Delay Probability Research Articles

A Scalable and Energy-Efficient MAC Protocol for Linear Sensor Networks

Linear topologies arise naturally in the context of Internet-of-Things (IoT) applications for smart cities, where the infrastructure itself commonly has a linear or semi-linear structure. This is the case of buildings, public transportation systems, road infrastructure, and utility distribution networks. Given the prevalence of this type of topologies, several Medium Access Control (MAC) protocols have been designed to take advantage of their particular properties. Unfortunately, most of them do not scale well as the node density and the distance in hops to the sink increases. The result is that packets generated many hops away from the sink tend to experience unacceptable high end-to-end delay and low delivery probabilities. This paper introduces HP-MAC, a synchronized duty-cycled MAC protocol for Linear Sensor Networks (LSNs) that assigns transmission priorities to nodes to avoid collisions, through the implementation of distributed elections based on hash functions. HP-MAC also implements a packet queuing scheme that acts as a mechanism to control the amount of network resources allocated to data flows generated at different distances to the sink. This way, packets can reach their destination with loss probability and end-to-end delay that do not depend on their distance to the sink. We use a Discrete-Time Markov Chain (DTMC) to model the performance of the proposed protocol. Numerical solutions of this model show that HP-MAC outperforms state-of-the-art representatives in terms of throughput, end-to-end delay, power consumption, and packet loss probability. These results are validated through extensive discrete-event simulations.

SLA-Aware Protocol Design for Energy-Efficient OLT Transmitter in TWDM-EPON

Environmental and economic concerns promote research on designing energy-efficient Time and Wavelength Division Multiplexed Ethernet Passive Optical Network (TWDM-EPON). TWDM-EPON allows achieving energy efficiency at the Optical Line Terminal (OLT) along with Optical Network Units (ONUs). All existing protocols for energy efficient TWDM-EPON have primarily been designed for saving energy at the OLT receivers. In this paper, we propose a method for saving energy at the OLT transmitter in TWDM-EPON while satisfying the service level agreements (SLAs) of corresponding downstream traffic. This includes a proposal for a heuristic algorithm that exploits some specific features of downstream scheduling in TWDM-EPON. Through extensive simulations, we demonstrate that the proposed method provides a significant improvement in energy efficiency in comparison with existing DBA protocols (up to ~ 45%). Moreover, we analyse a theoretical lower bound of the probability of delay bound violation (SLA parameter) and through simulations, we have demonstrated that delay bound violation probability of our proposed protocol is close to this lower bound.

Optimal control to improve reliability of demand responsive transport priority at signalized intersections considering the stochastic process

Bus priority has been proposed for many years to improve traffic congestion, energy conservation, and emissions reduction. Transit signal priority is a main measure of executing bus priority. Moreover, because the time that buses arrive at an intersection should be predicted quite precisely to make a signal control scheme in advance, bus lane is an important segment in transit priority at signalized intersections to avoid disturbances from other vehicles on the road. However, in practice, the bus route of demand responsive transport is widespread, which is not inappropriate to set bus lane for each bus route because of low frequency. Therefore, in this paper, a robust optimization control method is proposed to solve the bus priority problem at signalized intersections without exclusive bus lanes and improve reliability of demand responsive transport priority at signalized intersections. First, the bus delay probability density function is formulated to describe the interaction mechanism about the bus and other vehicles at the signalized intersection. Then, the accurate bus delay expectation and variance calculation mathematical models are formulated based on the bus delay probability density function. Furthermore, a robust optimal signal control method is proposed followed by the discussions of boundary conditions. Finally, simulation test and case study were conducted to illustrate the performance of the robust optimization control method proposed in this paper. The results show that the proposed control method could significantly improve bus delay compared to considering the bus operation process as stable.

Behavioral Economics in Plea-Bargain Decision-Making: Beyond the Shadow-of-Trial Model

AbstractLegal scholars have long assumed that plea bargains are contracts negotiated between rational actors who adhere to the dictates of the normative shadow-of-trial model. The two key features that rational actors presumably haggle over in the shadow of trial are the criminal charge (and associated sentence) and the probability of trial conviction. The behavioral economics theory ofdiscounting, however, offers a theoretical foundation for testing the shadow-of-trial model. This article summarizes findings from experimental discounting studies in behavioral economics and psychological science – showing that these paradigms can be successfully applied to the plea-bargaining decision context wherein the likelihood of trial is uncertain and delayed, and the plea bargain is relatively certain and immediate. We suggest that the implications of applying discounting to plea bargaining are three-fold: (1) empirical evidence suggests that the shadow-of-trial model is too narrow; (2) the discounting of non-monetary losses may involve slightly different psychological processes than contexts involving monetary outcomes; and (3) probability of conviction and delay until trial constitute situational features that elicit guilty pleas despite a defendant’s factual innocence.

