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Nowadays, efficient spectrum usage is one of the most important design principles to take into account in wireless communications due to the exponential growth of mobile devices. In that sense, solutions such as Non-Orthogonal Multiple Access (NOMA) and cognitive radio (CR) have been proposed. In essence, NOMA allows some interference level by using non-orthogonal resource allocation with a tolerable increase in receiver complexity employing successive interference cancellation (SIC). In this work, a novel mathematical model of teletraffic for users performing accessment, simultaneously, by means of Orthogonal Multiple Access (OMA) and NOMA, is developed using a Markovian process that considers bursts of arrivals to model the access schemes. This novel procedure implies a closed-form solution of the proposed system compared to other works where these parameters are estimated assuming the moment generating function obtained with approximation models. The model is validated with a discrete event simulator, considering different scenarios and simulation conditions. The simulation results are in agreement with the mathematical solution proposed. [ABSTRACT FROM AUTHOR]

Abstract The authors study the energy efficiency (EE) of ambient backscatter communication (AmBC) device‐assisted and reconfigurable intelligent surfaces (RIS)‐assisted non‐orthogonal multiple access (NOMA) downlinks. The authors establish two optimisation problems based on the two collaborative devices (AmBC devices, RIS) with the objective of maximising the EE of the system, taking into account the requirements of power limitation and rate limitation, etc. and also obtain the solutions of two problems by optimising the relevant performance metrics based on the alternating optimisation algorithm. For the backscatter device (BD)‐aided downlink NOMA network, the problem is first decoupled into three subproblems, where the power allocation optimisation subproblem is solved by using the quadratic transformation method and the subgradient algorithm. The maximum EE is obtained by iterating according to the Dinkelbach's algorithm. For the RIS‐aided downlink NOMA network, the power allocation problem is solved by the same method as above and the phase optimisation problem is solved by the successive convex approximation method. Numerical results show that the proposed algorithm can achieve convergence after several iterations, and the EE of systems with BD‐assisted and RIS‐assisted have different levels of sensitivity to different influencing factors.

Abstract Industrial IoT (IIoT) applications are widely used in multiple use cases to automate the industrial environment. Industry 4.0 presents challenges in numerous areas, including heterogeneous data, efficient data sensing and collection, real‐time data processing, and higher request arrival rates, due to the massive amount of industrial data. Building a time‐sensitive network that supports the voluminous and dynamic IoT traffic from heterogeneous applications is complex. Therefore, the authors provide insights into the challenges of industrial networks and propose a strategy for enhanced traffic management. An efficient multivariate forecasting model that adapts the Multivariate Singular Spectrum Analysis is employed for an SDN‐based IIoT network. The proposed method considers multiple traffic flow parameters, such as packet sent and received, flow bytes sent and received, source rate, round trip time, jitter, packet arrival rate and flow duration to predict future flows. The experimental results show that the proposed method can effectively predict by contemplating every possible variation in the observed samples and predict average load, delay, inter‐packet arrival rate and source sending rate with improved accuracy. The forecast results shows reduced error estimation when compared with existing methods with Mean Absolute Percentage Error of 1.64%, Mean Squared Error of 11.99, Root Mean Squared Error of 3.46 and Mean Absolute Error of 2.63.

The authors study the energy efficiency (EE) of ambient backscatter communication (AmBC) device‐assisted and reconfigurable intelligent surfaces (RIS)‐assisted non‐orthogonal multiple access (NOMA) downlinks. The authors establish two optimisation problems based on the two collaborative devices (AmBC devices, RIS) with the objective of maximising the EE of the system, taking into account the requirements of power limitation and rate limitation, etc. and also obtain the solutions of two problems by optimising the relevant performance metrics based on the alternating optimisation algorithm. For the backscatter device (BD)‐aided downlink NOMA network, the problem is first decoupled into three subproblems, where the power allocation optimisation subproblem is solved by using the quadratic transformation method and the subgradient algorithm. The maximum EE is obtained by iterating according to the Dinkelbach's algorithm. For the RIS‐aided downlink NOMA network, the power allocation problem is solved by the same method as above and the phase optimisation problem is solved by the successive convex approximation method. Numerical results show that the proposed algorithm can achieve convergence after several iterations, and the EE of systems with BD‐assisted and RIS‐assisted have different levels of sensitivity to different influencing factors. [ABSTRACT FROM AUTHOR]

In the literature, macroscopic fundamental diagram (MFD)- or network fundamental diagram-based perimeter control approaches have been demonstrated as cost-effective means to improve traffic efficiency for urban traffic networks. This article proposes a new perimeter control approach, which extends previous approaches in two aspects. First, the proposed approach optimizes the allocation of perimeter metered flow considering the heterogeneity of network traffic flow distribution, which is inversely related to network production. Second, queue management is considered by the proposed approach so as to prevent queue spillback induced by perimeter control. The proposed control approach is well advanced for field application using only loop detector data. It adopts a hierarchical framework, where the upper level controller determines the total flow entering the protected network through perimeter links based on an MFD-based feedback regulator, while the lower level controller optimizes the distribution of the total metered flow among the perimeter links based on real-time network heterogeneity and perimeter queues. The proposed approach is tested and compared with state-of-the-art perimeter control approaches, using a real traffic network developed in the microscopic simulation software VISSIM. Results demonstrate that the proposed approach significantly reduces the overall network delay. Moreover, it outperforms other perimeter control approaches by maintaining low network heterogeneity within the protected network and preventing (or delaying) queue spillback at perimeter links. [ABSTRACT FROM AUTHOR]

