Search

This article is dedicated to investigating the impulsive-based almost surely synchronization issue of neural network systems (NSSs) with quality-of-service constraints. First, the communication network considered suffers from random double deception attacks, which are modeled as a nonlinear function and a desynchronizing impulse sequence, respectively. Meanwhile, the impulsive instants and impulsive gains are randomly and only their expectations are available. Second, by taking two different types of random deception attacks into consideration, a novel mathematical model for vulnerable NSSs is constructed. Then, almost surely synchronization criteria are established by using Borel-Cantelli lemma. Furthermore, based on the derived strong and weak sufficient conditions, the almost surely synchronization of NSSs is achieved. Finally, the section of numerical example is shown to illustrate the effectiveness of the proposed method.

An innovative control method for 1-phase DSTATCOM incorporating communication network is discussed in this paper to obtain the coordinated control between the Distributed Generators and Compensators. The methodology is intended for micro grid supplying loads with feeders traversed physically distant apart from each other. The anticipated reactive compensation is constructed on confined measurement and the transmitted power through the cables. It ensures that the discussed approach minimizes the voltage level more efficiently while sustaining the voltage control even after a high-level dissemination of the DGs. The MATLAB simulations carried over thearbitary network confirm the DSTATCOM outstanding performances with different operating situations like utility side, mid of the feeder and end of the feeder. The impact of communication node breakdown is analyzed at various nodes and the significance of communication in loop control is validated through an arbitrary 5-bus system.

In this paper, we study detectability problem of Networked Discrete Event Systems (NDES) where the communication between the plant and the agent is carried out through a network that can have several channels, so that, communication delays can cause changes in the order of the observations, and, also, loss of observations. We deploy an approach previously presented in the literature to construct an untimed nondeterministic automaton that models the behavior of a NDES, and, based on this model, we present a methodology for the state estimation of NDES, and new networked D-detectability definitions. Finally, we show that the NDES is networked D-detectable if, and only if, the equivalent untimed nondeterministic model is D-detectable.

The modern electricity network has become smarter and more reliable because of the addition of information and communication technology. Dynamic thermal rating systems (DTR) allow existing transmission lines to be operated much closer to their limits. However, line outages after DTR implementation could threaten network security. Thus, system integrity protection schemes (SIPS), which are designed to ensure security, are useful to forestall impending contingencies that could arise from DTR implementation. Furthermore, synchrophasors can improve wide-area monitoring and protection capabilities. However, the reliability of these technologies considering their communication network availability is yet to be studied in a unified framework. Therefore, a novel framework of synchrophasor-based DTR and SIPS is presented while examining eight DTR network topologies and two SIPS schemes simultaneously. A sequential Monte Carlo simulation-based technique is proposed to simulate the effect of the topologies on system-wide reliability. This method is tested on the IEEE-RTS. Results show an improvement of 93.27% between DTR communication Schemes 1 and 8 in load curtailment. More importantly, the article reveals an improvement of 99.15% in the load curtailment value in scenarios where SIPS is deployed compared with the case where SIPS is not deployed, highlighting the benefits of DTR and SIPS hybrid implementation.

The consensus problem of general linear multiagent systems (MASs) is studied under switching topologies by using observer-based event-triggered control method in this article. On the basis of the output information of agents, two kinds of novel event-triggered adaptive control schemes are designed to achieve the leaderless and leader-follower consensus problems, which do not need to utilize the global information of the communication networks. Finally, two simulation examples are introduced to show that the consensus error converges to zero and Zeno behavior is eliminated in MASs. Compared with the existing output feedback control research, one of the significant advantages of our methods is that the controller protocols and triggering mechanisms do not rely on any global information, are independent of the network scale, and are fully distributed ways.

Modern network systems (unmanned aerial vehicles groups, social networks, network production chains, transport and logistics networks, communication networks, cryptocurrency networks) are distinguished by their multi-element nature and the dynamics of connections between its elements. A number of discrete problems on the construction of optimal substructures of network systems described in the form of various classes of graphs are NP-complete problems. In this case, the variability and dynamism of the structures of network systems leads to an "additional" complication of the search for solutions to discrete optimization problems. At the same time, for some subclasses of dynamical graphs, which are used to model the structures of network systems, conditions for the solvability of a number of NP-complete problems can be distinguished. This subclass of dynamic graphs includes pre-fractal graphs. The article investigates NP-complete problems on pre-fractal graphs: a Hamiltonian cycle, a skeleton with the maximum number of pendant vertices, a monochromatic triangle, a clique, an independent set. The conditions under which for some problems it is possible to obtain an answer about the existence and to construct polynomial (when fixing the number of seed vertices) algorithms for finding solutions are identified.

