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Flight delay identification is an important way to coordinate the operation time of airport ground service providers and improve the efficiency of airport operations. By analyzing the flight turnaround operation process, considering the randomness and synchronization of the turnaround process, and using Colored Petri Nets and Python (4.0.1), we explore the correlation between various links in the flight turnaround process and the take-off delay at the next station. This paper is committed to improving the service performance of airports and airlines, dynamically predicting flight delays, and providing guidance for avoiding excessive time in the actual operation of bad combinations. The results show that there are six kinds of bad combinations in the departure slip-out link, which is the most likely to affect the transit time. The maximum lifting degree in the bad combination is 2.043, and the maximum average delay time in the bad combination is 22.5 min. When the combination of passenger boarding and departure slip-out time is too long, it has a great positive correlation with delay. When the other links are in a state of being able to pass the station on time, the departure time and baggage loading and unloading are the two links that most affect the flight delay value. [ABSTRACT FROM AUTHOR]

As well-known, virtual reality (VR) video covers the entire 360° scene, while the best visual angle of the human eyes is about 120°, and the Field of View (FoV) supported by VR terminal is only 90 ° ~ 110°. Therefore, the viewpoint-aware adaptive streaming methods have attracted more and more attention in the research of for VR video transmission over the Internet. In this context, to further improve the overall QoE of all users, this paper first design a reasonable, effective and complete system framework for tile-based 360° VR video to achieve the centralized optimization approach on viewpoint-aware adaptive streaming over future Mobile Edge Computing (MEC) assisted 5G heterogeneous networks. And then, based on the video quality, quality fluctuation as well as the error characteristics of the predicted viewpoints, a novel priority-based weighted QoE assessment method is presented to measure the contribution of each tile on the user's QoE. Besides, the end-to-end delay of each tile is also estimated under the guidance of the optimized MEC-server assignment. On these bases, we finally formulate a novel adaptive optimization problem with the aim of maximizing the QoE of the tiles that delivers to all users within the delay constraint and solve it by developing a low-complexity Lagrange Relaxation (LR) based heuristic algorithm. The simulation results show that our algorithm outperforms the others in the overall QoE of all users, i.e., increasing video quality by at least 3.888dB and decreasing quality fluctuation by at least 1.7477 dB under the premise of guaranteeing smooth playback. [ABSTRACT FROM AUTHOR]

Flight delay identification is an important way to coordinate the operation time of airport ground service providers and improve the efficiency of airport operations. By analyzing the flight turnaround operation process, considering the randomness and synchronization of the turnaround process, and using Colored Petri Nets and Python (4.0.1), we explore the correlation between various links in the flight turnaround process and the take-off delay at the next station. This paper is committed to improving the service performance of airports and airlines, dynamically predicting flight delays, and providing guidance for avoiding excessive time in the actual operation of bad combinations. The results show that there are six kinds of bad combinations in the departure slip-out link, which is the most likely to affect the transit time. The maximum lifting degree in the bad combination is 2.043, and the maximum average delay time in the bad combination is 22.5 min. When the combination of passenger boarding and departure slip-out time is too long, it has a great positive correlation with delay. When the other links are in a state of being able to pass the station on time, the departure time and baggage loading and unloading are the two links that most affect the flight delay value.