Resource Allocation for Deterministic Delay in Wireless Control Networks

Meeting the high requirements of deterministic transmission in wireless control systems is very challenging, but also very important. In order to improve the determinacy of wireless short packet transmission, we optimize the communication resource required for multi-sensors in the wireless control system in this letter. In particular, we derive the probability of the transmission delay falling within a specified time window to characterize the deterministic transmission and then investigate the relationship between the delay determinacy and the wireless resources by taking the system control performance into account. We then optimize the transmission power spectral density of sensors and the transmission bandwidth allocation to maximize the delay determinacy, and obtain its suboptimal solution by numerical derivation. Finally, numerical results show that the proposed allocation achieves significantly greater the delay determinacy than that of other allocation schemes.

Rail transport network reliability with train arrival delay: A reference indicator for a travel agency in tour planning

As a tour agency plans a tour schedule with rail transport, it is necessary to evaluate the capacity of the rail transport network to determine the number of passengers in the tour group that can be served. This study develops a stochastic rail transport network (SRTN) according to the train timetable and tour schedule, such that each available train’s loading capacity (that is, the number of cars) may be fully and partially reserved for other requests. Under the consideration of a tour group including q passengers, the network reliability accordingly means the probability that the q-passenger group can successfully depart from a source station for a sink station via the SRTN. In addition, the passengers might not punctually change trains at a transfer station owing to train arrival delay. Because the travel agency cannot control the situation of train arrival delay while planning a tour schedule, the uncertainty of train arrival delay results in an acceptable delay probability that the passengers traveling by non-direct trains can successfully change trains and this must be considered for the network reliability evaluation. An algorithm integrating the minimal paths and the modified Recursive Sum of Disjoint Product are integrated to solve the addressed problem. A real case of a rail transport network in Taiwan is adopted to demonstrate the applicability of the proposed approach. The travel agency can view the network reliability as a reference indicator to evaluate the number of passengers in the group that can be served.

Language function following preterm birth: prediction using machine learning

BackgroundPreterm birth can lead to impaired language development. This study aimed to predict language outcomes at 2 years corrected gestational age (CGA) for children born preterm.MethodsWe analysed data from 89 preterm neonates (median GA 29 weeks) who underwent diffusion MRI (dMRI) at term-equivalent age and language assessment at 2 years CGA using the Bayley-III. Feature selection and a random forests classifier were used to differentiate typical versus delayed (Bayley-III language composite score <85) language development.ResultsThe model achieved balanced accuracy: 91%, sensitivity: 86%, and specificity: 96%. The probability of language delay at 2 years CGA is increased with: increasing values of peak width of skeletonized fractional anisotropy (PSFA), radial diffusivity (PSRD), and axial diffusivity (PSAD) derived from dMRI; among twins; and after an incomplete course of, or no exposure to, antenatal corticosteroids. Female sex and breastfeeding during the neonatal period reduced the risk of language delay.ConclusionsThe combination of perinatal clinical information and MRI features leads to accurate prediction of preterm infants who are likely to develop language deficits in early childhood. This model could potentially enable stratification of preterm children at risk of language dysfunction who may benefit from targeted early interventions.ImpactA combination of clinical perinatal factors and neonatal DTI measures of white matter microstructure leads to accurate prediction of language outcome at 2 years corrected gestational age following preterm birth.A model that comprises clinical and MRI features that has potential to be scalable across centres. It offers a basis for enhancing the power and generalizability of diagnostic and prognostic studies of neurodevelopmental disorders associated with language impairment.Early identification of infants who are at risk of language delay, facilitating targeted early interventions and support services, which could improve the quality of life for children born preterm.