Though it is common in network control systems that the sensor and control signals are transmitted via a common communication network, no result is available in investigating the stabilization problem for uncertain nonlinear systems with both input and state quantization. The issue is solved in this paper, by presenting an adaptive backstepping based control algorithm for the systems with sector bounded input and state quantizers. In addition to overcome the difficulty to proceed recursive design of virtual controls with quantized states, the relation between the input signal and error state need be well established to handle the effects due to quantization. It is shown that all closed-loop signals are ensured uniformly bounded and all states will converge to a compact set. Simulation results are provided to validate the effectiveness of the proposed control scheme.

The distributed observer problem is motivated by the case where the output information of the system is decentralized in different subsystems. In this scene, all the subsystems form an observer network, and each of them has access to only a part of output information and the information exchanged via the given communication networks. Due to the limitation of communication conditions among subsystems, the communication network is often time varying and disconnected. However, the existing research about the aforementioned scene is still not enough to solve this problem. To this end, this article is concerned with the challenge of the distributed observer design for linear systems under time-variant disconnected communication networks. The design method is successfully established by fixing both completely decentralized output information and incompletely decentralized output information into account. Our work overcomes the limitation of the existing results that the distributed observer can only reconstruct the full states of the underlying systems by means of fast switching. In the case of completely decentralized output information, a group of sufficient conditions is put forward for the system matrix, and it is proved that the asymptotical omniscience of the distributed observer could be achieved as long as anyone of the developed conditions is satisfied. Furthermore, unlike similar problems in multiagent systems, the systems that can meet the proposed conditions are not only stable and marginally stable systems but also some unstable systems. As for the case where the output information is not completely decentralized, the results show with the observable decomposition and states reorganization technology that the distributed observer could achieve omniscience asymptotically without any constraints on the system matrix. The validity of the proposed design method is emphasized in two numerical simulations.

Deploying unmanned aerial vehicles (UAVs) in communication networks could achieve desirable degrees of freedom, but resulting from the high line-of-sight link attributes, security threats faced by UAV communications are serious. Taking that into consideration, we focus on a UAV swarm-enabled communication network in this article, where a fixed-wing UAV acts as a legitimate user and a neighboring UAV eavesdropper attempts to eavesdrop the confidential information forwarded by rotary-wing UAV relays. In particular, we jointly optimize transmit power on the base station and UAV relays, power allocation coefficient and beamforming on UAV relays, and trajectory of the fixed-wing UAV to maximize the average secrecy rate. Due to the nonconvexity of the optimization problem, the block coordinate descent method is utilized to solve it. Specifically, the overall optimizing problem is divided into four subproblems in which the transmit power, power allocation coefficient, beamforming vector, and the trajectory of user are optimized, respectively. By employing the successive convex approximate method, subproblems are transformed to convex problems which are numerically tractable. Numerical simulations validate the effectiveness of the proposed algorithm and show that compared with benchmark schemes, the considered transmission scheme with artificial noise and optimized network parameters can improve secrecy rate by about 13.8%.

In this article we investigates the practical fixed-time consensus tracking problem for multiple Euler–Lagrange systems, which is subject to, concurrently, stochastic packet losses in directed communication networks (DCNs) and input/output constraints. In practice, the packet losses in the communication network may lead to communication failure and control accuracy degradation. To address this impact, a novel fully distributed observer is established to estimate the leader’s states within fixed time. Subsequently, the state constraint function is introduced so that the designed adaptive control protocol can be used in a uniform structure with unconstrained or asymmetric/symmetric output constraints without changing the adaptive structure. Combining the backstepping technique with fuzzy-logic systems, an adaptive fixed-time local control protocol is constructed to drive each agent to track the leader’s states estimated by the observer. Besides, a novel auxiliary system is introduced to solve the constraint problem of the control input. Furthermore, through rigorous theoretical analysis, it is verified that all signals converge to the compact set close to zero with guaranteed fixed-time convergence rate. At last, a representative simulation is carried out to demonstrate the practicability of the proposed method.