Wireless Sensor Networks (WSNs) have a rapidly increasing number of applications due to the development of long-range low-powered wireless devices. Node decoupling for (NoRD) efficient power-supplying of nodes offers a evade network to avoid sleepy nodes (Chen and Pinkston in NoRD: Node-node decoupling for effective power-gating of on-chip nodes, in Intl, Symp, On Microarchitecture (MICRO), 2012). Though, it obtains a huge latency as well as restricted scalability. In addition, it enhances energy utilization. To defeat this problem, Energy Efficient and Delay Aware Optimization Reverse Routing Strategy (EEOS) is proposed for forecasting link quality in WSN. The main objective of this research is to design a Multi-hop Reverse Routing Technique in WSN. The reverse routing technique avoids the amount of retransmission. Forecasting link quality is used to measure the link quality by Estimating Communication Count (ECC), energy, and delay. This technique enhances routing, link stability, and energy efficiency and minimizes network congestion. It supports Quality of Service (QoS) necessities of energy control, traffic arrangement, and route allotment. In this scheme, the Signal-to-Interference and Noise Ratio (SINR) assists in measuring the quality of a wireless connection. In addition, the route link score is used to form the route from sender to receiver. The reverse routing also provides an efficient route. Simulation results prove that the EEOS minimizes both the delay and the energy utilization and increases the network throughput compared to the baseline protocol. [ABSTRACT FROM AUTHOR]

The decision to use static, dynamic, additive, or subtractive delay analysis is critical because making the wrong decision may have serious undesired consequences. As a response to this problem, a delay analysis selection (DAS) model was developed that identifies the most appropriate type of delay analysis for a target case. The model constitutes the first step of a two-step process to perform delay analysis. It involves a case-base that is populated by 3,776 possible cases developed by using combinations of 26 attributes that were found in the literature to influence the selection of the most appropriate type of delay analysis. The 26 attributes were organized in 7 categories. The weights of the attributes and the categories were calculated using the entropy method. The DAS model finds the best match between a target case and one of the cases stored in the case-base by performing weighted case similarity assessment. The type of delay analysis that is indicated in the matched case is adopted for use in the target case. This study is the first attempt to automate the selection of the most appropriate type of delay analysis. Once the most suitable type of delay analysis is identified in the first step, it is immediately performed in the second step. [ABSTRACT FROM AUTHOR]

Being one of the most common dispute causes in construction projects, the delay is extremely important to be resolved promptly. To resolve delay-related disputes, delay analysis that is conducted to determine the amount of the delay that has occurred on a project together with its contractual responsible party is of paramount importance. Notwithstanding the numerous enhancements in the delay analysis domain, delay resolution remains one of the most challenging issues in construction projects due to various drawbacks of existing delay analysis methods. To minimize the delay-related issues in construction projects, this study, therefore, aims to develop a new delay analysis method that overcomes the drawbacks of existing delay analysis methods. The drawbacks of current methods were initially determined via an in-depth literature review and a focus group discussion (FGD) session conducted with seven experts experienced in the delay analysis domain. This step revealed that most of these methods ignore some critical features, such as consideration of critical path (CP), actual progress data, and fluctuations on CP. Moreover, most of the methods lack detailed numerical procedures and consider only the owner's delay. Furthermore, some types of activity relationships, actual improvements, and further delays made by contractors on the planned excusable compensable delay (ECD) and excusable noncompensable delay (END) are usually ignored by these methods. Then, a new delay analysis method, namely modified schedule versus modified updated schedule (MSvsMUS), was developed. To overcome the detected drawbacks, the proposed method is designed and developed in a way that can consider various activity relationships among the critical activities, include detailed numerical equations, and compute the variations between the modified schedule (MS) including planned fragnet/s and modified updated schedule (MUS) including actualized fragnet/s periodically. This was then followed by a case study conducted to demonstrate the method's mechanism. Finally, the developed method was evaluated and validated by conducting a second FDG session and a hypothetical case study. Results demonstrated that MSvsMUS has considerable potential to resolve delays and delay-related disputes escalated in construction projects. [ABSTRACT FROM AUTHOR]