Dual cruise-control limit cellular automaton model with cell length refinement in the framework of the three-phase traffic theory

In this study, a new dual cruise-control limit cellular automaton model with a refined cell length, where vehicles moving with high or low velocity are not affected by noise and the slow-to-start rule is incorporated for standing vehicles with just one free cell in front, is established. In the new refined model, one sets each cell and each vehicle to 2.5 m and 7.5 m in length, respectively. Computer simulation results have shown that the new refined model can successfully reproduce the empirical features of three-phase traffic flow theory including 2Z-characteristic for phase transitions, whether vehicles are uniformly distributed, densely distributed or randomly distributed on the road first. Furthermore, one also finds that the slope tgα of the linear synchronous flow varies with the delay probability p and the maximum value of low velocity v LM in the synchronized flow region. Under homogeneous distribution, the linear synchronous flow with double slopes (tgα, tgβ) is first discovered for the case of 3 ⩽ v LM ⩽ 5. Under random distribution, all possible co-existence phases of three-phase traffic flow are presented in the fundamental diagram.

Asymptotic Analysis for Systems with Deferred Abandonment

This short paper concerns the analysis of the M/M/k queueing system with customer abandonment. In this system, service managers provide a finite buffer space, which is a waiting area that prevents customers from abandoning the system. Abandonment of the system can occur from reneging (exiting from the queue while waiting), and/or balking (leaving the system without waiting). We derive an analytical expression to represent the impact of the buffer space capacity on the delay probability and the abandonment probability for a system with deferred abandonment. The result indicates the provision of the buffer space in a large system could only increase the delay probability while the abandonment probability remains unchanged. Despite the benevolent intentions of service managers, providing a buffer space may exacerbate the performance of larger systems.

Statistical analysis of pulsed discharges in dielectric liquid: effects of voltage amplitude, pulse width, electrode configuration, and liquid composition

Discharge in dielectric liquid is an active field of research involved in many technological applications. Such multiple discharges are often applied during an experiment and that the electrode and liquid are altered by these stochastically behaving discharges. Therefore, it is important to address the variation of discharge characteristics as a function of discharge occurrence. In this study we analyze the electrical characteristics of multiple discharges run at low repetition rate (5 Hz) in a liquid of known initial composition using an electrode with a specific geometry. The discharges are run continuously until they fail to occur due to the increase in gap distance, and the recorded voltage and current waveforms are processed using an algorithm in order to determine the probability of discharge occurrence, breakdown voltage, discharge current, and discharge delay. The injected charge and energy of each discharge are also calculated. Furthermore, the effects of applied voltage, pulse width, electrode configuration, and liquid composition on the characteristics of the discharge are investigated. The obtained results demonstrate that the highest and lowest numbers of occurred discharges are achieved using the plate-to-plate and pin-to-pin configurations, respectively. Moreover, the composition of the liquid has an appreciable effect on the discharge current, as well as on electrode erosion. For discharges in water and cyclohexane, we measured the lowest and highest current, respectively. As for the erosion rate, it was comparable for the three liquid hydrocarbons but relatively smaller than that in water. The plot of breakdown voltage as a function of discharge current varies depending on the discharge parameters and displays regions that may be linked to the ignition mechanisms. The original data reported herein is of great significance for various applications that utilize repetitive discharges in liquid.

Passenger and energy-saving oriented train timetable and stop plan synchronization optimization model

The stability of train timetables plays an important role in ensuring the safety and punctuality of train operations. Meanwhile, a reasonable stop plan has a significant effect on passenger and energy consumption. Existing research has focused exclusively on adjusting timetables after delays occur, or on adjusting them without considering stop plans. By contrast, in this study, we developed a train timetable and stop plan synchronization optimization model that is passenger and energy-saving oriented. The following objectives were considered for minimization: probability of train delays, energy consumption, and travel time of the trains. A parallelism selection genetic algorithm was designed to solve the model. The section of high-speed railway from Nanjing South to Shanghai Hongqiao was utilized as a case study to evaluate the proposed model. The results showed that, while meeting passenger demand, the proposed optimization approach increased the punctuality rate by 12.55% and decreased the energy consumption by 8.16%.