The increasing demand for road freight has raised tremendous attentions to vehicle platooning, which reduces air resistance and improves fuel economy. To achieve a small and safe spacing between vehicles while ensuring platoon stability, wireless communication assistance is indispensable. However, the imperfection of communication brings degradation of platooning performance (i.e., spacing error) and more possibilities for adversarial attacks. This article proposes a prediction-assisted platooning mechanism from the perspective of performance optimization, wherein each vehicle establishes its local platoon model based on the information received from the communication network, thereby reducing information latency. Then, to secure the system against malicious vehicles, we carry out analysis and design of a detection algorithm for a typical attack type, i.e., data injection attack. The detection is based on three indicators: absolute spacing error, spacing prediction error, and acceleration prediction error. The advantages of the novel platooning mechanism and detection algorithm are ultimately demonstrated on a road-traffic simulation platform that considers the imperfection of realistic vehicle perceptions and Cellular-Vehicle-to-Everything (C-V2X) communication.

Wireless network is the communication foundation that supports the intelligentization of Unmanned Aerial Vehicle (UAV) swarm. The topology of UAV communication network is the key to understanding and analyzing the behavior of UAV swarm, thus supporting the further prediction of UAV operations. However, the UAV swarm network topology varies over time due to the high mobility and diversified mission requirements of UAVs. Therefore, it is important but challenging to research dynamic topology inference for tracking the topology changes of the UAV network, especially in non-cooperative manner. In this paper, we study the problem of inferring UAV swarm network topology based on external observations, and propose a dynamic topology inference method. First, we establish a sensing framework for acquiring the communication behavior of the target network over time. Then, we expand the multi-dimensional dynamic Hawkes process to model the communication event sequence in a dynamic wireless network. Finally, combining the sliding time window mechanism, the maximum weighted likelihood estimation is applied to inferring the network topology. Extensive simulation results demonstrate the effectiveness of the proposed method.

The distributed optimization problem is set up in a collection of nodes interconnected via a communication network. The goal is to find the minimizer of a global objective function formed by the addition of partial functions locally known at each node. A number of methods are available for addressing this problem, having different advantages. The goal of this work is to achieve the maximum possible convergence rate. As the first step towards this end, we propose a new method which we show converges faster than other available options. As with most distributed optimization methods, convergence rate depends on a step size parameter. As the second step towards our goal we complement the proposed method with a fully distributed method for estimating the optimal step size that maximizes convergence speed. We provide theoretical guarantees for the convergence of the resulting method in a neighborhood of the solution. Also, for the case in which the global objective function has a single local minimum, we provide a different step size selection criterion together with theoretical guarantees for convergence. We present numerical experiments showing that, when using the same step size, our method converges significantly faster than its rivals. Experiments also show that the distributed step size estimation method achieves an asymptotic convergence rate very close to the theoretical maximum.

In communication networks, the existence of fractional factors can be characterized as the feasibility of data transmission. When some nonadjacent nodes with each other are damaged and a special channel is assigned, the possibility of data transmission in a communication network is equivalent to the existence of fractional ID-factor-critical covered graph. As a consequence, the existence of fractional ID- [ a , b ] -factor-critical covered graph plays a key role in studying data transmissions of communication networks. Neighborhood and minimum degree of a graph or a network are often used to measure the vulnerability and robustness of a graph or a network, which are two important parameters considered in network design. In this article, we mainly investigate the relationship between neighborhood of independent set, minimum degree and the fractional ID- [ a , b ] -factor-critical covered graph, and acquire a neighborhood of independent set and minimum degree condition for a graph being fractional ID- [ a , b ] -factor-critical covered, which is a generalization of the previous results.

