Realistic Communication Networks Research Articles

IEEE 802.1 Zaman Hassas Araç-İçi Haberleşme Ağları için Siber Güvenlik: Güncel Gelişmeler ve Hizmet Reddi Saldırılarının Etki Analizi

Dedicated computers control the operation of Cyber Physical Systems such as in-vehicle architectures with the help of sensors and actuators. Securing the real-time communication technologies for in-vehicle platforms attracts the attention of the research community. IEEE 802.1 Time Sensitive Networking (TSN) task group targets the standardization of Ethernet-based deterministic communication technologies due to its high bandwidth and low cost. IEEE P802.1DG working group specifies the profile for secure and real-time automotive embedded systems relying on TSN, which is envisioned to be widely used in future in-vehicle platforms. In this paper, we present an elaborate review of the research work on the security of in-vehicle communication networks with TSN support, and define various Denial of Service (DoS) attack scenarios targeting the real-time traffic in in-vehicle networks. We evaluate the impact of our attack scenarios on the performance of two different realistic in-vehicle communication networks with varying sizes. Experimental results show that DoS attacks can lead to severe consequences disrupting the healthy operation of safety-critical functions in a TSN-compliant in-vehicle network.

Coordinated Control of Virtual Power Plants to Improve Power System Short-Term Dynamics

The paper proposes a coordinated frequency control strategy for Virtual Power Plant (VPPs), with the inclusion of Distributed Energy Resource (DERs), e.g., Solar Photo-Voltaic Generation (SPVG), Wind Generator (WG) as well as Energy Storage System (ESS). The objective is to improve the short-term dynamic response of the overall power system. The robustness of the proposed control is evaluated through a Monte Carlo analysis and a detailed modeling of stochastic disturbances of loads, wind speed, and solar irradiance. The impact of communication delays of a variety of realistic communication networks with different bandwidths is also discussed and evaluated. The case study is based on a modified version of the WSCC 9-bus test system with inclusion of a VPP. This is modeled as a distribution network with inclusion of a variety of DERs.

Evolution of entanglement in quantum neural network

This study concerns with the evolution of entanglement in a quantum neural network (QNN) model that is locally in contact with data environments. As a valuable resource, duration of entanglement in quantum systems is extremely important. Therefore, the effect of various initial states on the occurrence or decay of entanglement are examined in the presence of information reservoirs. In this study, central spin model was investigated as a quantum version of neural networks inspired by biological models. The architecture of the model is based on a central spin system with two nodes where the nodes are coupled to independent spin baths. Numerical results show that initial state preparation has a profound effect on the fate of entanglement. The results show that the entanglement lifetime can be adjusted by engineering the reservoir states as well as the initial states of the system of interest. The results can be used to improve the performance of the formation or distribution of entanglement in realistic communication network states.

A Gossip-Based Distributed Algorithm for Economic Dispatch in Smart Grids With Random Communication Link Failures

Motivated by the fact that gossip algorithms are asynchronous and suitable for the sensor networks with random link failures, a gossip-based distributed algorithm for the economic dispatch problem in smart grids is addressed in this paper. Random communication link failure phenomena is very common in a realistic communication network. By modeling a network as a Bernoulli network, the proposed algorithm can solve the economic dispatch problem in the imperfect communication network. Mean square consensus and almost sure consensus are adopted to analyze the convergence of the algorithm. Using matrix theory and perturbation theory, we prove that the algorithm can achieve mean square consensus and almost sure consensus . Therefore, the proposed algorithm converges to the optimal solution of the economic dispatch problem with probability 1. The convergence speed of the algorithm is discussed and simulation results show that the convergence rate is governed by the second largest eigenvalue of the system matrix. Case studies are designed to illustrate the effectiveness and plug-and-play capability of the algorithm.

Fate of Entanglement for Initial Separable States in Quantum Neural Network

This study is related to the fate of entanglement for initial separable states in a quantum neural network (QNN) model, which is in contact with the data environments locally. The duration of entanglement in quantum systems becomes extremely important when we consider it as a valuable resource. Thus, the effects of various initial states on the occurrence or decay of entanglement are investigated in the presence of information reservoirs. Especially in this study, central spin model has been examined as a quantum version of neural networks by taking inspiration from the biological models. Our model consists of a central spin system with two nodes to which the nodes are coupled to independent spin baths. Numerical results clearly show that different initial states have a profound effect on the fate of the entanglement. It also shows that the entanglement lifetime can be adjusted by regulating the reservoir states. The results can be used in realistic communication network situations to improve the performance of entanglement formation or distribution.