This paper presents the design of Networked Control Systems (NCS) for pipeline systems. NCS plays an important role in controlling and monitoring large-scale systems such as pipeline systems. To implement the NCS, one must derive the pipe model and consider the network communication constraints. Here, the pipeline model is divided into two sections for simplicity. For example, in a long pipeline system, one can use a higher number of sections in order to give a better result for the analysis. Then let's consider a networked-induced delay as the network communication constraint. The discretize pipe dynamics model is derived to support the NCS scheme in the pipeline system. The stability analysis of the proposed NCS is derived by taking into account the small and the large networked-induced delay. Then the optimal LQR control is designed for both stabilizing and tracking. The stability region of the pipeline system in the NCS scheme with networked-induced delay is derived and depicted to provide stability information. The design of the proposed controller under network constraint (networked-induced delay) must consider the stability plot that is divided into the stable region and unstable region. In this research, let's assume that it is possible to measure the exact time delay and then consider the allowable sampling time for the controller. The proposed controller is designed by considering the NCS scheme with time delay both for regulator and tracking problems. By using the proposed controller, the pipeline system can be controlled in the presence of network-induced delay, which commonly occurs in a distributed system. The simulation verifies the stability analysis of the proposed optimal control for the pipeline systems with the NCS scheme under networked induced delay. [ABSTRACT FROM AUTHOR]

Most construction projects are delayed, and many are subject to claims or disputes. Therefore, delay analysis is a critical component of any construction project to determine who is responsible for delays. This research examines four different techniques for estimating delay impacts using the impacted as-planned (IAP) method. A sample network was introduced as an example to discuss several concerns. The advantages and limitations of each approach were identified, and recommendations were given for each approach. When inserting an activity or activities representing delay events in IAP, it is necessary to use both constraints and logical relations among delay events, their logical predecessors, and successors. Constraints representing the actual date of delay events are the simplest and easiest. However, constraints should not be used in "single insertion" and "inserting only owner- or contractor-caused delay" approach. In addition, in the case of using constraints, it is critical to ensure that the impact of delay events is less than the duration of those delay events. Constraints should be avoided in this scenario, and delay events should be logically connected to their logical predecessors and successors without constraints. This study also identified through an example that inserting delay events only by logic can cause wrong analysis results. The results of this study will be helpful for delay analysts in identifying what kinds of problems occur in IAP methods and how to prevent those problems. [ABSTRACT FROM AUTHOR]

Vehicular communications is expected to be one of the key applications for cellular networks during the following decades. Key international organizations have already described in detail a number of related use cases, along with their requirements. This article provides a comprehensive analysis of these use cases and a harmonized view of the requirements for the latest and most advanced autonomous driving applications. It also investigates the extent of support that 4G and 5G networks can offer to these use cases in terms of delay and spectrum needs. The paper identifies open issues and discusses trends and potential solutions.

Super passive optical network (Super-PON) is a next-generation Ethernet PON (NG-EPON) candidate that is envisaged to provide high data rate and low latency. For NG-EPON, there are two types of algorithms that manages bandwidth and wavelength scheduling, namely offline and online. The latter method is more scalable and efficient than the former method. Several online algorithms exist that propose schemes to manage wavelength and bandwidth scheduling. However, these algorithms lack in efficient wavelength utilization and switching. In this work, we propose a novel online bin-packing based dynamic bandwidth and wavelength allocation (DBWA) algorithm for Super-PON, namely updated best fit bin-packing (UBF-BP). This algorithm limits the wavelength switching per cycle, and uses the modified version of best fit bin-packing (BF-BP) technique for optimal wavelength allocation. Simulation results show that the proposed DBWA algorithm has a low complexity and overcomes the inefficiencies of wavelength utilization and switching. This results in lower network delay and higher channel utilization than the state-of-the-art DBWA. Furthermore, to verify the correctness of the proposed algorithm, we propose an analytical model and validate these simulation results with the analytical results.