This paper considers remote state estimation in cyber-physical systems (CPSs) with multiple sensors. Each plant is modeled by a discrete-time stochastic linear system with measurements of each sensor transmitted to the corresponding remote estimator over a shared communication network when their securities are interdependent due to network induced risks. A dynamic nonzero-sum game with asymmetric information is formulated in which each sensor subject to a resource budget constraint needs to decide whether to invest in security for sending data packets, taking the behaviors of other sensors into account. To overcome the difficulty in characterizing or computing the Nash equilibria (NE), the game with asymmetric information is transformed into another game with symmetric information such that the equilibrium of the original game can be obtained by solving the equilibrium of the new game. Under certain conditions, we devise a backward induction algorithm to obtain a subclass of NE of the original game, known as common information based Markov perfect equilibria (CIBMPE). Finally, a numerical example is provided to illustrate the results obtained.

In this article, the recursive filtering problem is investigated for state-saturated complex networks (CNs) subject to uncertain coupling strengths (UCSs) and deception attacks. The measurement signals transmitted via the communication network may suffer from deception attacks, which are governed by Bernoulli-distributed random variables. The purpose of the problem under consideration is to design a minimum-variance filter for CNs with deception attacks, state saturations, and UCSs such that upper bounds on the resulting error covariances are guaranteed. Then, the expected filter gains are acquired via minimizing the traces of such upper bounds, and sufficient conditions are established to ensure the exponential mean-square boundedness of the filtering errors. Finally, two simulation examples (including a practical application) are exploited to validate the effectiveness of our designed approach.

У роботі визначена актуальність розробки та вдосконалення систем виявлення вторгнень, головним завданням яких є розпізнавання мережевих атак, спроб несанкцiонованого доступу та використання ресурсiв мережi. Ця проблема вирішується шляхом використання засобiв монiторингу, здатних аналiзувати трафiк мережі в режимi реального часу. Для цього розроблена методика оцінювання та визначення характеристик самоподібного трафіку сучасних інформаційно-комунікаційних мереж, що дозволяє формувати вектор інформативних ознак щодо визначення аномалій для системи виявлення атак. На прикладі експериментального сигналу трафіка мережі Ethernet відповідно до запропонованої методики проаналізовані та визначені характеристики мережевого трафіка. Метою роботи є обгрунтування методики комплексного оцінювання характеристик самоподібного трафіка інформаційно-комунікаційних систем і мереж щодо визначення аномалій для систем виявлення атак. Методологія вирішення поставленого завдання полягає у комплексному використанні методів час-частотного, статистичного, фрактального та мультифрактального аналізів, адекватних закономірностям сучасного трафіку інформаційно-комунікаційних мереж, що має самоподібний (фрактальний чи мультифрактальний) характер. Наукова новизна. Обгрунтована методика комплексної оцінки характеристик самоподібного трафіка інформаційно- комунікаційних мереж, що включає час-частотний, статистичний, фрактальний і мультифрактальний аналізи, яка дозволяє підвищити ймовірність визначення вторгнень для систем виявлення атак за рахунок підвищення достовірності інформативних ознак самоподібного трафіка. Висновки. Запропонована методика комплексного використання методів аналізу, адекватних закономірностям сучасного трафіку інформаційно-комунікаційних мереж, підвищує ефективність визначення аномалій за рахунок підвищення інформативності оцінюваних характеристик трафіку.

With the increasing complexity of the search environment, it is particularly important for multiunmanned aerial vehicles (UAVs) to achieve a safe and efficient search for a dynamic target. For the communication network and information fusion among multi-UAVs, we adopt the minimum spanning tree structure as the communication network, and then based on this structure, the consensus algorithm with state predictor is utilized for the fusion of predicted target probability map. Moreover, based on the traditional model predictive control (MPC), the future-dependent MPC framework is proposed to realize the cooperative trajectory optimization and obtain the optimal control input, and especially the objective function of trajectory optimization is established by considering comprehensively the communication performance and the search performance. Finally, the feasibility of our method in the multi-UAV cooperative search task is verified via simulation.