Neural-network-based containment control of nonlinear multi-agent systems under communication constraints

Purpose The purpose of this paper is to investigate the neural-network-based containment control of multi-agent systems with unknown nonlinear dynamics. Moreover, communication constraints are taken into account to reflect more realistic communication networks. Design/methodology/approach Based on the approximation property of the radial basis function neural networks, the control protocol for each agent is designed, where all the information is exchanged in the form of sampled data instead of ideal continuous-time communications. Findings By utilizing the Lyapunov stability theory and the Lyapunov–Krasovskii functional approach, sufficient conditions are developed to guarantee that all the followers can converge to the convex hull spanned by the stationary leaders. Originality/value As ideal continuous-time communications of the multi-agent systems are very difficult or even unavailable to achieve, the neural-network-based containment control of nonlinear multi-agent systems is solved under communication constraints. More precisely, sampled-data information is exchanged, which is more applicable and practical in the real-world applications.

Synchronization for non-uniform sampling networked rigid bodies

This paper investigates the synchronization problem of multiple rigid bodies under directed communication topology. Unlike the existing designs relying on continuous-time information exchanges, a novel sampled-data networking strategy is introduced, which is more reliable and practical in realistic communication networks. Specifically, we focus on the analysis of continuous-time rigid body dynamics with non-uniform discrete-time information exchanges in the communication network. In particular, the sampling period is assumed to be time-varying and governed by a Markov chain. Furthermore, the relative angular velocity information between neighbouring rigid bodies is not required, such that the communication network burden can be further reduced. Another important and distinct feature of our synchronization scheme is that the communication energy consumption can be effectively decreased, since the communication network is utilized more efficiently. Based on model transformation and stochastic stability analysis, sufficient conditions are established to guarantee that the synchronization can be achieved. Finally, a numerical example is provided to show the effectiveness and benefits of our proposed results.

A universal quantum information processor for scalable quantum communication and networks.

Entanglement provides an essential resource for quantum computation, quantum communication, and quantum networks. How to conveniently and efficiently realize the generation, distribution, storage, retrieval, and control of multipartite entanglement is the basic requirement for realistic quantum information processing. Here, we present a theoretical proposal to efficiently and conveniently achieve a universal quantum information processor (QIP) via atomic coherence in an atomic ensemble. The atomic coherence, produced through electromagnetically induced transparency (EIT) in the Λ-type configuration, acts as the QIP and has full functions of quantum beam splitter, quantum frequency converter, quantum entangler, and quantum repeater. By employing EIT-based nondegenerate four-wave mixing processes, the generation, exchange, distribution, and manipulation of light-light, atom-light, and atom-atom multipartite entanglement can be efficiently and flexibly achieved in a deterministic way with only coherent light fields. This method greatly facilitates the operations in quantum information processing, and holds promising applications in realistic scalable quantum communication and quantum networks.

Solving Multicommodity Capacitated Network Design Problems Using Multiobjective Evolutionary Algorithms

Evolutionary algorithms can be applied to a variety of constrained network communication problems with centric type models. This paper shows that with real-world complex network communication problems of this type, sophisticated statistical search is required. This situation occurs due to the fact that these optimization problems are at least NP-complete. In order to appreciate the formal modeling of realistic communication networks, historical network design problems are presented and evolved into more complex real-world models with associated deterministic and stochastic solution approaches discussed. This discussion leads into the design of an innovative multiobjective evolutionary algorithm (MOEA) to solve a very complex network design problem variation called the multicommodity capacitated network design problem (MCNDP). This variation represents a hybrid real-world communication architecture as reflected in real-world network centric models with directional communications, multiple objectives including costs, delays, robustness, vulnerability, and operating reliability within network constraints. Nodes in such centric systems can have multiple and varying link capacities as well as rates and information (commodity) quantities to be sent and received. Each commodity can also have independent prioritized bandwidth and spectrum requirements. The nondominated sorting genetic algorithm (NSGA-II) is selected as the MOEA framework which is modified and parallelized to solve the generic MCNDP. Since the MCNDP is highly constrained but with an enormous number of possible network communication topologies, a novel initialization procedure and mutation method are integrated resulting in reduced search space. Empirical results and analysis indicate that effective topological Pareto solutions are generated for use in highly constrained, communication-based network design.