Context. We investigate a method to signal processing based on the Kolmogorov-Wiener filter weight function calculation for the prediction of a continuous stationary heavy-tail process in the MFSD (multifractal fractional sum-difference) model. Such a process may describe telecommunication traffic in some systems with data packet transfer, the consideration of the continuous filter may be reliable in the case of the large amount of data. Objective. The aim of the work is to obtain an approximate solution for the Kolmogorov-Wiener filter weight function and to show the applicability of the method to signal processing used in the paper. Method. The Galerkin method based on the orthogonal Chebyshev polynomials of the first kind is used for the calculation of the weight function under consideration. The approximations up to the thirteen-polynomial one are investigated. The corresponding integrals are calculated numerically on the basis of the Wolfram Mathematica package. The higher is the packet rate, the higher accuracy of the integral calculation is needed. Results. It is shown that for rather large number of polynomials the misalignment between the left-hand side and the right-hand side of the Wiener-Hopf integral equation under consideration is rather small for the obtained solutions. The corresponding mean absolute percentage errors of misalignment for different packet rates are calculated. The method to signal processing used in the paper leads to reliable results for the Kolmogorov-Wiener filter weight function for the prediction of a process in the MFSD model. Conclusions. The theoretical fundamentals of the continuous Kolmogorov-Wiener filter construction for the prediction of a random process in the MFSD model are investigated. The filter weight function is obtained as an approximate solution of the WienerHopf integral equation with the help of the Galerkin method based on the Chebyshev polynomials of the first kind. It is shown that the obtained results for the filter weight function are reliable. The obtained results may be useful for the practical telecommunication traffic prediction. The paper results may also be applied to the treatment of heavy-tail random processes in different fields of knowledge, for example, in agriculture. [ABSTRACT FROM AUTHOR]

Industrial IoT (IIoT) applications are widely used in multiple use cases to automate the industrial environment. Industry 4.0 presents challenges in numerous areas, including heterogeneous data, efficient data sensing and collection, real‐time data processing, and higher request arrival rates, due to the massive amount of industrial data. Building a time‐sensitive network that supports the voluminous and dynamic IoT traffic from heterogeneous applications is complex. Therefore, the authors provide insights into the challenges of industrial networks and propose a strategy for enhanced traffic management. An efficient multivariate forecasting model that adapts the Multivariate Singular Spectrum Analysis is employed for an SDN‐based IIoT network. The proposed method considers multiple traffic flow parameters, such as packet sent and received, flow bytes sent and received, source rate, round trip time, jitter, packet arrival rate and flow duration to predict future flows. The experimental results show that the proposed method can effectively predict by contemplating every possible variation in the observed samples and predict average load, delay, inter‐packet arrival rate and source sending rate with improved accuracy. The forecast results shows reduced error estimation when compared with existing methods with Mean Absolute Percentage Error of 1.64%, Mean Squared Error of 11.99, Root Mean Squared Error of 3.46 and Mean Absolute Error of 2.63. [ABSTRACT FROM AUTHOR]

Electronic toll collection system (ETCS) devices on vehicles communicate wirelessly with road head devices at toll stations. ETCS is preferred in countries because it is more convenient and efficient than manual toll collection. However, the scattered and constant innovations in this sector require a bridge between worldwide ETCS usage and advancements and a single page to grasp this sector's developments to ensure further developments. Following a systematic review, this study examined ETCS worldwide and technology adaption considering the scopes, limitations, usages, and related technology of the ever‐developing electronic toll collection system. Existing ETCS technologies have been investigated and synchronized with ingrowing tech disparity to overcome the research synchronization gap. The study also included a bibliometric assessment to identify the important areas of research, technologies most used, degree of knowledge generated, growth/trend of the researches, most well‐known authors, keywords, countries, documents, source of publication, and so on considering time‐to‐time changes. The study provided bibliometric solutions to the gap of lack of synchronization of research worldwide and even ETCS implementation difference within a country, which complicated collaboration with the central system and linked organizations and made it difficult for users to adapt the system. [ABSTRACT FROM AUTHOR]

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This paper presents an approach for the predictive analysis of teletraffic performance indices through derived analytical and regression structures based on Artificial Intelligence. The systematization of synthesis, testing, and verification processes for simulation-modeled teletraffic ICT infrastructure with queue service organization was carried out. The forecast models for the selected system throughput and system response time indices against the specific complex indicator Service Demand were obtained. Polynomial regression models based on the Coefficient of determination R were achieved. In the course of procedural teletraffic forecasting, we used Feed-Forward Neural Networks (FFNNs), Generalized Regression Neural Networks (GRNNs), and Cascade-Forward Neural Networks. The selection of neural models was performed as the functional minimization of the Mean-Squared Error (MSE) and Mean Absolute Error (MAE). [ABSTRACT FROM AUTHOR]

Rapid growth in the volume of transmitted information has lead to the emergence of new wireless networking technologies with variable heterogeneous topologies. With limited radio frequency resources, optimal routing problems arise, both at the network design stage and during its operation. We propose an algorithm based on a minimum loss intensity (greedy-gradient algorithm) to search for optimal routes of information transmission in telecommunication networks. The relevance of the developed algorithm is determined by its practical use in data-transmitting modeling systems. The proposed algorithm satisfies several requirements, such as the speed of the calculations performed, the fulfillment of the conditions for its convergence, and its independence on the selected loss probability function, as well as on the network topology. The idea of the algorithm is a step-by-step recalculation of metrics based on derivatives of the loss intensity function with simultaneous redistribution of information flows along the routes determined by the Floyd algorithm. The comparative efficiency of the proposed algorithm is demonstrated by computational experiments on various network topologies (up to 100 nodes) with various traffic intensities. [ABSTRACT FROM AUTHOR]