Federated learning (FL) is a collaborative machine learning paradigm, which enables deep learning model training over a large volume of decentralized data residing in mobile devices without accessing clients’ private data. Driven by the ever increasing demand for model training of mobile applications or devices, a vast majority of FL tasks are implemented over wireless fading channels. Due to the time-varying nature of wireless channels, however, random delay occurs in both the uplink and downlink transmissions of FL. How to analyze the overall time consumption of a wireless FL task, or more specifically, a FL’s delay distribution, becomes a challenging but important open problem, especially for delay-sensitive model training. In this paper, we present a unified framework to calculate the approximate delay distributions of FL over arbitrary fading channels. Specifically, saddle point approximation, extreme value theory (EVT), and large deviation theory (LDT) are jointly exploited to find the approximate delay distribution along with its tail distribution, which characterizes the quality-of-service of a wireless FL system. Simulation results will demonstrate that our approximation method achieves a small approximation error, which vanishes with the increase of training accuracy. [ABSTRACT FROM AUTHOR]

When applying machine learning techniques to the Internet of things, aggregating massive amount of data seriously reduce the system efficiency. To tackle this challenge, a distributed learning framework called federated learning has been proposed. Due to the parallel training structure, the performance of federated learning suffers from the straggler effect. In this paper, to mitigate the straggler effect, we propose a novel learning scheme, edge-assisted federated learning (EAFL), which utilizes edge computing to reduce the computational burdens for stragglers in federated learning. It enables stragglers to offload partial computation to the edge server, and leverages the server's idle computing power to assist clients in model training. The offloading data size is optimized to minimize the learning delay of the system. Based on the optimized data size, a threshold-based offloading strategy for EAFL is proposed. Moreover, we extend EAFL to a dynamic scenario where clients may be offline after several update rounds. By grouping clients into different sets, we formulate the new EAFL delay optimization problem and derive the corresponding offloading strategy for the dynamic scenario. Simulation results are presented to show that EAFL has lower system delay than the original federated learning scheme. [ABSTRACT FROM AUTHOR]

Effective data interoperability and schedule analysis play a significant role in improving the management of prefabricated buildings. However, there is a lack of efficient strategies and comprehensive approaches for data interoperability and data-based automated schedule analysis. This paper intends to promote prefabricated buildings' management by solving these two problems via developing an IFC-based framework consisting of three parts. Firstly, this framework proposed a mechanism to establish an IFC-based 4D construction management information model of prefabricated buildings. Furthermore, a non-relational database—graph database—is introduced to twin this model into a task-centered network to realize the interoperation of construction information among different participants. Finally, graph database-based strategies to update data, automatically analyze construction schedules and visualize the 4D construction management information model are described. The proposed framework is validated in a prefabricated engineering case. In this case, an IFC-based and graph database-based 4D construction management information model is established through IFC standard's extension. The graph database-based analysis of the model automatically recognizes the engineering case's critical path information, delay analysis information, and schedule network analysis information. It is illustrated that this framework can successfully establish a unified IFC-based information model of prefabricated buildings' construction management to prompt effective data interoperability. In addition, the application of this IFC-based information model in graph database can automatically analyze the construction schedules to prevent possible delays in advance. In short, the significance of this paper is to innovatively propose an IFC-based and graph data-based information model to solve the difficulties of ineffective data interoperation and unautomated schedule analysis in prefabricated buildings' construction management. This study can be the digital foundation of further IFC-based digital twin. [ABSTRACT FROM AUTHOR]

With the rapid growth in IC outsourcing in the semiconductors industry, concerns have increased about the weakening ICs security against hardware Trojan attacks. In this brief, an integrated hardware Trojan detection and localization methodology is presented by employing the proposed SAT-based test pattern generation scheme and the MUX-based debugging technique. The experimental results show that our methodology can effectively detect timing anomalies in the path-delays caused by hardware Trojans with node coverage around 97% as well as localizing all Trojan’s gates with a localization resolution around 99.6%. Moreover, all timing error sites are successfully identified with zero False Negative and 0.56% False Positive rates. [ABSTRACT FROM AUTHOR]