Abrupt transition to complete congestion on complex networks and control

Previous works on traffic-flow dynamics on complex networks have mostly focused on continuous phase transition from a free-flow state to a locally congested state as a parameter, such as the packet-generating rate, is increased through a critical value. Above the transition point congestion occurs on a small subset of nodes. Utilizing a conventional traffic-flow model based on the packet birth-death process and more importantly, taking into account the fact that in realistic networks nodes have only finite buffers, we find an abrupt transition from free flow to complete congestion. Slightly below the transition point, the network can support the maximum amount of traffic for some optimal value of the routing parameter. We develop a mean-field theory to explain the surprising transition phenomenon and provide numerical support. Furthermore, we propose a control strategy based on the idea of random packet dropping to prevent/break complete congestion. Our finding provides insights into realistic communication networks where complete congestion can occur directly from a free-flow state without any apparent precursor, and our control strategy can be effective to restore traffic flow once complete congestion has occurred.

Guest Editorial Optimization of MIMO Transceivers for Realistic Communication Networks: Challenges and Opportunities

Guest Editorial Optimization of MIMO Transceivers for Realistic Communication Networks: Challenges and Opportunities

Special issue on optimization of MIMO transceivers for realistic communication networks: Challenges and opportunities

Special issue on optimization of MIMO transceivers for realistic communication networks: Challenges and opportunities

Special issue on optimization of MIMO transceivers for realistic communication networks: Challenges and opportunities

Special issue on optimization of MIMO transceivers for realistic communication networks: Challenges and opportunities

Special issue on optimization of MIMO transceivers for realistic communication networks: Challenges and opportunities

Special issue on optimization of MIMO transceivers for realistic communication networks: Challenges and opportunities

Special issue on optimization of MIMO transceivers for realistic communication networks: Challenges and opportunities

Special issue on optimization of MIMO transceivers for realistic communication networks: Challenges and opportunities

Calls for papers on optimization of mimo transceivers for realistic communication networks: challenges and opportunities

Calls for papers on optimization of mimo transceivers for realistic communication networks: challenges and opportunities

Concepts of Exact QoS Routing Algorithms

The underlying concepts of an exact QoS routing algorithm are explained. We show that these four concepts, namely 1) nonlinear definition of the path length; 2) a /spl kappa/-shortest path approach; 3) nondominance; and 4) look-ahead, are fundamental building blocks of a multiconstrained routing algorithm. The main reasons to consider exact multiconstrained routing algorithms are as follows. First, the NP-complete behavior seems only to occur in specially constructed graphs, which are unlikely to occur in realistic communication networks. Second, there exist exact algorithms that are equally complex as heuristics in algorithmic structure and in running time on topologies that do not induce NP-complete behavior. Third, by simply restricting the number /spl kappa/ of paths explored during the path computation, the computational complexity can be decreased at the expense of possibly loosing exactness. The presented four concepts are incorporated in SAMCRA, a self-adaptive multiple constraints routing algorithm.

Simulation and theoretical results on cluster management and directory management in dynamic hierarchical networks

A cluster management scheme for dynamic networks, the purpose of which is to maintain the cluster structure of the hierarchical network as a balanced-tree topology is presented. The theoretical time complexity bounds of the cluster management scheme for node birth and death are derived. The effects of the cluster management on gate-connected fixed-node networks under heavy intercluster traffic situations are discussed. In order to show that the scheme can handle realistic communication networks, routing tables and OD pair shortest path routing are used. The settle-down time, throughput, and end-to-end link delays of a network that uses cluster management and a network of the same topology that only uses flooding are compared. >

Implementation of a resonant cavity package on MIMD computers

The task of calculating the lowest few eigenvalues and eigenvectors of Maxwell's equations for a resonating cavity of moderately complicated shape easily exhausts the capacity of present-day computers. Massively parallel MIMD-systems would provide the necessary memory size and computing power, but the problems of implementing the algorithms are not solved yet. The present paper studies some ways of partitioning the computation for an existing software package and evaluates the resulting requirements for memory size, communication capabilities and computing power. It turns out that for realistic communication networks the minimal size of a subtask that can efficiently be treated by one processor is quite large, thus placing a limit to the reasonable amount of parallelism for a given problem size.