Context. The development of information and communication technologies has led to an increase in the volume of information sent over the network. Media service platforms play an important role in the creation and processing of bitrate in the information network. Therefore, there is a need to develop a methodology for predicting bitrate in various media service platforms by creating an effective algorithm that minimizes the forecast error. Objective. The aim of the work is to synthesize in analytical form the state transition matrix of the Kalman filter for nonstationary self-similar processes when predicting the bitrate in telecommunication networks. Method. A methodology has been developed for predicting teletraffic in media service platforms, based on a modification of the Kalman filter for non-Gaussian processes. This methodology uses an original procedure for calculating statistics, which makes it possible to reduce the filtering and forecast error that arises due to the uncertainty of the analytical model of the process under study. The methodology does not require knowledge of the analytical model of the process, as well as strict restrictions on its stochastic characteristics. Results. A methodology for estimating and forecasting bitrate in telecommunication systems is proposed. This methodology was used to study teletraffic processes in the media service platforms Google Meet, Zoom, Microsoft Teams. The passage of real bitrate through the specified media service platforms was studied. A comparison of real teletraffic with predicted teletraffic was carried out. The influence of the order of the state transition matrix of the Kalman filter on the error of estimation and prediction has been studied. It has been established that even a low (second) order of the state transition matrix allows one to obtain satisfactory forecast results. It is shown that the use of the proposed methodology makes it possible to predict traffic with a relative error of the order of 3-4%. Conclusions. An original algorithm for assessing and forecasting the characteristics of media traffic has been developed. Recommendations for improving the technology for building media service platforms are formulated. It is shown that the bitrates generated by various media service platforms, in the case of applying the proposed estimation and forecasting methodology, are invariant with respect to the type of stochastic processes being processed [ABSTRACT FROM AUTHOR]

In this article, a novel approach to calculate the Erlang B formula and its inverses is proposed. It is based on application of artificial neural networks (ANNs). Namely, ANNs are trained to calculate the Erlang B call blocking probability as well as to solve the inverse problem, i.e. to calculate the required number of channels or the maximum amount of traffic for the given call blocking probability. Comparing to direct calculation of Erlang B formula, computational efficiency is significantly increased, while the accuracy is maintained. As far as the calculation of Erlang B inverse values is considered, as there is a lack of exact mathematical formulas, the proposed approach provides the closed‐form mathematical expressions. [ABSTRACT FROM AUTHOR]

Context. Some of the authors’ recent papers were devoted to the Kolmogorov-Wiener filter for telecommunication traffic prediction in some stationary models, such as the fractional Gaussian noise model, the power-law structure function model, and the GFSD (Gaussian fractional sum-difference) model. Recently, the so-called generalized fractional Gaussian noise model was proposed for stationary telecommunication traffic description in some cases. So, in this paper the theoretical fundamentals of the continuous Kolmogorov-Wiener filter used for the prediction of the generalized fractional Gaussian noise are investigated. Objective. The aim of the work is to obtain the filter weight function as an approximate solution of the corresponding Wiener-Hopf integral equation with the kernel equal to the generalized fractional Gaussian noise correlation function. Method. A truncated Walsh function expansion is proposed in order to obtain the corresponding solution. This expansion is a special case of the Galerkin method, in the framework of which the unknown function is sought as a truncated series in orthogonal functions. The integral brackets and the results for the mean absolute percentage errors, which are a measure of discrepancy between the left-hand side and the right-hand side of the Wiener-Hopf integral equation, are calculated numerically on the basis of the Wolfram Mathematica package. Results. The investigation is made for approximations up to sixty four Walsh functions. Different model parameters are investigated. It is shown that for different model parameters the proposed method is convergent and leads to small mean absolute percentage errors for approximations of rather large numbers of Walsh functions. Conclusions. The paper is devoted to a theoretical construction of the continuous Kolmogorov-Wiener filter weight function for the prediction of a stationary random process described by the generalized fractional Gaussian noise model. As is known, this model may give a good description of some actual telecommunication traffic data in systems with packet data transfer. The corresponding weight function is sought on the basis of the truncated Walsh function expansion method. The corresponding discrepancy errors are small and the method is convergent. [ABSTRACT FROM AUTHOR]

Machine learning and neural networks have become increasingly popular solutions for encrypted malware traffic detection. They mine and learn complex traffic patterns, enabling detection by fitting boundaries between malware traffic and benign traffic. Compared with signature-based methods, they have higher scalability and flexibility. However, affected by the frequent variants and updates of malware, current methods suffer from a high false positive rate and do not work well for unknown malware traffic detection. It remains a critical task to achieve effective malware traffic detection. In this paper, we introduce CBSeq to address the above problems. CBSeq is a method that constructs a stable traffic representation, behavior sequence, to characterize attacking intent and achieve malware traffic detection. We novelly propose the channels with similar behavior as the detection object and extract side-channel content to construct behavior sequence. Unlike benign activities, the behavior sequences of malware and its variant’s traffic exhibit solid internal correlations. Moreover, we design the MSFormer, a powerful Transformer-based multi-sequence fusion classifier. It captures the internal similarity of behavior sequence, thereby distinguishing malware traffic from benign traffic. Our evaluations demonstrate that CBSeq performs effectively in various known malware traffic detection and exhibits superior performance in unknown malware traffic detection, outperforming state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Multi-threaded design of the applications has given a new dimension to the execution concurrency. The Network-on-Chip (NoC) infrastructure has evolved as the communication backbone in multiprocessor System-on-Chips (MPSoCs) to exploit Thread-Level Parallelism (TLP). Mapping the threads of various applications on the processing cores is crucial in utilizing the resources of a many-core system. An application's performance depends primarily on the mapping strategy. However, the performance of an application gets influenced significantly by the underlying network architecture of the NoC. The present work proposes a run-time application mapping technique based on the Cube-Tree-Hybrid (CTH) topology focusing on the traffic load balancing across the network. The mapping overhead decreased significantly by exploiting the low network diameter of CTH with its high scalability and path diversity, projecting a 31% reduction in normalized execution time over the prevalent algorithms on the existing mapping platforms. Rigorous experimentation with varying application and network sizes is carried out across mesh, ZMesh, torus, and Mest-of-Tree (MoT) networks. The experimental observations project that the proposed algorithm results in sustainable application performance with 15%-21% gain in the overall energy consumption over the existing state-of-the-art techniques across various benchmark workloads. [ABSTRACT FROM AUTHOR]

