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It is now possible to detect dangerous arcing faults in the paper insulation of transformers and distinguish them from arcing faults in oil, using new sub-zones of Duval Pentagons 1 and 2. [ABSTRACT FROM AUTHOR]

Time-domain indirect methods based on Lyapunov stability theory and linear matrix inequality techniques (LMIs) have been applied for delay-dependent stability analysis of large-scale load frequency control (LFC) schemes. This paper aims to enhance the numerical tractability of large-scale LMIs by exploiting the special characteristics of the LFC loops. First, in the typical LFC model, only a few delayed states that are directly influenced by transmission delays are distinguished from other normal system states. Hence, an improved reconstruction model is formed, based on which the delay-dependent stability condition is established with the decreased order of the LMIs and decision variables. Then, to further improve the numerical tractability of the developed stability criterion, all weighting matrices required in the augmented Lyapunov functional are enforced to have structural restrictions by proposing an extended symmetry-exploiting technique. Case studies show that the method proposed in this paper significantly improves the calculation efficiency of stability criterion established for multi-area power systems at the cost of only a minor reduction in computational accuracy. [ABSTRACT FROM AUTHOR]

Stream processing is used in various fields. In the field of big data, stream aggregation is a popular processing technique, but it suffers serious setbacks when the order of events (e.g., stream elements) occurring is different from the order of events arriving to the systems. Such data streams are called “non-FIFO steams”. This phenomenon usually occurs in a distributed environment due to many factors, such as network disruptions, delays, etc. Many analyzing scenarios require efficient processing of such non-FIFO streams to meet various data processing requirements. This paper proposes an efficient scalable checkpoint-based bidirectional indexing approach, called $CPiX$ C P i X , for faster real-time analysis over non-FIFO streams. CPiX maintains the partial aggregation results in an on-demand manner per checkpoint. CPiX needs less time and space than the state-of-the-art approach. Extensive experiments confirm that CPiX can deal with out-of-order streams very efficiently and is, on average, about 3.8 times faster than the state-of-the-art approach while consuming less memory. [ABSTRACT FROM AUTHOR]

Absolute time synchronization is a basic issue in wireless communication. With the advent of 5G, many emerging vertical industry applications have put forward demands for timing accuracy over air interface. High Precision Timing method over Air Interface based on PHY Layer Signals (TAP) is proposed for mobile networks. It is designed to provide microsecond level timing accuracy at a relatively low cost compared to the existing timing methods, such as GNSS, NTP, PTP, etc. The purpose of this paper is to comprehensively improve TAP, including timing accuracy, stability and compatibility, so as to promote its practical application. This paper proposes improvements to TAP from four aspects: timing algorithm, timing pilot signal, terminal timing process and timing parameters determination. We improve the TAP’s deficiencies in delay estimation, delay compensation accuracy and real-time matching of timing information. We implement the workflow of TAPv2 for UE based on FPGA. Finally, we settle the problem of TAP parameters determination by applying a DQN method. The simulations and tests not only show that the DQN model can effectively determine the TAP parameters, but also show TAPv2 can achieve higher accuracy and stability than TAP in multiple scenarios based on the interaction with baseband. [ABSTRACT FROM AUTHOR]

Due to the non-uniform geographic distribution and time-varying characteristics of the ground traffic request, how to make full use of the limited beam resources to serve users flexibly and efficiently is a brand-new challenge for beam hopping satellite systems. The conventional greedy-based beam hopping methods do not consider the long-term reward, which is difficult to deal with the time-varying traffic demand. Meanwhile, the heuristic algorithms such as genetic algorithm have a slow convergence time, which can not achieve real-time scheduling. Furthermore, existing methods based on deep reinforcement learning (DRL) only make decisions on beam patterns, lack of the freedom of bandwidth. This paper proposes a dynamic beam pattern and bandwidth allocation scheme based on DRL, which flexibly uses three degrees of freedom of time, space and frequency. Considering that the joint allocation of bandwidth and beam pattern will lead to an explosion of action space, a cooperative multi-agents deep reinforcement learning (MADRL) framework is presented in this paper, where each agent is only responsible for the illumination allocation or bandwidth allocation of one beam. The agents can learn to collaborate by sharing the same reward to achieve the common goal, which refers to maximize the throughput and minimize the delay fairness between cells. Simulation results demonstrate that the offline trained MADRL model can achieve real-time beam pattern and bandwidth allocation to match the non-uniform and time-varying traffic request. Furthermore, when the traffic demand increases, our model has a good generalization ability. [ABSTRACT FROM AUTHOR]

Motivated by applications in data center networks, in this paper, we study the problem of scheduling in an input queued switch. While throughput maximizing algorithms in a switch are well-understood, delay analysis was developed only recently. It was recently shown that the well-known MaxWeight algorithm achieves optimal scaling of mean queue lengths in steady state in the heavy-traffic regime, and is within a factor less than 2 of a universal lower bound. However, MaxWeight is not used in practice because of its high time complexity. In this paper, we study several low complexity algorithms and show that their heavy-traffic performance is identical to that of MaxWeight. We first present a negative result that picking a random schedule does not have optimal heavy-traffic scaling of queue lengths even under uniform traffic. We then show that if one picks the best among two matchings or modifies a random matching even a little, using the so-called flip operation, it leads to MaxWeight like heavy-traffic performance under uniform traffic. We then focus on the case of non-uniform traffic and show that a large class of low time complexity algorithms have the same heavy-traffic performance as MaxWeight, as long as it is ensured that a MaxWeight matching is picked often enough. We also briefly discuss the performance of these algorithms in the large scale heavy-traffic regime when the size of the switch increases simultaneously with the load. Finally, we perform empirical study on a new algorithm to compare its performance with some existing algorithms. [ABSTRACT FROM AUTHOR]