A key strategy for increasing railway capacity is utilizing infrastructure more efficiently. While much research has been completed on methods for assessing railway capacity, very little has focused on the details of capacity utilisation, such as assessing the various ways trains use capacity, the impacts of specific blocking time components, and how train dynamics (accelerating, cruising, braking, and dwelling) affect capacity. This paper presents a methodology for comparing planned occupancy to actual occupancy under real operations and applies it in a case study. The methodology is based on identifying a critical path which represents an extension of bottleneck concept presented in UIC leaflet 406. The methodology was applied in a case study to determine the specific blocking times and train dynamics which cause a blocking time gap for a sequence of trains, both a-priori and a-posteriori, after considering the operational variations. The analysis of real operations with variations in train trajectories shows that capacity occupation is mostly influenced by train sequence heterogeneity in the original schedule. The varying effects of operations have a smaller but relevant impact. The methods developed in this paper can be used to help assess railway capacity under real operations. • Capacity occupation of planned and realized railway operation are compared. • Train trajectories are reconstructed by simulation based on operation data. • A critical path approach for assessing capacity occupation of railways is proposed. • UIC norm 406 timetable compression method is extended. • Realized operation usually occupies relevantly more capacity than planned. [ABSTRACT FROM AUTHOR]

The advent of the Internet-of-Things (IoT) has led to a rapid growth in data generation. The amount of computation resources required to process the massive data generated by IoT devices, along with the new intelligent use and applications of IoT data such as smart city that can be computation intensive and delay sensitive, have caused an increase in demand for locally available resources at network edge for computation offloading. To address this issue, we have proposed the concept of opportunistic fog radio access network (OF-RAN), which extends the computation capacity of existing fog-RAN (F-RAN) by establishing virtual fog access points (v-FAP) opportunistically using resourceful user devices that participate as service nodes. In this article, we develop an analytical model to evaluate the offloading performance of three RAN architectures: the traditional cloud radio access network (C-RAN), the existing F-RAN, and the proposed OF-RAN. The performances are analyzed in terms of their energy consumption, completion delay, and failure rate, under the effect of varying scenarios.

Future mobile networks supporting Internet of Things are expected to provide both high throughput and low latency to user-specific services. One way to overcome this challenge is to adopt Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC). Besides latency constraints, these services may have strict function chaining requirements. The distribution of network functions over different hosts and more flexible routing caused by service function chaining raise new challenges for end-to-end performance analysis. In this paper, as a first step, we analyze an end-to-end communication system that consists of both MEC servers and a server at the core network hosting different types of virtual network functions. We develop a queueing model for the performance analysis of the system consisting of both processing and transmission flows. We propose a method in order to derive analytical expressions of the performance metrics of interest, i.e., end-to-end delay, system throughput, task drop rate. Then, we show how to apply the similar method to a larger system and derive a stochastic model for such systems. We observe that the simulation and analytical results are very close. By evaluating the system under different scenarios, we provide insights for the decision making on traffic flow control and its impact on critical performance metrics.

There are delays in delivering construction projects, which have great affects in all countries' economies, due to many factors and reasons and these delays bring many negative consequences. Checking the source of these consequences or minimizing their effects is very important in terms of time and cost savings in the construction sector, especially for countries which have a continuous improvement such as Turkey. Determination of project factors that will cause delay, analysis of their impacts and taking protective measures will help to reduce losses. Hereby in this study, the project factors that may cause to delays are identified and some important association rules between these factors and delay are extracted by collecting the data from Turkish Public and private construction projects. Also, some recommendations were presented for reducing delays by using the extracted rules.

Most construction projects are delayed, and many are subject to claims or disputes. Therefore, delay analysis is a critical component of any construction project to determine who is responsible for delays. This research examines four different techniques for estimating delay impacts using the impacted as-planned (IAP) method. A sample network was introduced as an example to discuss several concerns. The advantages and limitations of each approach were identified, and recommendations were given for each approach. When inserting an activity or activities representing delay events in IAP, it is necessary to use both constraints and logical relations among delay events, their logical predecessors, and successors. Constraints representing the actual date of delay events are the simplest and easiest. However, constraints should not be used in “single insertion” and “inserting only owner- or contractor-caused delay” approach. In addition, in the case of using constraints, it is critical to ensure that the impact of delay events is less than the duration of those delay events. Constraints should be avoided in this scenario, and delay events should be logically connected to their logical predecessors and successors without constraints. This study also identified through an example that inserting delay events only by logic can cause wrong analysis results. The results of this study will be helpful for delay analysts in identifying what kinds of problems occur in IAP methods and how to prevent those problems.