In connection with the global decarbonization program until 2050, the transition to clean green energy, the growth of the Internet of Things (IoT) number, and energy distribution and control across the load are being raised. The relevance of the work is confirmed that there has been significant growth of the industrial IoT for years, significantly changing the mechanism of industrial enterprise management programs. The object of the research is the IoT device control system for efficient energy distribution using a Queuing Theory, namely the Teletraffic Theory. The novelty of the work is that the Teletraffic Theory, which deals with the mathematical modeling and analysis of traffic patterns in communication networks, can be explicitly applied to IoT device control. The authors developed a mathematical model of IoT control using the Teletraffic Theory and, based on it, created a simulation model of a network router and a transition schedule in the "GPSS World" software. The obtained results of the work were 16 states and a balance equation in which all probabilities were found. Probabilities were used to calculate nodes and network characteristics. 100,000 requests from IoT devices coming to two routers were simulated. The study results showed that the first node's load is 63.2 % with an average processing time per transaction of M=1.436 sec., and the load of the second node is 32 % with M=0.914 sec. The created network router model worked with minimal losses during transactions. Accordingly, the IoT control system developed in this study has shown its effectiveness and is applicable for practical use in controlling IoT devices in Smart Grid. It is planned to research the possibility of using Teletraffic Theory in energy distribution control systems in Smart Grids. [ABSTRACT FROM AUTHOR]

One-sided heavy tailed distributions have been used in many engineering applications, ranging from teletraffic modelling to financial engineering. In practice, the most interesting heavy tailed distributions are those having a finite mean and a diverging variance. The LogNormal distribution is sometimes discarded from modelling heavy tailed phenomena because it has a finite variance, even when it seems the most appropriate one to fit the data. In this work we provide for the first time a LogNormal distribution having a finite mean and a variance which converges to a well-defined infinite value. This is possible thanks to the use of Non-Standard Analysis. In particular, we have been able to obtain a Non-Standard LogNormal distribution, for which it is possible to numerically and experimentally verify whether the expected mean and variance of a set of generated pseudo-random numbers agree with the theoretical ones. Moreover, such a check would be much more cumbersome (and sometimes even impossible) when considering heavy tailed distributions in the traditional framework of standard analysis. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) networks are distributed systems with a communication model in which no central authority governs the behavior of individual peers. These networks currently account for a considerable percentage of all bandwidth worldwide. However, this communication model also has a clear disadvantage: it has a multitude of vulnerabilities and security threats. The nature of the P2P philosophy itself means that there is no centralized server responsible for uploading, storing, and verifying the authenticity of the shared files and packets. A direct consequence of this is that P2P networks are a good choice for hackers for the spread of malicious software or malware in general since there is no mechanism to control what content is shared. In this paper, we present a mathematical model for P2P networks to study the effect of two different attacks on these systems, namely, malware and denial of service. To analyze the behavior of the cyber attacks and identify important weaknesses, we develop different Markov chains that reflect the main dynamics of the system and the attacks. Specifically, our model considers the case in which a certain number of nodes are infected with a cyber worm that is spread throughout the network as the file is shared among peers. This allows observation of the final number of infected peers when an initial number (we evaluate the system for from 1 to 14 initial nodes) of malicious nodes infect the system. For the DoS attack, our model considers the portion of peers that are unable to communicate and the average attack duration to study the performance degradation of such an attack. A two-pronged approach was used to study the impact of the attacks on P2P networks; the first focused only on the P2P network, and the second focused on the attacks and the network. [ABSTRACT FROM AUTHOR]

The study aims to uncover the service level of major cellular providers in Indonesia. The stages of the research included the determination of point where cellular towers are built as well as the density of the telecommunication traffic in particular regions in Indonesia. The users of the cellular towers are representatives of the companies from certain logistics establishment that utilizing the existence of the towers. Twelve spots of cellular towers were taken as research objects with 155 users representing their companies who uses and utilizing the cellular towers for various of businesses dominantly in logistics businesses. Structural equation model analysis were implemented to seek the affects of each variables. The result showed that all the data collected were supporting the hypotheses and this result were also showed that loyalty towards service delivered and complaints might occur parallelly. [ABSTRACT FROM AUTHOR]

Abstract In this article, a novel approach to calculate the Erlang B formula and its inverses is proposed. It is based on application of artificial neural networks (ANNs). Namely, ANNs are trained to calculate the Erlang B call blocking probability as well as to solve the inverse problem, i.e. to calculate the required number of channels or the maximum amount of traffic for the given call blocking probability. Comparing to direct calculation of Erlang B formula, computational efficiency is significantly increased, while the accuracy is maintained. As far as the calculation of Erlang B inverse values is considered, as there is a lack of exact mathematical formulas, the proposed approach provides the closed‐form mathematical expressions.