Cloud-based VANETs are designed to enable communication between high-speed vehicles. In such a highly dynamic environment, how to provide secure and anonymous communication service is a challenge. In this paper, we affirmatively address the challenge by proposing a traitor-resistant and dynamic anonymous communication framework (TD-ACF) for cloud-based VANETs. In TD-ACF, each vehicle is represented by a set of attributes and the driving data is transmitted in encrypted form. Therefore, the anonymous authentication and the confidentiality of driving data are achieved. Meanwhile, TD-ACF supports two practical requirements in cloud-based VANETs: the revocation and the traceability of traitor. For the former, TD-ACF can force a vehicle to exit the communication network at any moment. We employ an efficient binary tree algorithm to reduce the size of key updates for revocation from the traditional linear to the logarithmic level. For the latter, we overcome the barrier of the one-to-many relationship between a vehicle and the shared set of attributes to support traitor tracing. In TD-ACF, the Semi-Trusted Cloud (STC) can directly capture and punish a traitor instead of querying all the records in the list of unrevoked vehicles. The theoretical analysis and experimental simulation show that our scheme is feasible and effective.

Following recent advancements in Terahertz (THz)technology, THz communications are currently being celebrated as key enablers for various applications in future generations of communication networks. While typical communication use cases are over medium-range air interfaces, the inherently small beamwidths and transceiver footprints at THz frequencies support nano-communication paradigms. In particular, the use of the THz band for in-body and on-body communications has been gaining attention recently. By exploiting the accurate THz sensing and imaging capabilities, body-centric THz biomedical applications can transcend the limitations of molecular, acoustic, and radio-frequency solutions. In this paper, we study the use of the THz band for body-centric networks, by surveying works on THz device technologies, channel and noise modeling, modulation schemes, and networking topologies. We also promote THz sensing and imaging applications in the healthcare sector, especially for detecting zootonic viruses such as Coronavirus. Wepresent several open research problems for body-centric THz networks.

Quality of Service (QoS) in a fifth-generation communication network is leveraged through its interoperable information communication technologies. This interoperability improves the scalable and adaptable level of heterogenous users by maximizing the radio and network resources reliably. Denial of service (DoS) turns out to be a threat in granting communication quality and reliability in service responses due to periodic flooding and invariable traffic flows. This manuscript introduces a differential flow management scheme (DFMS) for the 5G communication network for thwarting the impact of DoS adversaries. This scheme classifies the request/ response flow traffic as continuous and discrete and addresses the discrete flow as a sub-optimal differential problem. In this optimization problem, the goal is to converge the time of adversary detection and to re-formulate resource allocation as a continuous flow based on the remaining flows. The invariable flow is modeled for the persistence time based on service and transmission intervals to retain the user equipment's response rate. The experimental results show the proposed scheme's consistency by achieving less adversary detection time, maximizing the request delivery ratio, and retaining the response rate, respectively.

In the past few years, the industry and academia have been working hard to establish and standardise vehicle-to-everything (V2X) communications, which is one of the vital emerging services for next generation wireless networks. Therewith, to control radio resource properly with balanced implementation complexity, a full-duplex V2V discovery mechanism in a 5G-V2X network based on a random backoff procedure is proposed to better utilize radio resources on which nearby vehicles establish their links. The main aims are to reduce complexity, latency, improve throughput and guarantee stability for V2V communication. The unemployed cellular network channel operates in full-duplex mode and leaves the channel as soon as they are informed that someone is in place through latency and throughput analysis technique. The channel sensing and identification are done in a cooperative manner before transmission. Furthermore, two effective resource distribution algorithms are proposed that grant the optimum resource distribution for V2V and V2I-Users. The detection and the throughput of full-duplex V2V communication in the proposed scheme have been formulated and the performance of the proposed scheme and validity of corresponding analysis are verified through simulation experiments.

This article studies the group coordinated control problem for distributed nonlinear multiagent systems (MASs) with unknown dynamics. Cloud computing systems are employed to divide agents into groups and establish networked distributed multigroup-agent systems (ND-MGASs). To achieve the coordination of all agents and actively compensate for communication network delays, a novel networked model-free adaptive predictive control (NMFAPC) strategy combining networked predictive control theory with model-free adaptive control method is proposed. In the NMFAPC strategy, each nonlinear agent is described as a time-varying data model, which only relies on the system measurement data for adaptive learning. To analyze the system performance, a simultaneous analysis method for stability and consensus of ND-MGASs is presented. Finally, the effectiveness and practicability of the proposed NMFAPC strategy are verified by numerical simulations and experimental examples. The achievement also provides a solution for the coordination of large-scale nonlinear MASs.