This paper analyzes the end-to-end delay performance in an edge-computing scenario where a set of Internet of Things devices (IoTDs) access the computation facilities of an Edge Node by means of a 5G based network. In particular, the paper deals with a two power levels slotted Aloha non-orthogonal-multiple-access (NOMA) scheme and formulates a stochastic end-to-end delay bound, in terms of complementary cumulative probability distribution, by resorting to the application of the martingale theory. In order to validate the proposed analysis, the paper proposes comparisons between the achieved analytical predictions and actual values derived by resorting to extensive computer simulations. Furthermore, the well known Boole bound has been formulated and compared with the proposed Martingale approach to highlight the better behavior of the proposed solution. [ABSTRACT FROM AUTHOR]

With the rapid growth of renewable energy and power electronic loads, voltage source converters (VSCs) have been commonly applied in different VSCs-based power systems, which also bring in stability issues due to their power electronic characteristics. The impedance-based stability theory and corresponding reshaping method can be used to analyze and solve system stability issue. However, dc stability issue in VSCs-based power systems becomes more complicated when considering ac/dc coupling relationship. This paper proposes an improved reshaping control strategy based on the detailed impedance model considering grid impedance and control delay. The influence of grid impedance, control delay and control parameters of VSC on dc stability are further analyzed to identify the cause of potential resonance risk. The proposed impedance reshaping method considers the trade-off between stability improvement and control performance, and compensates the impacts of grid impedance and control delay at the same time. The validity of stability analysis and reshaping method is further verified through the simulation and experimental results. [ABSTRACT FROM AUTHOR]

Human-machine interactive systems show increasing demand for analysing fast moving objects in high-frame-rate videos. Robust foreground detection, which is able to reduce large amount of redundant background data from high-frame-rate video, becomes the essence to achieve ultra-high-speed human-machine interactions. This paper proposes a local spatial propagation based background model generation, a local linear illumination correction based background model update, and a regional central coordinates and edge keypoints constrained foreground region reselection. The three proposals make up a robust and hardware-friendly foreground detection method. Experimental results prove that the proposed hardware-friendly algorithm achieves high accuracy and robustness on various kinds of challenging cases. Meanwhile, the hardware implementation utilizes little hardware resources and achieves realtime processing of high-frame-rate (784 frame/second) video with the delay less than 1 ms/frame in image processing core. In addition, a practical system is implemented by combing a PC, a high-speed camera and a field programmable gate array (FPGA) for realworld applications. This work will significatively promote the development and application of high-speed human machine interaction. A demo of the proposed vision system working at 784 FPS is available at https://wcms.waseda.jp/em/5f84f75136a6. Note to Practitioners—This paper was motivated by the problem of high-frame-rate video contains large amount of redundant background pixels which makes ultra-high-speed human-machine interactions inaccessible. Existing approaches are mainly focused on designing complex background models, but processing speed, which is the most important issue for ultra-high-speed human-machine interactions, has received relatively little attention. This paper suggests a robust and hardware-friendly foreground detection algorithm which has been implemented as a hardware system by using an FPGA, a high-frame-rate camera, and a PC. We show that the hardware implementation utilizes less hardware resources and achieves real-time processing speed of 784 FPS with the delay less than 1 ms/frame in the image processing core. This work is a pioneering attempt of ultra-high-speed foreground detection, which will significatively speed up the wide applications of ultra-high-speed human machine interactions. [ABSTRACT FROM AUTHOR]

State-of-charge (SoC) imbalance and bus voltage deviation are two of the main problems in autonomous dc microgrids. Based on this concern, this paper presents an improved dual-quadrant SoC weighted control strategy and a distributed optimization control method to achieve SoC balance, ensuring accurate power-sharing and bus voltage recovery. Firstly, this paper couples the injected/released power with the current SoC and observed average SoC value to weight the droop coefficient, which is based on the charge/discharge mode for the energy storage system. Then a secondary controller is designed based on distributed optimal control to eliminate the bus voltage deviation caused by the line impedance difference. The proposed optimal control method optimizes the average bus voltage to the nominal value and achieve accurate power-sharing by constructing the correlated variables and voltage independent intermediate variables exchanged among bulk energy storage units (ESUs). Since the voltage observer cannot accurately observe the true average bus voltage under the communication delay, the proposed distributed optimal control method without the voltage observer can ensure that the average bus voltage is optimized to the nominal value, thus improving the robustness of the control system. Finally, the correctness and effectiveness of the proposed method are verified in Simulink/MATLAB. [ABSTRACT FROM AUTHOR]

Owning to the sub-standards being developed by IEEE Time-Sensitive Networking (TSN) Task Group, the traditional IEEE 802.1 Ethernet is enhanced to support real-time dependable communications for future time- and safety-critical applications. Several sub-standards have been recently proposed that introduce various traffic shapers (e.g., Time-Aware Shaper (TAS), Asynchronous Traffic Shaper (ATS), Credit-Based Shaper (CBS), Strict Priority (SP)) for flow control mechanisms of queuing and scheduling, targeting different application requirements. These shapers can be used in isolation or combination and there is limited work that analyzes, evaluates, and compares their performance, which makes it challenging for end-users to choose the right combination for their applications. This paper aims at (i) quantitatively comparing various traffic shapers and their combinations, (ii) summarizing, classifying, and extending the architectures of individual and combined traffic shapers and their Network calculus (NC)-based performance analysis methods, and (iii) filling the gap in the timing analysis research on handling ATS and CBS used for different priority queues, and two novel hybrid architectures of combined traffic shapers, i.e., TAS+ATS+SP and TAS+ATS+CBS when ATS and CBS used at the same queue. A large number of experiments, using both synthetic and realistic test cases, are carried out for quantitative performance comparisons of various individual and combined traffic shapers, from the perspective of upper bounds of delay, backlog, and jitter. To the best of our knowledge, we are the first to quantitatively compare the performance of the main traffic shapers in TSN. The paper aims at supporting the researchers and practitioners in the selection of suitable TSN sub-protocols for their use cases. [ABSTRACT FROM AUTHOR]