Ultra-reliable low latency communications (URLLC) is one of the most important scenarios in 5G. URLLC with strict latency and reliability requirements is widely used in delay-sensitive application such as self-driving. As the 3GPP claims, the URLLC is amenable to 99.999% transmission correctness and within 1ms delay bound. How to meet the requirements of reliability and latency is still an open issue. Few efforts have been made to applying a theoretical method to analyze the delay bound. Stochastic network calculus is an elegant way to obtain the delay bound based on traffic models and service guarantees. In this paper, we take the character of 5G architecture into account and use the stochastic network calculus to analyze the delay in URLLC. A tandem model describing the communication in the 5G network is built, and parameters which have an influence on the delay are analyzed. Numerical results are presented to verify the correctness of the delay analysis. We investigate how to optimize the parameters to reduce the delay, which would provide valuable guidelines for the design of URLLC architecture. [ABSTRACT FROM AUTHOR]

The promising capability of Triple Material Surrounding Gate Junctionless Tunnel FET (TMSG – JL – TFET) based 6 T SRAM structure is demonstrated by employing Germanium (Ge) and High-K gate dielectric material. The high – K insulation guarantees the proposed device to be used in low leakage memory systems. The corresponding analytical model is developed to extract various device parameters such as surface potential, electric field and threshold voltage. The results yield minimization of hot carrier effects at the drain end, when compared to conventional Silicon (Si) based Tunnel FETs (TFETs). Further, the ambipolar characteristics of the proposed device is explored and 6 T Ge – TMS – SG – JL – TFET based SRAM design is proposed. The results are compared with CMOS based SRAM and the analytical model presented is validated using 3D - TCAD ATLAS simulation, which ensures the accuracy and exactness of the developed model. [ABSTRACT FROM AUTHOR]

We first investigate the accumulated loopback self-interference (ALSI) under amplify-and-forward (AF) protocol in two-way full-duplex (TWFD) relaying systems with simultaneous wireless information and power transfer (SWIPT). We analyze how SWIPT affects ALSI and establish the condition for ALSI to converge when there is no energy buffer. Unlike ALSI in TWFD relaying without SWIPT (Chang et al., 2019), ALSI in TWFD relaying with SWIPT always converges and does not grow unbounded. In addition, we study the delay performance in such a system under AF and decode-and-forward (DF) protocols. In AF protocol, the condition for successful signal transmissions is provided. When that condition is met, the received signals at the relay can always get through the channels but experience delays. Otherwise, the outage event occurs. In DF protocol, the best-effort scenario (having no energy buffer) and the QoS scenario (having energy buffer) are discussed. The probability of successful transmissions and the distribution of the delay in both scenarios are derived. Simulation results of DF protocol show that the best-effort scenario is suitable for the delay-tolerant service and the QoS scenario is suitable for the delay-sensitive service. Furthermore, AF protocol is more suitable for the service with less QoS demanding. [ABSTRACT FROM AUTHOR]

In order to meet the ever-increasing traffic demands, the combination of fiber and Millimeter Wave (mmWave) is expected to play a key role for 5G Centralized-Radio Access Networks (C-RANs). Due to the inefficiency of the Common Public Radio Interface for the Baseband Unit (BBU)-Remote Radio Head (RRH) communication, analog-Radio-over-Fiber (a-RoF) technology is considered a promising solution, mainly due to the RRH simplification and lower fronthaul requirements it imposes. In such mmWave a-RoF C-RANs, efficient Medium Transparent-Medium Access Control (MT-MAC) protocols are needed able to meet the challenging 5G requirements. To this end, in this paper, we propose a gated service MT-MAC protocol which authorizes each user to transmit the amount of data it requested. A detailed delay model is proposed, which is validated through simulations for different fiber lengths, network load conditions and number of available optical wavelengths. Moreover, the proposed protocol is compared with the state-of-the-art (SoA) and is shown to achieve up to 20 times higher throughput, 2 times lower delay with 100% lower blocking probability and 5 times higher data wavelength utilization, while being able to adapt to varying network traffic conditions. Our proposal also attains sub-ms latency in most cases, constituting it a promising candidate for 5G mmWave a-RoF C-RANs. [ABSTRACT FROM AUTHOR]