The world has seen exponential growth in deploying Internet of Things (IoT) devices. In recent years, connected IoT devices have surpassed the number of connected non-IoT devices. The number of IoT devices continues to grow and they are becoming a critical component of the national infrastructure. IoT devices' characteristics and inherent limitations make them attractive targets for hackers and cyber criminals. Botnet attack is one of the serious threats on the Internet today. This article proposes pattern-based feature selection methods as part of a machine learning (ML)-based botnet detection system. Specifically, two methods are proposed: the first is based on the most dominant pattern feature values and the second is based on maximal frequent itemset mining. The proposed feature selection method uses Gini impurity and an unsupervised clustering method to select the most influential features automatically. The evaluation results show that the proposed methods have improved the performance of the detection system. The developed system has a true positive rate of 100% and a false positive rate of 0% for best performing models. In addition, the proposed methods reduce the computational cost of the system as evidenced by the detection speed of the system. [ABSTRACT FROM AUTHOR]

Abstract Convolutional neural networks (CNNs) are the specific architecture of feed‐forward artificial neural networks. It is the de‐facto standard for various operations in machine learning and computer vision. To transform this performance towards the task of network anomaly detection in cyber‐security, this study proposes a model using one‐dimensional CNN architecture. The authors' approach divides network traffic data into transmission control protocol (TCP), user datagram protocol (UDP), and OTHER protocol categories in the first phase, then each category is treated independently. Before training the model, feature selection is performed using the Chi‐square technique, and then, over‐sampling is conducted using the synthetic minority over‐sampling technique to tackle a class imbalance problem. The authors' method yields the weighted average f‐score 0.85, 0.97, 0.86, and 0.78 for TCP, UDP, OTHER, and ALL categories, respectively. The model is tested on the UNSW‐NB15 dataset.

Modern information and telecommunication, transportation and logistic, economic and financial systems are represented by complex networks exhibiting traffic flows with spatio-temporal long-term persistence. Conventional queuing theory relies largely upon stationary models where traffic flows are assumed independent and are typically characterized by the first two moments of inter-arrival and service time distributions, leading to drastic underestimations of traffic flow delays. Here we extend a recent superstatistical approach focusing on traffic models with variable arrival rates by accounting for interdependent activity patterns on multiple network nodes. We suggest an analytical correction to the conventional stationary queue model given by the Kingman's formula based on the calculation of aggregated inter-arrival times variability from the variabilities of arrival rates at individual nodes and cross-correlations between them. We confirm our analytical approximations by comparing with computer simulation results and large-batch empirical traffic analysis from the backbone of a major academic network. We believe that our results, in combination with recent data on the effects of long-term temporal persistence in network traffic flow, are applicable to various complex networks not limited to information and telecommunication, transportation, and logistics but also to economics and finance, rainfall and river flow dynamics, water accumulation in reservoirs, and many other research domains exhibiting spatio-temporal interdependence patterns. • Abrupt bursts in traffic dynamics are governed by simultaneous multiple nodes access. • Inter-arrival times distribution is derived from cross-correlations between nodes. • Analytical correction for an approximate evaluation of waiting times is proposed. • Results are supported by computer simulations and large-batch traffic data analysis. • Corrections are applicable to complex networks governed by autonomous agents dynamics. [ABSTRACT FROM AUTHOR]

Abstract The software‐defined networking (SDN) represents the future backbone of the Internet. In general, to understand the performance and the limitation of SDN is a prerequisite for deploying these networks and making the most of their resources. For achieving that goal, this paper discusses the difference between traditional networks and SDN over wide area network (WAN) where an accurate queuing model of packet forwarding is proposed to measure the performance of many factors affecting the system, such as response time, service rate, utilization, throughput and delay through a vEdge router. Additionally, it focusses for studying algebraic estimates of the probability distribution of the system states. The Matrix‐Geometric solution procedure of a phase‐type distribution queue with first‐in, first‐out (FIFO) discipline is used. The analysis results indicate that the proposed queuing model gives a more accurate approximation for software‐defined WAN (SD‐WAN) controller system performance than the present, where this solution shows that the most important results are benefits of centralized management of SD‐WAN, an increase in bandwidth through separating the control plan from the data plan, thus reducing volume operations in the data plan, a delay and an increase in packet transfer speed. Additionally, it improves business performance and diminishes budget.

Accurate rainfall monitoring is critical for sustainable societies and yet challenging in many ways. Opportunistic monitoring using commercial microwave links (CMLs) in telecommunication networks is emerging as a powerful complement to conventional gauges and weather radar. However, CML data are often inaccessible or incomplete, which limits research and application. Here, we aim to reduce this barrier by openly sharing data at 10 s resolution with true coordinates from a pilot study involving 364 bi-directional CMLs in Gothenburg, Sweden. To enable further comparative analyses, we also share high-resolution data from 11 precipitation gauges and the Swedish operational weather radar composite in the area. This article presents an overview of the data, including the collection approach, descriptive statistics, and a case study of a high-intensity event. The results show that the data collection was very successful, providing near-complete time series for the CMLs (99.99 %), gauges (100 %), and radar (99.6 %) in the study period (June–August 2015). The bandwidth consumed during CML data collection was small, and hence, the telecommunication traffic was not significantly affected by the collection. The gauge records indicate that total rainfall was approximately 260 mm in the study period, with rainfall occurring in 6 % of each 15 min interval. One of the most intense events was observed on 28 July 2015, during which the Torslanda gauge recorded a peak of 1.1 mmmin-1. The variability in the CML data generally followed the gauge dynamics very well. Here we illustrate this for 28 July, where a nearby CML recorded a drop in received signal level of about 27 dB at the time of the peak. The radar data showed a good distribution of reflectivities for mostly stratiform precipitation but also contained some values above 40 dBZ , which is commonly seen as an approximate threshold for convective precipitation. Clutter was also found and was mostly prevalent around low reflectivities of - 15 dBZ. The data are accessible at 10.5281/zenodo.7107689 (Andersson et al., 2022). We believe this Open sharing of high-resolution data from Microwave links, Radar, and Gauges (OpenMRG) will facilitate research on microwave-based environmental monitoring using CMLs and support the development of multi-sensor merging algorithms. [ABSTRACT FROM AUTHOR]

