROL USING RELATIVE ROUTING PACKET FLOW ALGORITHM (NRC-RRPFA) TO IMPROVE THE DATA TRANSMISSION ROUTING IN VEHICULAR AD HOC NETWORK

Vehicle ad-hoc networks are highly mobile wireless networks for energy consumption. The Ad-Network in the routing environment become probable in traffic conditions. A VANET provides a vehicle-to-vehicle connection that can be used in communication as in the form of data flow information in energy constraint level. The data transmission of the wireless communication device a mounted on the vehicle network as well in the defined network. The relative transmission algorithm in each time slot is used to predict all users that are predictable to connect to the Routing relay nodes created mitigation. Therefore, try to find the nearest facility here from the specified location and the path between any two points of the service area destination defined factors dominate the average path loss and the propagation speed and time and the probability distribution of VANET vehicle infrastructure communication delays. To propose a Neighbor routing Control using Relative Routing packet Flow algorithm (NRC-RRPFA) to improve the data transmission routing in vehicular ad hoc network. To improve the Data speed, efficiency and determine the path, optimization problem. To address this, delay-tolerant communications also fly around. The protocol allows location based nodes to carry packets back to their anchor location from the point of relay nodes. where they periodically first separated to make easy communication medium. A unique feature of this protocol is that when it is moved away from the origin, the recorded geographic track is the improve the network communication to optimize the return path. the performance improvement will become even more by neighbor routing.

VEHICLE TRAJECTORY BASED CONTROL DELAY ESTIMATION AT INTERSECTIONS USING LOW-FREQUENCY FLOATING CAR SAMPLING DATA

Control delay is an important parameter that is used in the optimization of traffic signal timings and the estimation of the level of service at signalized intersection. However, it is also a parameter that is very difficult to estimate. In recent years, floating car data has emerged as an important data source for traffic state monitoring as a result of high accuracy, wide coverage and availability regardless of meteorological conditions, but has done little for control delay estimation. This article proposes a vehicle trajectory based control delay estimation method using low-frequency floating car data. Considering the sparseness and randomness of low-frequency floating car data, we use historical data to capture the deceleration and acceleration patterns. Combined with the low-frequency samples, the spatial and temporal ranges where a vehicle starts to decelerate and stop accelerating are calculated. These are used together with the control delay probability distribution function obtained based on the geometric probability model, to calculate the expected value of the control delay for each vehicle. The proposed method and a reference method are compared with the truth. The results show that the proposed method has a root mean square error of 11.8 s compared to 13.7 s for the reference method for the peak period. The corresponding values for the off-peak period are 9.3 s and 12.5 s. In addition to better accuracy, the mean and standard deviation statistics show that the proposed method outperforms the reference method and is therefore, more reliable. This successful estimation of control delay from sparse data paves the way for a more widespread use of floating car data for monitoring the state of intersections in road networks.

Event-Triggered $H_{\infty }$ Control of Networked Control Systems With Distributed Transmission Delay

This article contributes to design an event-triggered $H_{\infty }$ controller for networked control systems with network channel delay. First, the network channel delay is modeled as a distributed delay with a probability density function as its kernel. Then, the closed-loop event-triggered control system is established as a distributed delay system. To make full use of the delay probability distribution, the Lyapunov–Krasovskii functional is constructed with the distributed kernel. By applying the Lyapunov method, sufficient conditions for ensuring the stability of the closed-loop system with prescribed $H_{\infty }$ performance are formulated in linear matrix inequalities. A numerical example shows that the proposed method is less conservative than some existing results considering delay distribution.