A distributed control policy is designed for a group of Euler-Lagrange (EL) agents in a leader-follower based communication network with time-varying delays. The non-singular terminal sliding mode control (NTSMC) policy is integrated with mixed-type feedback and time-varying, adaptive control parameters. The control gain and proportions of feedback with and without estimated self-delays are tuned online with fuzzy logic control (FLC). The total and maximum tracking errors of a group of EL agents are assessed to demonstrate an improvement in synchronization performance with the proposed NTSMC+FLC approach compared to the NTSMC approach with constant parameters. Simulation and experimental results of a group of Phantom Omni manipulators are presented to validate the proposed control policy.

In order to realize the reliable and safe operation of the smart railways, and provide high quality information transmission service for passengers, the railway system needs to develop innovative communication network and advanced communication technology to meet the gradually increasing service demand of multi-dimensional comprehensive information resources. The Space-Air-Ground Integrated Network (SAGIN) can provide seamless information services for land, sea, air and space users, and is an effective solution to the challenge posed by the future smart railways to the all-time, all-domain, all-air, high-reliability and high-throughput communication. This paper aims to comprehensively discuss the technical development and application examples of High-Speed Railways (HSRs) based on SAGIN. Firstly, we analysis the development of the SAGIN and the mobile communication network of the HSRs, and comprehensively discuss the single network architecture of the space-based, air-based and ground-based networks, as well as the integrated network, and discuss the application scenario and network structure of the combination of the integrated networks. At the same time, the communication services, existing problems and key technologies of the space-based, air-based and ground-based networks are discussed, and the application trend of the SAGIN in HSRs is presented. Furthermore, the application scenarios of Artificial Intelligence (AI) technologies in solving the efficient resource utilization of smart railways communication and the SAGIN are studied. Based on these technologies, we point out the research direction for the future development of AI technologies in SAGIN in HSRs communications.

This paper is the first to investigate both communication and control in traffic channel (TCH) and control channel (CCH) respectively when considering leader-follower formation keeping in UAV communication networks. In this paper, we analyze the relationship between the mutual interference and information exchange cost, and then formulate the joint channel and link selection problem as a two-way consensus game between CCH and TCH. To characterize the two-way choice of link selection, we creatively propose the generalized two-way consensus equilibrium (GTCE) to capture the stable state. Then, we prove that the formulated game has at least one pure-strategy GTCE which can maximize the UAV communication network utility. A distributed better reply based joint channel and link selection (BRJCLS) algorithm as well as two-dimensional minimum spanning tree (MST) based initialization (TMSTI) algorithm are proposed to achieve the GTCE. Simulation results are presented to show the convergence and effectiveness of the formulated two-way consensus game and proposed algorithms.

In this article, quadrotor unmanned aerial vehicles (QUAVs) are organized as a two-layer structure, where the first layer is leader QUAVs and the second layer is follower QUAVs. In this structure, only the leader QUAVs can receive the desired tracking information of position and attitude. Although the followers cannot obtain the given tracking information directly, they can obtain the corresponding information from leaders and other followers through the communication network based on the graph theory. In terms of this case, a distributed formation-containment (FC) control method is proposed to handle the related flight problems. We aim to develop a formation control for the leader QUAVs and a containment control for the follower QUAVs with the graph theory. Furthermore, a neural network (NN) technique is utilized to cope with the uncertainty of each QUAV. In order to guarantee good flight performance when tracking, the finite-time stability theorem is introduced into the control design to make each QUAV achieve satisfactory tacking performance in finite time. Finally, numerical simulations are conducted in the platform of two-layer 16 QUAVs to validate the feasibility and effectiveness of the proposed NN-based finite-time FC control.

Reconfigurable intelligent surface (RIS) has emerged as important transmission technology to improve the spectral/energy efficiency in the next-generation (beyond 5G (B5G) and 6G) wireless communication network and has numerous applications in the areas of Internet of Things (IoT) and vehicular communication systems. Thus, in this paper, we investigate the application of RIS in unmanned aerial vehicle (UAV) enabled vehicular communication system with infinite and finite block length codes, where UAV communicates with the single antenna ground vehicle in the presence of several interfering vehicles on the road. We have obtained the approximate closed-form statistics of received SINR at ground vehicle in the presence of multiple nonidentical interference links. Furthermore, we analyze the performance of the considered system in terms of the coverage probability, bit-error-rate, block error rate (BLER) and goodput. It has been shown through the numerical results that the deployment of RIS significantly improves the performance of UAV-enabled vehicular communication network, even in the presence of the direct link between the UAV and the ground vehicle. Additionally, we also investigate the impact of various channel and system parameters like practical reflection coefficients of RIS, number of RIS reflecting elements, and number of interfering vehicles on the system performance. The analytical results are corroborated with Monte Carlo simulations.