While attack detection is key to realize trustworthy smart cities, the use of large amounts of network traffic data by machine learning techniques can lead to privacy issues for citizens. To face this issue, we propose a federated learning approach in the context of Internet of Things-enabled smart cities integrating the Threat and Manufacturer Usage Description files as a prevention/mitigation approach. [ABSTRACT FROM AUTHOR]

This paper considers a caching system of a single server and multiple users. We aim to characterize the memory-rate tradeoff for caching with uncoded cache placement, under nonuniform file popularity. Focusing on the modified coded caching scheme (MCCS) recently proposed by Yu, etal., we formulate the cache placement optimization problem for the MCCS to minimize the average delivery rate under nonuniform file popularity, restricting to a class of popularity-first placements. We then present two information-theoretic lower bounds on the average rate for caching with uncoded placement, one for general cache placements and the other restricted to the popularity-first placements. By comparing the average rate of the optimized MCCS with the lower bounds, we prove that the optimized MCCS attains the general lower bound for the two-user case, providing the exact memory-rate tradeoff. Furthermore, it attains the popularity-first-based lower bound for the case of general $K$ users with distinct file requests. In these two cases, our results also reveal that the popularity-first placement is optimal for the MCCS, and zero-padding used in coded delivery incurs no loss of optimality. For the case of $K$ users with redundant file requests, our analysis shows that there may exist a gap between the optimized MCCS and the lower bounds due to zero-padding. We next fully characterize the optimal popularity-first cache placement for the MCCS, which is shown to possess a simple file-grouping structure and can be computed via an efficient algorithm using closed-form expressions. Finally, we extend our study to accommodate nonuniformity in both file popularity and size, where we show that the optimized MCCS attains the lower bound for the two-user case, providing the exact memory-rate tradeoff. Numerical results show that, for general settings, the gap between the optimized MCCS and the lower bound only exists in limited cases and is very small. [ABSTRACT FROM AUTHOR]

Crowdsensing systems are developed in order to use the computational and communication capabilities of registered users to monitor specific variables and phenomena in an opportunistic manner. As such, the Quality of Experience is not easily attained since these systems heavily rely on the user's behavior and willingness to cooperate whenever an event with certain interest needs to be monitored. In this work, we analyze the data acquisition phase, where pedestrians opportunistically transmit to vehicles to further disseminate it in the city according to their trajectory. This highly dynamic environment (sensors and data sinks are mobile, and the number of users varies according to the region and time) poses many challenges for properly operating a crowdsensing system. We first study the statistical properties of vehicular traffic in different regions of Luxembourg City where pedestrians share their computational resources and send data to passing cars. Then we propose an Erlang distribution to model the vehicles' dwelling times and develop a Markov chain accordingly. We model the system using two different queues: we use a single server queue to model the vehicle traffic, while we use an infinite server queue system to model the pedestrian traffic. [ABSTRACT FROM AUTHOR]

In a Fog Radio Access Network (F-RAN), fog access points (F-APs) are equipped with caches that can store popular files during off-peak hours. Besides, they are densely deployed to have overlapping radio coverage so that requested files can be delivered cooperatively using beamforming. The bottleneck of the network is typically in the bandwidth-limited wireless fronthaul, which connects a cloud server to the F-APs. This work studies index coding design for cooperative caching and delivery in F-RAN to minimize fronthaul traffic and transmit energy. Index coding algorithms are designed considering the cached content at the F-APs and the possibility of beamforming in the access network under coded and uncoded caching schemes. An optimal polynomial-time index coding algorithm for uncoded and repetition caching and an efficient heuristic for Maximum Distance Separable (MDS) coded caching are designed, and their superior performance is verified by simulations. The study is further extended to consider the tradeoff between the traffic load of the fronthaul link and the transmit energy consumed in the access network. At the expense of more fronthaul traffic, beamforming opportunities can be increased, significantly reducing energy consumption. Algorithms to achieve the tradeoff are crafted, and simulation results show that uncoded caching well balances the tradeoff. [ABSTRACT FROM AUTHOR]

With the rise of the Industrial Internet of Things (IIoT), more and more industrial devices can be connected via the network. Data collection, processing, analysis, task execution, and other devices that can product network traffic volume are gradually being deployed to IIoT. However, under the limited spectrum resources and low-cost and low-energy production requirements of enterprises, how to ensure the interconnection and intercommunication of industrial networks while realizing the effective use of network communication resources is currently a hot topic. Among them, network traffic prediction is considered to be a very important task. The time variability and interpretability, especially the time-varying features of traffic sequences, greatly challenge this task. To address those, this article proposes a method called Flow2graph to predict network traffic in IIoT. Specifically, some key segments, i.e., shapelets are extracted from the network traffic sequence according to time-varying traffic; then uses the relationship between the traffic sequence and shapelets to convert the flow into a shapelets conversion graph; Subsequently, the graph isomorphism network are used to learn the specificity of the flow sequence from different devices, thereby to predict its traffic value for a period of time in the future; finally, we conduct extensive experiments on real data to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the rapid advance of industrial Internet of Things (IIoT), to provide flexible access for various infrastructures and applications, software-defined networks (SDNs) have been involved in constructing current IIoT networks. To improve the quality of services of industrial applications, network traffic prediction has become an important research direction, which is beneficial for network management and security. Unfortunately, the traffic flows of the SDN-enabled IIoT network contain a large number of irregular fluctuations, which makes network traffic prediction difficult. In this article, we propose an algorithm based on multitask learning to predict network traffic according to the spatial and temporal features of network traffic. Our proposed approach can effectively obtain network traffic predictors according to the evaluations by implementing it on real networks. [ABSTRACT FROM AUTHOR]