This paper analyzes the literature to identify ideas which may be applied to decentralized realtime railway traffic management. This system represents a new way for dealing with railway traffic perturbations in absence of a central decision maker. Specifically, we are interested in identifying techniques that may constitute suitable automatic mechanisms for the emergence of an effective system behavior. In this literature review, we discuss the possibility of exploiting the existing research works on other transport modes. The analysis of these works makes it clear that real-time railway traffic management is very peculiar. Hence, we consider different approaches: hierarchical self-organization, task allocation, reinforcement learning, consensus, auction and coopetition techniques. Some promising possibilities emerge, which we analyze proposing ideas for modeling decentralized real-time railway traffic management. [ABSTRACT FROM AUTHOR]

Wireless channel characterization and modeling is the foundation of vehicular communication systems. However, most of the existing researches on vehicle-to-vehicle (V2V) channel are aimed at the traditional scenarios such as urban and suburban, and the researches on some complicated vehicular scenarios are insufficient. For example, little attention is paid to viaduct, tunnel, and cutting scenarios in vehicular communications, and these complex scenarios often become the high-incidence area of communication interruption, and then affect the overall performance of the vehicular communication system due to the bad and unique channel characteristics. In this paper, to fill this gap, time-varying characteristics of V2V channels in viaduct, tunnel, and cutting scenarios are investigated. Specifically, based on 5.9 GHz channel measurements, channel non-stationarity is quantitatively evaluated and compared. Further, a detailed analysis of time-varying power and delay of multipath components (MPCs) is presented. Based on the obtained results, the mechanism of physical environment affecting channel characteristics is analyzed, and the impacts of vehicular scenarios are quantified. Besides, the impacts of scenarios on the spatial distribution of MPCs are investigated. The angular distributions of MPCs in the three typical V2V scenarios are revealed, and the corresponding statistical characteristics are presented. Moreover, the characteristics of MPC clusters in different scenarios are further quantified and analyzed, such as MPC number in each cluster and cluster lifetime. It is revealed that the non-stationarity of the V2V channel originates from the birth-death process of MPC clusters, and the birth-death process is modeled statistically. The results in the paper show the V2V channel propagation mechanism and can be used for the design of vehicular communication systems in complicated scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes on-driving experience sharing algorithms at junctions in infrastructure-assisted vehicles-to-everything networks. For the purpose, a millimeter-wave (mmWave) technology is used because it provides multi-Gbps data rates which is helpful for handling users’ short stay times at junctions and spatial reuse due to high beam directionality which is helpful for interference-avoidance among densely deployed vehicles at junctions. To realize on-driving experience sharing, the proposed algorithms focus on joint resource allocation and scheduling for 3GPP-compliant multiple unicast vehicle-to-vehicle (V2V) communications where the vehicles are group leaders (GLs) in 3GPP Mode 4(d). The resource allocation stands for the roadside unit (RSU) allocation to scheduled V2V GL links where RSU is essentially required for overcoming blockage by establishing two-hop relaying. Because vehicles stay for short times at junctions, this paper designs two algorithms without or with delay considerations. Without delay considerations, the joint optimization of RSU allocation and scheduling was originally formulated as mixed 0-1 non-convex optimization. However our proposed algorithm reformulates the problem into mixed 0-1 convex optimization, which is computationally easier to solve. With delay considerations, our proposed algorithm dynamically controls video contents frame rates for time-average on-driving video sharing quality maximization subject to delay constraints, inspired by Lyapunov optimization. Extensive simulation results demonstrate that our algorithms can significantly outperform in a variety of scenarios. Furthermore, we conduct the cost analysis for the proposed algorithms in terms of capital expenditure (CAPEX) and operating expenditure (OPEX). [ABSTRACT FROM AUTHOR]

The age of information (AoI) is a key performance metric for data freshness in real-time systems, measuring the time elapsed between status updates received at a remote destination. This paper addresses the analysis of both the AoI and the delay for a system demanding timely status updates, consisting of an internet of things (IoT) community, and a multi-core edge computing node (EN). The IoT devices perform channel contention according to a slotted aloha non-orthogonal multiple access scheme with two transmission power levels to access the EN. The objective of the paper is the dynamic selection of both the EN computation capabilities and the suitable access probability for the two power levels, aiming at maximizing the time-average system utility, while guaranteeing the queues stability. The device end-to-end delay is analyzed by resorting to the martingale theoretical approximation under the condition that the Lyapunov optimization framework is performed on a time slot basis, hence leading to a novel instantaneous mixed integer problem formulation. Furthermore, the generalized benders decomposition method is employed to provide a suboptimal solution for the formulated problem. Finally, the accuracy of the proposed framework is validated by comparing the obtained analytical predictions with simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a receiver architecture capable of compensating for a time-varying signal distortion induced by the transmitter analog components. In the proposed architecture, the novel transmitter distortion compensating (TDC) block is implemented outside of the feedback control loop of the adaptive equalizer to suppress the feedback delay by optimizing the parameter used in the least mean square (LMS) algorithm. The proposed TDC block is designed for compensating for the DC offset, the IQ amplitude imbalance, the IQ orthogonality error, and the IQ timing skew. Our simulation results show that the proposed scheme significantly reduces the required signal-to-noise ratio penalty from the theoretical limit, which is imposed due to the transmitter components distortion. Furthermore, our theoretical analysis confirms that the delay induced in the feedback loop of the adaptive equalizer determines the upper bound of the LMS step size under the stable condition, hence allowing us to maximize the tracking speed of our receiver. [ABSTRACT FROM AUTHOR]