Empowered by sufficient robotics and haptic hardware at the edges of a communication network system, Tactile Internet (TI) is the upcoming transformation that will empower the control of IoT in real-time and facilitate a true paradigm shift in making a dream into a reality. However, one of the most crucial challenges in realising TI is to achieve round trip time (RTT) of 1ms or less. This RTT includes transmission, queuing, processing (operator’s end), and acknowledgement times (controlled end). Another challenge is to achieve ultra-high reliability for establishing haptic communications for TI. This paper proposes a novel fog-based traffic flow framework employing Software-Defined Networking (SDN) and Fog Computing (FC) to address these challenges. An efficient traffic flow algorithm is developed to effectively manage complex and critical traffic in the network to reduce extra processing and waiting times at each level of clouds. The SDN and FC approaches provide an effective solution to route the traffic efficiently in the system. Hence, the traffic flow paths from the master to slave sections are reduced, and consequently, RTT is also reduced. The performance of the proposed system is evaluated by an iFogSim simulator in terms of throughput, RTT, energy consumption, and reliability. The simulation results obtained show that the proposed system outperforms the existing edge, cloud, and cellular networks.

As the general development trend of the automotive industry, connected and autonomous vehicles (CAVs) can be used to increase transportation safety, promote mobility choices, reduce user costs, and create new job opportunities. However, with the increasing level of connectivity and automation, malicious users are able to easily implement different kinds of attacks, which threaten the security of CAVs. Hence, this paper provides a comprehensive survey on the cyber-security in the environment of CAVs with the aim of highlighting security problems and challenges. Firstly, based on the types of communication networks and attack objects, it classifies various cyber-security risks and vulnerabilities in the environment of CAVs into in-vehicle network attacks, vehicle to everything network attacks, and other attacks. Next, it regards cyber-risk as another type of attacks in the environment of CAVs. Then, it describes and analyzes up-to-date corresponding defense strategies for securing CAVs. In addition, it concludes several available cyber-security and safety standards of CAVs, which is helpful for the practical application of CAVs. Finally, several challenges and open problems are discussed for the future research.

Cyber-physical systems (CPSs) integrate computing and communication capabilities to monitor and control physical processes. In order to do so, communication networks are commonly used to connect sensors, actuators, and controllers in the feedback system. The use of communication networks increases the vulnerability of CPSs to cyberattacks that can drive the system to unsafe states. One of the most powerful cyberattacks is the so-called man-in-the-middle attack, where the intruder can observe, hide, create, or change information in the attacked network channels. In this article, we propose a defense strategy that can thwart man-in-the-middle attacks in the sensor and/or control communication channels of CPSs modeled as discrete-event systems. We also introduce the definition of network attack security (NA-Security), which is related to the possibility of preventing the system from reaching unsafe states by using a security supervisor, whose online implementation has polynomial computational complexity, and we propose an algorithm to verify this property.

Cybertwin leverages the capabilities of networks and serves in multiple functionalities, by identifying digital records of activities of humans and things, from the Internet of Everything (IoE) applications. Cybertwin emerges as a promising solution along with next-generation communication networks, i.e., 6G technology, however, it increases additional challenges at the edge networks. Motivated by the above-mentioned perspectives, in this paper, we introduce a new cybertwin-driven edge framework using 6G-enabled technology with an intelligent service provisioning strategy, for supporting a massive scale of IoE applications. The proposed strategy distributes the incoming tasks from IoE applications using the Deep Reinforcement Learning technique based on their dynamic service requirements. Besides that, an Artificial Intelligence-driven technique, i.e., the Support Vector Machines (SVM) classifier model is applied at the edge network to analyze the data and achieve high accuracy. The simulation results over the real-time financial datasets demonstrate the effectiveness of the proposed service provisioning strategy and SVM model over the baseline algorithms in terms of various performance metrics. The proposed strategy reduces the energy consumption by 15% over the baseline algorithms, while increasing the prediction accuracy by 12% over the classification models.