In order to overcome the capacity limitations of current lightwave systems based on the single-mode optical fiber, massively parallel transmission in the spatial domain [space-division multiplexing (SDM)] supported by extended parallelism in the frequency domain (ultrawideband (UWB) systems) must be used. This article reviews key aspects of parallel transmission systems as the only significant capacity scaling option going forward and discusses the various tradeoffs on an architectural level and a hardware integration level. In doing so, this article also serves as an introduction to the more detailed accounts of fiber-optic systems and their future scaling within this Special Issue of the Proceedings of the IEEE. [ABSTRACT FROM AUTHOR]

Abstract We consider leveraging the joint potential of non‐orthogonal multiple access (NOMA) and traffic offloading to benefit cell‐edge users in multi‐cell scenarios. We formulate the edge‐driven throughput maximisation problem where both the offloading decision and resource allocation are to be optimised, accounting for the impact of the inter‐cell interference on the decoding order in NOMA. The inter‐cell interference is not given constants as it is influenced by resource allocation. Therefore the non‐orthogonal multiple access (NOMA) decoding order is not known a priori. We explore the problem by decomposing it into two subproblems. By analysis of structural properties, we derive algorithm modules which interact iteratively for reciprocity, forming the overall algorithm. Numerical results show that the proposed scheme considerably improves the cell‐edge throughput, compared with the conventional scheme of orthogonal multiple access without traffic offloading.

This paper describes the prototype and tests carried out to demonstrate the feasibility of implementing 4G/5G systems based on mobile network operators (MNOs) to transmit signalling critical data for railway systems as part of a possible solution for the Future Railway Mobile Communication System (FRMCS). This communication system design is performed from a protocol-stack perspective, introducing the KPIs for the physical layer (RSRP, RSRQ and SINR), network layer (latency and jitter) and application layer (signalling communication timeout). The analysis focuses on the characterisation of signalling critical railway data to assess the data traffic behaviour in a telecommunications laboratory. Finally, the study validates the approach in a realistic railway environment. [ABSTRACT FROM AUTHOR]

In function as a service (FaaS), an application is decomposed into functions. We propose to generalize FaaS by allowing functions to alternate between remote-state and local-state phases, depending on internal and external conditions, and dedicating a container with persistent memory to functions when in a local-state phase. [ABSTRACT FROM AUTHOR]

The presence of unbalanced loads in power systems creates voltage imbalances that perturb the operation of sensitive loads, such as induction motors, power electronics converters, and adjustable speed drives. This article presents a novel control scheme that achieves accurate voltage balance at any node of an islanded ac microgrid with seamless integration with the existing hierarchical control of the microgrid. A master–slave architecture is adopted for the unbalance compensation due to its low network traffic requirements and its modular conception. The unbalance compensation follows a collaborative principle where current injections are computed as a function of the current rating of each inverter, thus avoiding the overcurrent stress of the unbalance compensation by a single inverter. This article presents the stability analysis and the design guidelines for the control parameters. The performance of the control proposal has been successfully tested in a laboratory microgrid. Selected experimental results are presented. [ABSTRACT FROM AUTHOR]

Nowadays, it is hard to see an organization without a digital presence, while our modern society relies on a wide range of activities like banking, government services, commerce, or education that are offered online. Even more, the recent years have pushed the limits of digital transformation for multiple organizations, companies, and educational institutions. This transition occurred without any prior planning or preparation and at an unprecedented scale [1]. As the globe is converging towards a technology-driven society, cyber attacks and cyber crime campaigns are blooming. Recent reports show that cyber crime is growing in severity and frequency, competing with the traditional crime in both the number of incidents and revenue [2]. [ABSTRACT FROM AUTHOR]

BGP-Multipath (BGP-M) is a multipath routing technique for load balancing. Distinct from other techniques deployed at a router inside an Autonomous System (AS), BGP-M is deployed at a border router that has installed multiple inter-domain border links to a neighbor AS. It uses the equal-cost multi-path (ECMP) function of a border router to share traffic to a destination prefix on different border links. Despite recent research interests in multipath routing, there is little study on BGP-M. Here we provide the first measurement and a comprehensive analysis of BGP-M routing in the Internet. We extracted information on BGP-M from query data collected from Looking Glass (LG) servers. We revealed that BGP-M has already been extensively deployed and used in the Internet. A particular example is Hurricane Electric (AS6939), a Tier-1 network operator, which has implemented >1,000 cases of BGP-M at 69 of its border routers to prefixes in 611 of its neighbor ASes, including many hyper-giant ASes and large content providers, on both IPv4 and IPv6 Internet. We examined the distribution and operation of BGP-M. We also ran traceroute using RIPE Atlas to infer the routing paths, the schemes of traffic allocation, and the delay on border links. This study provided the state-of-the-art knowledge on BGP-M with novel insights into the unique features and the distinct advantages of BGP-M as an effective and readily available technique for load balancing. [ABSTRACT FROM AUTHOR]