Flight delay prediction has recently gained growing popularity due to the significant role it plays in efficient airline and airport operation. Most of the previous prediction works consider the single-airport scenario, which overlooks the time-varying spatial interactions hidden in airport networks. In this paper, the flight delay prediction problem is investigated from a network perspective (i.e., multi-airport scenario). To model the time-evolving and periodic graph-structured information in the airport network, a flight delay prediction approach based on the graph convolutional neural network (GCN) is developed in this paper. More specifically, regarding that GCN cannot take both delay time-series and time-evolving graph structures as inputs, a temporal convolutional block based on the Markov property is employed to mine the time-varying patterns of flight delays through a sequence of graph snapshots. Moreover, considering that unknown occasional air routes under emergency may result in incomplete graph-structured inputs for GCN, an adaptive graph convolutional block is embedded into the proposed method to expose spatial interactions hidden in airport networks. Through extensive experiments, it has been shown that the proposed approach outperforms benchmark methods with a satisfying accuracy improvement at the cost of acceptable execution time. The obtained results reveal that deep learning approach based on graph-structured inputs have great potentials in the flight delay prediction problem. [ABSTRACT FROM AUTHOR]

Spatial modulation (SM) is one special type of multiple-input multiple-output (MIMO) technique whose advantage is that only one radio-frequency (RF) chain is needed. Recently, the so-called cross Z-complementary pair (CZCP) was proposed as the SM training sequence. Optimal channel estimation performance over frequency-selective channels can be achieved since the CZCPs have the specific zero correlation zone (ZCZ) for auto-correlation and cross-correlation sums. However, the ZCZ width of the CZCP is theoretically upper bounded by half sequence length. In this paper, the CZCP is extended to the cross Z-complementary set (CZCS) to have larger ZCZ width which can be used in SM to combat larger delay spread. Several generic constructions of CZCSs with large ZCZ widths and flexible lengths are proposed in this paper. Even the perfect CZCS, whose ZCZ width has the maximum value, can be obtained. Numerical simulations demonstrate that the proposed CZCS-based training sequence can tolerate large delay spreads to improve the channel estimation performance in SM. [ABSTRACT FROM AUTHOR]

Connected automated driving technologies have shown substantial benefits to improve the safety and efficiency of traffic. However, connected mixed traffic, which involves both connected automated vehicles and connected human-driven vehicles, is more foreseen for the realistic case in the near future. This brings new challenges because of the complexity of human elements in the system. In addition, the communication constraints in realistic connectivity such as random delays and packet losses bring even more challenges to the system. Therefore, this paper proposes a new anticipative and predictive automated vehicle control approach in connected mixed traffic. The approach first anticipates the states of surrounding vehicles including human-driven vehicles, and then integrates the anticipation into the predictive control of automated vehicles, which can help improve the control performance and also handle the communication constraints. An inverse model predictive control (IMPC) based anticipation approach has been proposed. The proposed approach, together with constant speed (CS), intelligent driver model (IDM) and artificial neural network (ANN) based anticipation methods are integrated with model predictive control (MPC) for automated vehicle control. The approaches have been tested in human-in-the-loop experiments and the results show that the integration with a newly proposed IMPC based anticipation has shown the best performance in terms of accuracy, efficiency and scalability in connected mixed traffic with both ideal and constrained communications. [ABSTRACT FROM AUTHOR]

Future communication networksrequire higher bandwidth, greater coverage, and better throughput. The Space-Air-Ground Integrated Network (SAGIN) has the advantage of wide-area coverage and can cover global communications. It can meet the needs of network resources for maritime activities and remote mountainous areas, which is of great significance to the realization of a new generation of communications networks. With the development of the network and the emergence of delay-sensitive applications such as the Internet of Things, improving the delay performance of the system has received extensive attention. SAGIN involves multiple networks and is more complex than other networks. If there is no reasonable management between different networks, it is easy to lead to difficult link deployment and high time delay. On the basis of research and customization of SFC technology, it can provide a wide range of services and other advantages. It has significant performance in application scenarios involving multi network integration. In order to solve the above problems, this paper studies the SAGIN architecture of SFC based on business types. A service function chain mapping method based on delays prediction is proposed. Calculate the delay of the deployment path and select the path with the lowest delay as the SFC mapping path. The service model is constructed according to the mapping path, and the network slices are divided based on the service type. The simulation results show that the SFC mapping algorithm based on time delay prediction is compared with the traditional SFC mapping scheme. The algorithm does not affect other indicators, the CPU resource utilization rate is 27.8 $\%$ higher, and the link resource utilization rate is 22.7 $\%$ higher. The service acceptance rate increased by 21.5 $\%$ , the latency performance increased by 38.2 $\%$ , and the total resource consumption is reduced by 25.2 $\%$. [ABSTRACT FROM AUTHOR]

In this paper, a novel management framework for fog computing with strategic computing speed control at fog nodes (FNs) is studied. In the considered model, mobile users declare requests of offloading resource-hungry computation tasks that are dynamically collected at a dedicated edge server (ES). Upon receiving these requests, the ES can decide to either self-process or delegate some workloads to third-party FNs for maximizing the overall management profit. Unlike the existing work, this paper takes into account strategic behaviors of FNs in computing speed control, i.e., each FN can strategically allocate its computing resource to maximize its utility, which consists of the benefit gained from executing offloaded tasks and the cost incurred by dissatisfied (delayed) service to its own subscribed tasks. To jointly address the long-term system performance and FNs’ strategic interactions, a scheduling mechanism integrating a noncooperative game and a queueing model is formulated. We then investigate two delegation reward settings, i.e., constant and utility-dependent delegation prices, and propose efficient adaptive algorithms to determine the optimal workload distribution at the ES and the computing speed equilibrium among FNs. Both theoretical analyses and simulations are conducted to evaluate the performance of the proposed solutions and demonstrate their superiorities over counterparts. [ABSTRACT FROM AUTHOR]