In the real world the QOS attribute delay is a research dispute that has raised much distress being pragmatic in the multi-hop wireless networks with huge amount of data to be transmitted. The objective is to enhance the throughput and accomplish lesser end-to-end delay at the same time. The prevailing work tries to condense the delay by regulating the packet flow that might control the throughput ratio, as well as congestion is also not considered in the case of multiple flows that share single route path for the transmission of data. In the proposed system, optimal data flow scheduling is conceded out with the function of reducing the end to end delay with maximized throughput ratio by modifiable packet flow ratio. This is performed by offering the novel algorithm stated to as dynamic optimized scheduling and congestion control in wireless network that can defer the optimal performance. This novel approach recovers the throughput and rapidity for wireless networks by altering scheduling scheme with virtual adaptation model. In this scheduling every slot is separated into mini slots to reduce the complexity. To control the congestion again the mini slots are separated as micro slots. Virtual rate adjustment is performed in a diverse manner for multiple flow links that exist within the network environment on the origin of their level of emergency, so as to present individual priority for the separate flow links. This technique succeeds in rendering optimal delay compared to the available research techniques in terms of enhanced performance ratio. [ABSTRACT FROM AUTHOR]

This work considers a discrete-time multi-server queue with batch arrivals of packets having a deterministic length. By a novel analysis, the packet delay distribution is computed with a very light computational workload. [ABSTRACT FROM AUTHOR]

Air transport delays are a major source of direct and opportunity costs in modern societies, being this problem is especially important in the case of China. In spite of this, our knowledge on delay generation is mostly based on intuition, and the scientific community has hitherto devoted little attention to this topic. We here present the first data-driven systemic study of air transport delays in China, of their evolution and causes, based on 11 million flights between 2016 and 2018. A significant fraction of the delays can be explained by a few variables, e.g., weather conditions and traffic levels, the most important factors being the presence of thunderstorms and the season of the year. Remaining delays can often be explained by en-route weather phenomena or by reactionary delays. This study contributes towards a better understanding of delays and their prediction through a data-driven methodology, leveraging on statistics and data mining concepts. [ABSTRACT FROM AUTHOR]

We propose an autonomous blockchain-based federated learning (BFL) design for privacy-aware and efficient vehicular communication networking, where local on-vehicle machine learning (oVML) model updates are exchanged and verified in a distributed fashion. BFL enables oVML without any centralized training data or coordination by utilizing the consensus mechanism of the blockchain. Relying on a renewal reward approach, we develop a mathematical framework that features the controllable network and BFL parameters (e.g., the retransmission limit, block size, block arrival rate, and the frame sizes) so as to capture their impact on the system-level performance. More importantly, our rigorous analysis of oVML system dynamics quantifies the end-to-end delay with BFL, which provides important insights into deriving optimal block arrival rate by considering communication and consensus delays. We present a variety of numerical and simulation results highlighting various non-trivial findings and insights for adaptive BFL design. In particular, based on analytical results, we minimize the system delay by exploiting the channel dynamics and demonstrate that the proposed idea of tuning the block arrival rate is provably online and capable of driving the system dynamics to the desired operating point. It also identifies the improved dependency on other blockchain parameters for a given set of channel conditions, retransmission limits, and frame sizes. However, a number of challenges (gaps in knowledge) need to be resolved in order to realise these changes. In particular, we identify key bottleneck challenges requiring further investigations, and provide potential future reserach directions. 1 An early version of this work has been accepted for presentation in IEEE WCNC Wksps 2020. [ABSTRACT FROM AUTHOR]