Cognitive radio networks (CRNs) can facilitate ultra-reliable communication among IoT devices in the 6G environment by enhancing channel availability (CA) for primary and secondary users. However, CA does not necessarily lead to successful connection establishment unless receiver’s accessibility (RA) is guaranteed. This motivates us to propose the notion of connection availability (CoA) that incorporates RA into CA. We also introduce the idea of service maintainability (SM) that includes the effect of RA in service retainability. Additionally, spectrum utilization efficiency (SUE) is expressed and analyzed with and without considering the impact of RA. For performance evaluation, a channel reservation algorithm with customizable configurations is proposed. Furthermore, an analytical model is used to investigate the network performance for all key performance indicators (KPIs) under multiple channel failures and PU arrival rates and determine valuable tradeoffs among KPIs. [ABSTRACT FROM AUTHOR]

The rapid and ubiquitous deployment of Internet of Things (IoT) in smart homes has created unprecedented opportunities to automatically extract environmental knowledge, awareness, and intelligence. Many existing studies have adopted either machine learning approaches or deterministic approaches to infer IoT device events and/or user activities from network traffic in smart homes. In this paper, we study the problem of inferring user activity patterns from a sequence of device events by first deterministically extracting a small number of representative user activity patterns from the sequence of device events, then applying unsupervised learning to compute an optimal subset of these user activity patterns to infer user activity patterns. Based on extensive experiments with sequences of device events triggered by 2,959 real user activities and up to 30,000 synthetic user activities, we demonstrate that our scheme is resilient to device malfunctions and transient failures/delays, and outperforms the state-of-the-art solution. [ABSTRACT FROM AUTHOR]

Internet adaptive video streaming is a typical form of video delivery that leverages adaptive bitrate (ABR) algorithms to provide video services with high quality of experience (QoE) for various users in diverse and unique network conditions. Such heterogeneous network environments, which can be viewed as exogenous input processes, often lead to the unstable performance of ABR algorithms. Unfortunately, learning-based ABR algorithm which generated by state-of-the-art reinforcement learning (RL) technologies achieves good average performance but fails to perform well in all kinds of network conditions. In this work, considering the video playback process as the Input-driven Markov Decision Process (IMDP), we propose $\text{A}^{2}$ BR (Adaptation of ABR), a novel meta-RL ABR approach. $\text{A}^{2}$ BR is mainly composed of an online stage and an offline stage. It leverages meta-RL to learn an initial meta-policy with various network conditions at the offline stage and makes decisions in personalized network conditions at the online stage. At the same time, we continually optimize the meta-policy to the tailor-made ABR policy for varying the current network environment within few shots. Moreover, in order to improve the learning efficiency, we fully utilize domain knowledge for implementing a virtual player to replay the previously experienced network. Using trace-driven experiments on various scenarios including different vehicles, users, network types, and heterogeneous user-preferences, we show that $\text{A}^{2}$ BR outperforming recent ABR approaches with rapidly adapting to the personalized QoE metrics and specific network conditions. Testbed experimental results also illustrate the superiority of $\text{A}^{2}$ BR in adapting to the unseen environments. [ABSTRACT FROM AUTHOR]

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The HT ht and I g i provide the gain of the optical filter and optical concentrator respectively. Keywords: communication coverage area; LED lighting based communication system; mobility; root mean square delay; signal to noise ratio EN communication coverage area LED lighting based communication system mobility root mean square delay signal to noise ratio 411 420 10 07/05/22 20220701 NES 220701 1 Introduction The new innovative technologies can immensely help to reduce the global warming especially related to communication networks. The total received power HT ht from all the N LEDs can be described as Graph HT ht (3) where HT ht is amount of power transmission by HT ht LED. The HT ht denotes the time delay between I i i th LED and the receiver, HT ht is the channel gain as defined earlier. [Extracted from the article]

Taking into account the dynamic nature of traffic in telecommunication networks, the robust network design problem is to fix the edge capacities so that all demand vectors belonging to a polytope can be routed. While a common heuristic for this co‐NP‐hard problem is to compute, in polynomial time, an optimal static routing, affine routing can be used to obtain better solutions. It consists in restricting the routing to affinely depend on the demands. We show that a node‐arc formulation is less conservative than an arc‐path formulation. We also provide a cycle‐based formulation that is equivalent to the node‐arc formulation. To further reduce the solution's cost, several new formulations are obtained by relaxing flow conservation constraints and aggregating demands. As might be expected, aggregation allows us to reduce the size of formulations. A more striking result is that aggregation reduces the solution's cost. [ABSTRACT FROM AUTHOR]

Social media traffic constitutes the highest percentage of Internet traffic. Such traffic is largely facilitated by mobile devices, which imposes a huge traffic load on backhaul links in 5G networks, and can in turn affect the quality of service. This traffic load can be alleviated by using vehicular networks as a traffic offloading platform. In particular, vehicles can act as a resourceful asset for edge caching, thus enabling data acquisition from nearby caching nodes rather than the remote backhaul servers. In this paper, we propose the Predictive Proactive Caching Framework (PPCF), which exploits the daily driving routine and predictable behavior of users to pre-cache the data at parked vehicles for the requesters to proactively acquire as they pass by. PPCF is composed of a cache placement module and a prediction module. The former aims at maximizing cache hits by assigning replicas to caching spots that yield maximum certainty in their spatiotemporal availability for requesters. To estimate such availability, the prediction module aims at making accurate travel time predictions by proposing the use of a Long Short-Term Memory (LSTM) network trained using particle swarm optimization (PSO-LSTM). The predicted average travel time is then used to estimate a personalized travel time for users by considering their different driving behaviors. Extensive simulations demonstrate the ability of the proposed framework to achieve significant improvements in its targeted objectives compared to other prominent caching and prediction schemes in vehicular networks. [ABSTRACT FROM AUTHOR]