In this paper, we study how to learn a harmonious deep reinforcement learning (DRL) based lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) communication support. The basic framework of this paper can be viewed as a multi-agent reinforcement learning in which different agents will exchange their strategies after each round of learning to reach a zero-sum game state. Unlike cooperation driving, harmonious driving only relies on individual vehicles’ limited sensing results to balance overall and individual efficiency. Specifically, we propose a well-designed reward that combines individual efficiency with overall efficiency for harmony, instead of only emphasizing individual interests like competitive strategy. Testing results show that competitive strategy often leads to selfish lane change behaviors, anarchy of crowd, and thus the degeneration of traffic efficiency. In contrast, the proposed harmonious strategy can promote traffic efficiency in both free flow and traffic jam than the competitive strategy. This interesting finding indicates that we should take care of the reward setting for reinforcement learning-based AI robots (e.g., automated vehicles) design, when the utilities of these robots are not strictly in alignment. [ABSTRACT FROM AUTHOR]

Travelling wave based protection functions require significantly higher sampling rates than protection functions based on time-domain superimposed quantities or fundamental frequency phasors. Integrating these high sampling rates in digital substations leads to a significant increase of the communication load of process-level networks and causes high computational cost for centralized protection systems. This paper builds on a distributed signal processing approach, which allocates the filtering operations among standalone merging units (SAMU). In particular, the paper presents a decimation filter design to integrate signals with high sampling rates at the process-level. Thereby, signal sequences with high, medium and low sampling rates are provided according to the requirements of the respective protection function. The decimation filter structure is optimized with respect to time delay by the Remez exchange algorithm and with respect to computational cost by a multistage approach and a sparse filter design algorithm. In addition, the filter response is verified against the accuracy constraints defined in IEC 61869-6 and IEC 61869-13. The paper shows that it is feasible to distribute the filtering operations among SAMUs, while keeping the time delay below the maximum allowable processing delay of 2 ms. [ABSTRACT FROM AUTHOR]

Mobile edge computing has been an important supplement for traditional cloud computing architecture to offer low-delay computing services to mobile users. However, it is in general impossible for edge service providers to overdeploy so much edge resources to satisfy the rapidly increasing while diverse user demands. In this paper, we study a mobile edge computing system consisting of a service platform, cloudlets joining the system, and mobile users. In this study, we focus on a profit-driven perspective such that the service platform purchases computation resource from the resource-rich cloudlets and makes profit by processing tasks from user side. The design objective is to maximize the platform profit subject to budget constraint and stringent delay requirements for task processing. We formulate this problem as a mixed integer programming problem. Due to the NP-hardness of the problem, we design a logic based Benders decomposition algorithm as the offline solution. We further study the scenario where the task arrivals from user side and resource availability at the cloudlets are both stochastic and unknown in advance. We accordingly propose a Multi-Armed Bandit learning based resource purchasing and greedy task scheduling algorithm for the online scenario. Simulations results show the high performance of our proposed algorithms. [ABSTRACT FROM AUTHOR]

With the rapid advancement of intelligent transportation systems (ITS) and vehicular communications, vehicular edge computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the network- and base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment. [ABSTRACT FROM AUTHOR]

Co-simulation of heterogeneous systems allows for in-depth analysis of various aspects of power systems’ operation while staying within the environments of the simulation tools that are best fit to represent their respective domains. Equipped with a proprietary co-simulation platform, the paper focuses on the issue of power-conjugate coupling between parts of power grids modeled in transient stability and electromagnetic transient simulation tools. The problems of co-simulation stability and precision in presence of delays are tackled by means of designing a proper coupling interface. It is shown that two established interface methods – the V-I method and the Transmission Line Interface – are special cases of a generalized interface framework proposed in the paper. Moreover, a new interface algorithm is described by parametrizing the generalized framework. Analytical tools are also formulated to aid in the analysis of interface stability and precision via the concepts of passivity and transparency. Simulation results of benchmark systems of various complexity demonstrate the application of the developed power coupling interface. [ABSTRACT FROM AUTHOR]

Pulsed Flip-flops (FFs) are popular elements in the design of high-speed microprocessors. Technology scaling has led to a considerable increase in manufacturing process variation and aging phenomena affecting the reliability of these FFs. In this paper, the timing reliability of pulsed FFs is improved using a transistor-level restructuring technique. In this technique, we modify the pull-down network of pulsed FFs for decreasing the stress time (i.e., the time of being ON) of the pulsed clock transistors. Extensive Monte-Carlo based HSPICE simulations are conducted to show the effectiveness of the proposed restructuring technique under different process variation ratios and lifetime values. The obtained experimental result showed that the lifetime reliability of pulsed FFs is improved by 15% and at the expense of 4% area overhead under 30% process variation ratio and 9 years of operation time. [ABSTRACT FROM AUTHOR]

In human society, the normal operation of important infrastructures, such as the power system, communication system, and computer network depend on the precise time provided by stationary satellite timing receivers, which are vulnerable to a kind of spoofing interference called time-synchronization attack. The aim of the attacks is to change the time provided by receivers to other systems, resulting in system disorders. This paper presents a time-synchronization attack detection and weakening method to solve this problem. The proposed method can estimate the binomial model parameters of receiver clock bias and the clock bias attack values at all observation instants by solving an optimization problem. Once the existence of the attacks is detected, the estimated clock bias attack values are used to correct the clock bias, so as to weaken the time-synchronization attacks. According to the numerical simulation results and analysis, we find that the proposed method outperforms other algorithms mentioned in this paper. Because it can not only realize real-time detection, but also greatly weaken the impact of time-synchronization attacks, making the attacks almost invalid. [ABSTRACT FROM AUTHOR]