Recently, the demand for video applications has become more and more pressing. To save backhaul bandwidth and reduce the network delay, Mobile Edge Computing (MEC) has arisen as a promising technique in future mobile networks. It can provide the storage, computing and networking resources in close-proximity to the mobile users through deploying multiple MEC servers at mobile edge. However, how to effectively utilize the limited storage capacity of these MEC servers is still the major challenge, especially when Dynamic Adaptive Streaming over HTTP (DASH) is used for video transmission. Recent studies have shown that layered encoding is very suitable for streaming service using DASH standard. Therefore, in our paper, combining with the feature of DASH-based scalable video transmission, we first design a novel multi-server cooperative scheme with software-defined networking (SDN) architecture for future MEC-assisted heterogeneous networks, to effectively manage and schedule the available resources, thus guaranteeing the requested video delivered to the clients with higher quality and lower delay. And then, based on the above scheme, the end-end delay is carefully analyzed for each real video streaming. With a full consideration of the averag end-to-end delay of video streaming and the provided video quality of MEC servers, we finally propose a Dynamic Programming based Adaptive Caching Algorithm (DPACA) to obtain an optimized caching strategy under the constraint of cache storage capacity at each MEC sever. Simulation results show that, our algorithm can improve the cache hit ratio while significantly reducing the end-to-end delay. Moreover, our algorithm has higher flexibility and adaptability. [ABSTRACT FROM AUTHOR]

Caching at mobile devices and leveraging device-to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. The analysis of cache-enabled wireless networks is usually carried out by assuming that devices are uniformly distributed, however, in social networks, mobile devices are intrinsically grouped into disjoint clusters. In this regards, this paper proposes a spatiotemporal mathematical model that tracks the service requests arrivals and account for the clustered devices geometry. Two kinds of devices are assumed, particularly, content clients and content providers. Content providers are assumed to have a surplus memory which is exploited to proactively cache contents from a known library, following a random probabilistic caching scheme. Content clients can retrieve a requested content from the nearest content provider in their proximity (cluster), or, as a last resort, the base station (BS). The developed spatiotemporal model is leveraged to formulate a joint optimization problem of the content caching and spectrum partitioning in order to minimize the average service delay. Due to the high complexity of the optimization problem, the caching and spectrum partitioning problems are decoupled and solved iteratively using the block coordinate descent (BCD) optimization technique. To this end, an optimal and suboptimal solutions are obtained for the bandwidth partitioning and probabilistic caching subproblems, respectively. Numerical results highlight the superiority of the proposed scheme over conventional caching schemes under equal and optimized bandwidth allocations. Particularly, it is shown that the average service delay is reduced by nearly 100% and 350%, compared to the Zipf and uniform caching schemes under equal bandwidth allocations, respectively. [ABSTRACT FROM AUTHOR]

This article reports on the extraction of the electron velocity as a function of gate bias from N-polar GaN deep recess high-electron-mobility transistors (HEMTs) designed for mm-wave power amplification. Bias-dependent small-signal S-parameter measurements are used to obtain small-signal equivalent circuit parameters, which are applied to a transit delay model. The model accounts for fringing capacitance to arrive at an electron velocity associated with the transit of the physical gate length. A peak electron velocity of 1.4 × 107 cm/s was obtained at a drain current of 700 mA/mm corresponding to a channel charge density of 0.3 × 1013 cm−2. At higher current, the velocity slowly decreased with the electron velocity crossing below 107 cm/s at 1.8 A/mm. This behavior was found to be in good agreement with a previously proposed model based on optical phonon scattering at the source injection point. An analysis of the delay components is used to provide guidance for the factors influencing the device performance. [ABSTRACT FROM AUTHOR]