Protocol Design for Energy Efficient OLT in TWDM-EPON Supporting Diverse Delay Bounds

Owing to the Economic and Environmental concerns, energy-efficient Time and Wavelength Division Multiplexed Ethernet Passive Optical Network (TWDM-EPON) has already emerged as an important research area. TWDM-EPON allows saving energy at the Optical Line Terminal (OLT) along with Optical Network Units (ONUs), where existing protocols to save energy at ONUs of TDM-EPON can be employed. Moreover, a TWDM-EPON network is bound to support traffic with diverse delay bound to fulfil the recent need for supporting a variety of applications. Therefore, in this paper, for the first time to the best of our knowledge, we propose an online scheduling protocol to achieve energy efficiency at the OLT of a TWDM-EPON network that can support traffic with diverse delay bounds. Simulation results depict that the protocol provides a significant reduction in the probability of delay bound violation while maintaining a similar energy efficiency figure when compared with the most energy-efficient existing online protocol for a single traffic type. The performance of the protocol improves when the network is scaled up or the dimension of the network increases, and hence, suitable for both rural and urban scenarios.

Robust Shipping Date Predictions in Manufacturing using High-level Petri Nets

ABSTRACT Machine failures, unavailability of the workforce, and missing raw materials are typical, frequent faults in complex manufacturing systems and cause unstable processes. Plans and schedules become volatile, predictability of shipping dates suffers and delayed delivery appears. However, for a customer reliable shipping can often be of higher benefit than promised short-term shipping with significant probability of delay and error. Inserting buffer times stabilises schedules by reducing the potential impact of disruptions. Therefore, computing appropriately sized buffer times for individual jobs in a flexible job shop manufacturing with setup times is a promising yet difficult mission for production research. In this paper, we discuss methods for buffer time calculation that are based on data collected directly from the underlying production processes. We show how a high-level Petri net model can be used to simulate stochastic variations of the manufacturing process. Using this simulation model, we estimate the effect of buffer times and produce predictions for the expected shipping dates together with probabilities for how likely the predicted dates will actually be met.

Real-Time nonlinear predictive controller design for drive-by-wire vehicle lateral stability with dynamic boundary conditions

Due to flexible drive-by-wire technology, vehicle stability control can improve handling and lateral stability under extreme conditions. However, this technology can also increase the probability of random transmission delay. This paper proposes a nonlinear model predictive control (NMPC) strategy to improve vehicle stability and compensate for the random time delay. First, by combining the nonlinear dynamic characteristics and driver behavior, we obtain a stable region of the yaw rate and the sideslip angle under complex driving conditions. Second, an NMPC controller is designed to track the reference values in the identified stable region to improve the handling and lateral stability. Finally, the actuator receives the optimized control sequence and compensates for the random time delay of the transmission channel. CarSim/Simulink simulation and hardware-in-the-loop experiment results show that the proposed controller with dynamic boundary conditions can better track the expected value of the yaw rate and suppress the sideslip angle under low adhesion road conditions.

Potentially real and hypothetical food and monetary outcomes in delay and probability discounting are similar in a Czech sample

ABSTRACT Most of the studies that compare potentially real (PR) to hypothetical outcomes with delay discounting (DD) and probability discounting (PD) compare monetary outcomes in American college samples and shows that hypothetical and PR monetary outcomes are discounted at similar rates. Fewer, if any, studies have examined discounting for PR vs hypothetical outcomes in non-American samples with outcomes other than money. Using a choice-questionnaire format and a within-subjects design, the relation between PR and hypothetical outcomes was examined in a Czech Republic sample. Importantly, food-related and monetary outcomes for both delay and probability discounting were examined. Sixty participants were recruited from the greater Prague area and completed four discounting tasks: the Food Choice Questionnaire, Monetary Choice Questionnaire, Probability Monetary Choice Questionnaire, and Probability Food Questionnaire in randomized order for both PR and hypothetical outcomes. Each of these measures has three magnitudes of outcomes embedded in the choices. For food-related outcomes, PR and hypothetical food outcomes were discounted similarly and significantly correlated across two of the three magnitudes of the Food Choice Questionnaire and across all three magnitudes of the Probability Food Choice Questionnaire. For monetary outcomes, PR outcomes and hypothetical outcomes were discounted similarly and were significantly correlated across all magnitudes of the Monetary Choice Questionnaire and Probability Monetary Choice Questionnaire. Magnitude effects were found across all four measures. These findings suggest that hypothetical and PR food and money outcomes are discounted similarly for both DD and PD and extends the discounting literature on similarity between real and hypothetical discounting to food-related outcomes to European community samples and discounting choice questionnaires.