In this paper, variable measurement interval is proposed for channel-adaptive random access with discontinuous channel measurement. After each channel measurement, the next measurement interval is adaptively determined based on previous channel measurement. Results show that receiver power consumption for channel measurement can be reduced with variable measurement interval under the same average transmission delay constraint compared with fixed measurement interval. [ABSTRACT FROM AUTHOR]

As one of the three main application scenarios of 5 G cellular system, massive Machine-Type Communications (mMTC) has been regarded as the key solution to facilitate the IoT paradigm. One major bottleneck for accommodating mMTC is the severe congestion at the cellular random access channel when plenty of Machine-Type Devices (MTDs) send access requests concurrently while the preamble resources are limited. To remedy this issue, limiting the number of retransmissions and dropping access requests after the limit is reached can be an effective approach. Yet, the effect of the preamble retransmission limit $K$ on the optimal access performance of mMTC in cellular networks remains largely unexploited, which motivates the study in this paper. Specifically, in this paper, we start by characterizing the network steady-state points based on the limiting probability of successful transmission of access requests. We then obtain explicit expressions of the access throughput and the mean access delay of successfully-transmitted access requests as functions of $K$ and the number of preambles $M$. The maximum access throughput and the corresponding optimal backoff window size are further derived. It is shown that the maximum access throughput is independent of $K$ , while the mean access delay can be significantly reduced with a small $K$ , yet, at the expense of increased request dropping ratio. In addition, to improve both the throughput and delay performance, the analysis shows that more preambles should be allocated but the performance gain becomes marginal when $M$ is large. Therewith, an algorithm is proposed for determining the least number of preambles $M^\ast$ that maximizes the access throughput and the preamble resource utilization ratio. Numerical results show that a smaller preamble retransmission limit $K$ can further reduce $M^\ast$. [ABSTRACT FROM AUTHOR]

This paper considers the massive connectivity problem in an asynchronous grant-free random access system, where a huge number of devices sporadically transmit data to a base station (BS) with imperfect synchronization. The goal is to design algorithms for joint user activity detection, delay detection, and channel estimation. By exploiting the sparsity on both user activity and delays, we formulate a hierarchical sparse signal recovery problem in both the single-antenna and the multiple-antenna scenarios. While traditional compressed sensing algorithms can be applied to these problems, they suffer high computational complexity and often require the perfect statistical information of channel and devices. This paper solves these problems by designing the Learned Approximate Message Passing (LAMP) network, which belongs to model-driven deep learning approaches and ensures efficient performance without tremendous training data. Particularly, in the multiple-antenna scenario, we design three different LAMP structures, namely, distributed, centralized and hybrid ones, to balance the performance and complexity. Simulation results demonstrate that the proposed LAMP networks can significantly outperform the conventional AMP method thanks to their ability of parameter learning. It is also shown that LAMP has robust performance to the maximal delay spread of the asynchronous users. [ABSTRACT FROM AUTHOR]

During the last few years, with the decrease of their sizes and costs, Unmanned Aerial Vehicles (UAVs) became more feasible for their use in general-purpose applications. As an important basis for UAV applications, the Air-to-Ground (A2G) radio propagation channel has gained attention in the channel modeling literature. However, whereas the A2G propagation channel is inherently dynamic (time-varying), the majority of the available models do not consider such a time-variability. This paper proposes a channel multi-path component (MPC) tracking algorithm and shows its ability to analyze the data collected by a real A2G measurement campaign in a suburban environment. Based on the obtained results, a time-varying statistical channel model for A2G communications in realistic suburban scenarios is proposed. The model is able to stochastically characterize parameters related to the birth and time-of-life of the multi-path components (MPCs), as well as their evolution in terms of delay, Doppler frequency or magnitude. Correlation coefficients to relate different channel characteristics are also obtained. Our work shows that the MPCs evolution over time for the UAV A2G channel can be described by simple regular patterns. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) provides a novel computing paradigm to satisfy the increasing computation requirements of mobile applications. In MEC-enabled intelligent transportation systems (ITS), the latency-sensitive computing tasks are offloaded to RSUs for execution, reducing the transmission latency compared with the cloud solutions. However, the repetitive executions of the same tasks whose outputs are dependent on the inputs lead to the extra system latency, an alternative is to cache the required services on RSUs in advance. The service requirements of latency-sensitive computing tasks are satisfied by jointly considering computation offloading and service caching. Besides, the digital twin (DT) is utilized to construct the virtual world reflecting the physical world in real-time to efficiently make offloading strategies. In this paper, a computation offloading and service caching method using decision theory in ITS with DT, named CODT, is proposed. Specifically, the computation offloading and service caching in ITS is modeled first with DT. Then, a mixed-integer nonlinear programming (MINLP) problem is formulated to minimize the system latency. Afterward, the decision theory is used to analyze the utilities of offloading strategies in different states of RSUs and make the optimal strategy. Finally, extensive simulations based on the real-world datasets demonstrate that the proposed CODT outperforms other baselines. [ABSTRACT FROM AUTHOR]

Wireless multiplayer interactive virtual reality (VR) game has the high computing workload of VR and unpredictable interaction among players, which brings severe challenges to the design of wireless communication systems. In this paper, we propose a wireless multiplayer interactive VR game transmission framework based on mobile edge computing (MEC) that is able to model the interaction among players and compute the post-processing procedures at the MEC server or the mobile VR device. In the framework, the absolute delay of each player is used to avoid VR vertigo and the inter-player delay among players is used to model the fairness of the interactive game. Aiming to minimize the average inter-player delay, we optimize the computing resource allocation of the MEC server, the wireless bandwidth allocation and the post-processing decision policy subject to the constraints of the absolute delay requirements, the local energy limits of players, the total bandwidth limit and the computing resources limit. To tackle the non-convex problem efficiently, we design an iterative algorithm based on the NESTT-G algorithm which iteratively optimizes the truncated first-order Taylor approximation of the objective. Numerical results demonstrate the proposed algorithm can reduce the average inter-player delay significantly with lower complexity, and also reveal the impact of different parameters and the channel state conditions on the post-processing decision and edge resource allocation. [ABSTRACT FROM AUTHOR]