We study the relation between latency and alphabet size in the context of Multicast Network Coding. Given a graph $G =$ (${V}$, E) representing a communication network, a subset $S \subseteq V$ of sources, each of which initially holds a set of information messages, and a set $ T\subseteq V$ of terminals; we consider the problem in which one wishes to design a communication scheme that eventually allows all terminals to obtain all the messages held by the sources. In this study we assume that communication is performed in rounds, where in each round each network node may transmit a single (possibly encoded) information packet on any of its outgoing edges. The objective is to minimize the communication latency, i.e., number of communication rounds needed until all terminals have all the messages of the source nodes.For sufficiently large alphabet sizes (i.e., large block length, packet sizes), it is known that traditional linear multicast network coding techniques (such as random linear network coding)) minimize latency. In this work we seek to study the task of minimizing latency in the setting of limited alphabet sizes $(\mathrm {i}.\mathrm {e}.,$ finite block length), and alternatively, the task of minimizing the alphabet size in the setting of bounded latency. Through reductive arguments, we prove that it is NP-hard to (i) approximate (and in particular to determine) the minimum alphabet size given a latency constraint; (ii) to approximate (and in particular to determine) the minimum latency of communication schemes in the setting of limited alphabet sizes.

This article investigates the task assignment problem in which multiple dispersed robots need to visit a set of target locations while trying to minimize the robots' total travel distance. Each robot initially has the position information of all the targets and of those robots that are within its limited communication range, and each target demands a robot with some specified capability to visit it. We propose a decentralized auction algorithm which first employs an information consensus procedure to merge the local information carried by each communication-connected (CC) robot subnetwork. Then, we apply a marginal-cost-based strategy to construct conflict-free target assignments for the CC robots. When the communication network of the robots is not connected, we demonstrate that the robots' total travel distance might in fact increase when their communication range grows, and more importantly, such a somewhat counterintuitive fact holds for a range of algorithms. Furthermore, the proposed algorithm guarantees that the total travel distance of the robots is at most twice of the optimal when the communication network is initially connected. Finally, Monte Carlo simulation results demonstrate the satisfying performance of the proposed algorithm.

The Internet of Vehicles (IoV) has always attracted attention as the emerging communication network with the most development potential in the 5G era. However, the performance of IoV under 5G ultra-dense networks is an open issue, especially in practice the outage probability and ergodic capacity of the relay cooperative IoV network under aggregate interference are still unclear. Therefore, an opportunistic Decoding and Forwarding (DF) relay cooperative transmission algorithm was proposed in this paper when the destination node of IoV has aggregated interference. In addition, based on mathematical theoretical knowledge such as numerical analysis, the closed expressions of the outage probability and ergodic capacity of the IoV system under aggregated interference was derived. Finally, simulation experiments verify the effectiveness of the proposed scheme and the correctness of the theoretical analysis, which improves the transmission rate of the system.

In order to satisfy timeliness requirements arising from environmental sensing applications, age of information (AoI) was proposed to characterise the freshness of the received updates. In this paper, we consider a wireless powered relay aided communication network (WPRCN), in which a relay wirelessly powered by a hybrid-access point (H-AP) receives environmental monitoring information update from a sensor and forwards it to the H-AP. The long-term average AoI is studied since the decision correctness depends on the update timely uploaded by the relay. The wireless powered relay adopts either a decode-and-forward (DF) or an amplify and forward (AF) protocols, respectively, subject to the energy causality. We also consider the decoding cost of DF protocol owing to the relay’s limited energy storage. Since the expression of the average AoI is non-elementary, we propose a Taylor approximation based algorithm to obtain its integral. We optimise the transmit power/the equivalent average power consumption of the relay for the sake of minimising the average AoI in the whole WPRCN with different forwarding protocols. Our simulation results demonstrate the accuracy of the theoretical analysis, while the average AoI of the WPRCN is optimised by our power allocation scheme at the relay. The proposed algorithm is verified to achieve great approximation effect.