Uncertain accessibility estimation method for offshore wind farm based on multi‐step probabilistic wave forecasting

Accessibility estimation is significant to the offshore wind farm operation and maintenance (O&M) due to the extremely limited weather window and its sensitive effects on O&M tasks. Wave forecasting can be one solution to help maintenance decision-making. However, the uncertain and dynamic properties of wave forecasts are seldom considered in the accessibility estimation process. This paper presents an uncertain accessibility estimation method based on a multi-step probabilistic wave height forecasting (MPWHF) model and Monte Carlo simulation. First, an MPWHF model is proposed using the wavelet decomposition and the sequence to sequence (Seq2Seq) network with quantile outputs. Second, the O&M missions are randomly given a start time and simulated in the O&M flow chart by the Monte Carlo method. Finally, several access indexes, including accessibility probability, delay time, and delay probability, are evaluated based on the simulation results. Verification of the proposed MPWHF model and uncertain accessibility estimation is based on 7-year observation data of a buoy station. The results show that the MPWHF model outperforms other counterparts and the probability of offshore accessibility is nonlinearly dependent on the weather limits and the O&M required time.

A Two Stage Queuing System with delay time

This paper discusses about a two stage queueing system with shared server and the delay time between two end to end responses. This system is examined as almost the process of quasi-birth and death and the necessary and sufficient conditions for system stability are discussed. The ErntPS algorithm for VoIP Services has recently been investigated for average delay in queueing and it has been proven that ErtPS can allow more users than UGS. However, we need the distribution of probabilities of queue delays in order to include information necessary for QoS rather than the average queue delays. Some fascinating performance measures are achieved by matrix geometric method. The server wishes to determine the optimal point of differentiation and its optimal buffer size. Finally we find the delay time differentiation and the specific probabilistic system descriptors.

Adaptive Node Clustering for Underwater Sensor Networks.

Monitoring of an underwater environment and communication is essential for many applications, such as sea habitat monitoring, offshore investigation and mineral exploration, but due to underwater current, low bandwidth, high water pressure, propagation delay and error probability, underwater communication is challenging. In this paper, we proposed a sensor node clustering technique for UWSNs named as adaptive node clustering technique (ANC-UWSNs). It uses a dragonfly optimization (DFO) algorithm for selecting ideal measure of clusters needed for routing. The DFO algorithm is inspired by the swarming behavior of dragons. The proposed methodology correlates with other algorithms, for example the ant colony optimizer (ACO), comprehensive learning particle swarm optimizer (CLPSO), gray wolf optimizer (GWO) and moth flame optimizer (MFO). Grid size, transmission range and nodes density are used in a performance matrix, which varies during simulation. Results show that DFO outperform the other algorithms. It produces a higher optimized number of clusters as compared to other algorithms and hence optimizes overall routing and increases the life span of a network.

Analysis of default risk in credit card use / Análise do risco de inadimplência na utilização de cartões de crédito

This paper analyzes the risk of default in the use of credit cards generating probabilities of delay in payment with different variables such as age, gender, credit limit and annual income. The behavior of debtors who use credit cards is studied identifying changes in states of delay of risk levels. A multi-state model of Markov was used to perform the analysis. The study was applied to credit card usage records of individuals in 121 commercial and financial institutions. This research identifies the patterns of use by credit card customers and provides valuable inputs to help financial institutions understand the phenomenon of default risk.

Improve Performance by a Fuzzy-Based Dynamic Replication Algorithm in Grid, Cloud, and Fog

The efficiency of data-intensive applications in distributed environments such as Cloud, Fog, and Grid is directly related to data access delay. Delays caused by queue workload and delays caused by failure can decrease data access efficiency. Data replication is a critical technique in reducing access latency. In this paper, a fuzzy-based replication algorithm is proposed, which avoids the mentioned imposed delays by considering a comprehensive set of significant parameters to improve performance. The proposed algorithm selects the appropriate replica using a hierarchical method, taking into account the transmission cost, queue delay, and failure probability. The algorithm determines the best place for replication using a fuzzy inference system considering the queue workload, number of accesses in the future, last access time, and communication capacity. It uses the Simple Exponential Smoothing method to predict future file popularity. The OptorSim simulator evaluates the proposed algorithm in different access patterns. The results show that the algorithm improves performance in terms of the number of replications, the percentage of storage filled, and the mean job execution time. The proposed algorithm has the highest efficiency in random access patterns, especially random Zipf access patterns. It also has good performance when the number of jobs and file size are increased.