In vehicular networks, authentication technology provides a basic security means to achieve trusted communication between legitimate vehicles. Revocation checking for vehicle certificates is an indispensable procedure in the process of authentication to protect vehicular networks from attacks by non-legitimate vehicles. However, revocation checking introduces procedures that requires additional time to process which challenges latency-sensitive applications in vehicular networks. This challenge grows more evidently when considering the factor of privacy preservation. In this paper, we propose to offload partial revocation task to network edges to lighten the revocation process in vehicles. Particularly, we design a method to achieve decentralized Global Certificate Revocation List (GCRL) management in network edges using permissioned blockchain technologies. In the method, both tamper-proof property and quick synchronization of the GCRL are guaranteed. Besides, our method can detect illegal revocation behaviours by validation in vehicles. Finally, we build a prototype of our proposed solution based on Hyperledger Fabric, and compare with Proof-of-Work scheme in terms of blockchain synchronization latency performance. [ABSTRACT FROM AUTHOR]

The number of individuals and groups of users offloading independent and inter-related computational tasks to mobile edge computing (MEC) servers is rapidly increasing, thus overloading them and raising the risk of service interruptions. Hence, reactive service replication has been suggested to enable individuals and groups of users to access services from remote edge servers, thus guaranteeing system scalability. This paper proposes a task offloading and service replication scheme on local and remote MEC servers. The scheme minimizes the response time of all users while satisfying the delay requirements of user groups in traffic-heavy and multimedia-intense applications (e.g., online gaming, multimedia conferencing, augmenting reality). We formulate an integer linear problem that minimizes the average response time of all users while satisfying the time and time difference constraints of the user groups running the same applications. We then use linear relaxation programming using Lagrangian analysis and solve the problem using a numerical solver. In addition, we compare the optimal solution to distance-based and resource-based greedy approaches. The results demonstrate the merits of our proposed optimized decision scheme compared to these two greedy approaches. [ABSTRACT FROM AUTHOR]

This paper studies low-latency streaming codes for the multi-hop network. The source transmits a sequence of messages to a destination through a chain of relays, and requires the destination to reconstruct each message by its deadline. We assume that each communication link is subjected to a certain maximum number of packet erasures. The case of a single relay (a three-node network) was considered in Fong et al. (2020). A coding scheme known as symbol-wise decode and forward was proposed. In the present work, we propose an alternative scheme that is different from Fong et al. (2020) and still achieves the same rate as in Fong et al. (2020) for the one hop case as the field-size goes to infinity. Furthermore, our proposed scheme naturally generalizes to the case of multiple-relay nodes yielding new achievable rates for this setting. The main difference with Fong et al. (2020) is that our proposed scheme exploits the ability of the relay nodes to adapt the transmission based on the erasures on the previous link. Hence, we refer to our scheme as “state-dependent” and contrast it with the scheme in Fong et al. (2020) that is state-independent. Our scheme requires the relay nodes to append a header to the transmitted packets, and we show that the size of the header does not depend on the field-size of the code. We also derive an upper bound on the maximal streaming rate achievable over a network with an arbitrary number of relays. We show that this upper bound matches our achievable rate in the special case when the maximal number of erasures on the first link is greater than or equal to the maximal number of erasures on each of the following links, and the field size goes to infinity. [ABSTRACT FROM AUTHOR]

Age of Information (AoI) is an application layer performance metric that quantifies the freshness of information. This paper investigates scheduling problems at network edge when there is an AoI requirement for each source node, which we call Maximum AoI Threshold (MAT). Specifically, we want to determine whether or not a vector of MATs corresponding to the source nodes is schedulable, and if so, find a feasible scheduler for it. For a small network, we present an optimal procedure called Cyclic Scheduler Detection (CSD) that can determine the schedulability with absolute certainty. For a large network where CSD is not applicable, we present a novel low-complexity procedure, called Fictitious Polynomial Mapping (FPM), and prove that FPM can find a feasible scheduler for any MAT vector when the load is under $\ln 2$. We use extensive numerical results to validate our theoretical results and show that the performance of FPM is significantly better than a state-of-the-art scheduling algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a novel embedding protocol that allows for safe and efficient operation of the Hybrid Autonomous Intersection Management (H-AIM) protocol concurrently with actuated and adaptive signal controllers. The proposed protocol extends H-AIM to allow it to cope with some operational uncertainty that is common in actuated signal controllers. A novel approach for computing safety bounds on signal timing is presented as a way of insuring safety in the face of demand uncertainty. Experimental results show the feasibility and effectiveness of combining H-AIM with actuated controllers for various levels of connected and autonomous vehicle (CAV) market penetration and different combinations of common signal control schemes, namely, adaptive signal timing, fixed signal timing, and signal actuation. The benefits are presented in terms of delay improvement when common actuation protocols are used in conjunction with the H-AIM protocol. In contrast to previous reports, the results presented in this paper suggest that mixtures of turning movement assignments that are more permissive for CAVs and less permissive for human operated vehicles are often detrimental in terms of traffic delay. Nonetheless, when implemented on top of an actuated and adaptive controller, the extended H-AIM protocol is shown to never be detrimental while presenting statistically significant reductions in total delay when more than 15% of the traffic is composed of CAVs. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the PID controller design problem of path following for an autonomous ground vehicle (AGV). Firstly, a bicycle model is adopted and a vehicle offset model from the target path is integrated to the bicycle model. The PID controller considers the vehicle longitudinal speed variation for adaption to the road curvature and the stochastic delay induced by the network communication. This is realized by transforming the tuning problem of proportional-integral-derivative (PID) gains for path following into a design problem of a static-output-feedback (SOF) controller for a time-delayed linear parameter varying (LPV) model form. A sufficient condition is adopted to guarantee the stability of the closed-loop system. In order to achieve better tracking performance, we propose a strategy in which the PID gains are piecewise constant and are dependent on the road-curvature ranges. The stability of the switched system is guaranteed via the common Lyapunov function method. Grey wolf optimizer (GWO) is employed to solve the optimization problem with maximum absolute tracking error as the optimization objective and stability condition and actuator dynamics as constraints. Both simulation results based on the CarSim-Simulink joint platform and hardware-in-loop experiment results are used to verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Government environment is the main determinant in attracting private investment into public–private partnership (PPP) projects, especially in developing countries. Red tape, an indicator of a government's efficiency, plays a critical role in private investment in PPP projects. Reasonable levels of red tape can enhance government transparency and promote private investment, while excessive red tape usually represents low governance efficiency and imposes further risk on private investors. This article explores how developing countries’ red tape affects private investment in PPP projects by examining the moderating effect of corruption. Analyzing a database of 308 PPP projects in 111 developing countries, the study reveals an inverted U-shaped relationship between red tape and private investment. Corruption weakens the positive relationship between red tape and private investment at low levels of red tape and mitigates their negative relationship at high levels. The study integrates the inconsistent results of previous research that postulated either a positive or negative relationship between red tape and private investment by proposing a nonlinear model. It also theorizes the moderating effect of corruption based on real management practice and illustrates its mechanism in absorbing private investment in PPP projects. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) is emerging as a promising paradigm for network architectures. By migrating network functions from dedicated hardware appliances to software instances running in a virtualized environment, NFV promises to offer a more flexible way to deploy and manage service function chains (SFCs). When deploying these SFCs to users, the network operators require not only the user's demands (e.g., end-to-end delay) to be satisfied, but require the cost of SFC mapping to be minimized (e.g., resource consumption). To fulfill these two goals, in this paper, we have investigated how to realize the delay-aware and resource-efficient SFC mapping in inter-datacenter elastic optical networks. We first formulate an integer linear programming (ILP) model to solve the problem exactly. The main optimization goal in the ILP model is to jointly minimize resource consumption and end-to-end delay to achieve optimal virtual network function placement. Then, a delay-aware and load-balancing mapping algorithm (DALB-MA) is proposed to obtain a near-optimal solution in a reasonable amount of time. Finally, we evaluate the proposed ILP model and heuristic algorithms via extensive simulations. The results indicate that the proposed ILP model and the DALB-MA outperform the benchmarks in terms of block rate, average cost, number of CPUs used, maximum frequency slot index, and delay margin gain. [ABSTRACT FROM AUTHOR]

Blockchain protocols require nodes to verify all received transactions before forwarding them. However, massive spam transactions cause the participants in blockchain systems to consume many resources in verifying and propagating transactions. This paper proposes a reputation-based mechanism to increase the efficiency of processing transactions by considering the reputations of the sending nodes. Reputations are in turn adjusted based on the quality of transaction processing. Our proposed reputation-based mechanism offers three main contributions. First, we modify the verification strategy so that nodes set a probability of verifying a received transaction considering the likelihood of it being spam: transactions from a node with a low reputation have a high probability of being verified. Second, we optimize the transaction forwarding protocol to reduce propagation delay by prioritizing forwarding transactions to reputable receivers. Third, we design a data request protocol that provides alternative data exchange methods for nodes with different reputations. A series of simulations demonstrate the performance of our reputation-based mechanism. [ABSTRACT FROM AUTHOR]

The Smart City contexts and the adoption of Industry 4.0 are being upgraded with the latest technologies throughout all systems. New data are continuously collected in huge amounts; therefore, an efficient data aggregation scheduling scheme is highly demanded. This paper addresses the minimum time aggregation scheduling problem in duty-cycled sensor networks. Existing solutions schedule the sensory data through predefined routing structures, which limit the utilization of diverse active time slots of sensors. We propose a period-driven pipeline scheduling approach, namely PDA, that simultaneously grows the aggregation tree and assigns a transmission schedule for each node being added to the tree. Particularly, this process is performed in a top-down manner. In each iteration, corresponding to a time slot, PDA uses a multi-level ranking strategy to schedule several $\langle sender, receiver \rangle$ 〈 s e n d e r , r e c e i v e r 〉 pairs, so that in a working period ahead, the possibility to pipeline as many transmissions as possible is high. Intensive simulation results show that the proposed scheme notably works better than the best known recent algorithms by having up to 35 percent shorter aggregation time, as well as having a significantly improved network throughput and time utilization. [ABSTRACT FROM AUTHOR]

Underwater wireless acoustic networks (UWANs) take advantage of acoustic communication to enable many distinct applications. The peculiar features of underwater acoustic channels, such as long propagation delay, high bit error rates, and severely limited bandwidth, make the use of medium access control (MAC) solutions designed for terrestrial radio networks inefficient in UWANs. This paper proposes UW-SEEDEX, a MAC protocol for UWANs that employs random time slot schedules, created from seeds, to avoid collisions. After exchanging seeds, nodes can know other’s entire schedules, allowing them to then better plan their transmissions. Simulations evaluate how each of UW-SEEDEX’s parameter affects its performance in metrics such as end-to-end delay, energy consumption, transmissions per data reception, and reception rate using different test scenarios. Simulations also show that UW-SEEDEX can perform better than other MAC solutions, delivering more messages than protocols such as UW-Aloha and Slotted FAMA and reduced, on average, up to three times the number of transmissions required for each message reception in networks with grid topologies. UW-SEEDEX presented reception rates close to 100 percent, low energy cost, and fewer transmissions per data reception. Our code is available at https://gitlab.com/epmcj/ns-3-dev/-/tree/new-uan-mac-protocols. [ABSTRACT FROM AUTHOR]