Communication Network Topology Research Articles

"Extending Amdahl's and Gustafson Baris's Laws by Adding Communication Overheads"

The extension of Amdahl's law and Gustafson-Barsis' law presented in this article provides insight into the relationship between communication costs and network topology in parallel computing. By considering communication latency in parallel computation execution time, these extensions can help researchers and developers optimize the interconnected network architecture of processing nodes and improve the performance of parallel computing systems. Today, parallel computer systems consisting of hundreds and thousands of processing nodes based on multiprocessor chip technology, high-speed optical transmission such as supercomputers are being researched, developed, and applied in many fields many areas. Although chip technology has progressed to the 3 nm process, the network architecture connecting processing nodes continues to be a problem that greatly affects the communication delay in the parallel computation time of the applications. In this paper, extensions of Amdahl's law and Gustafson-Barsis' law are presented with the addition of communication costs depending on the topology of the topology. These extensions provide insight into the relationship between communication costs and network topology in parallel computing. By considering communication latency in parallel computation execution time, these extensions can help researchers and developers optimize the interconnected network architecture of processing nodes and improve the performance of parallel computing systems.

Observer‐based event‐triggered consensus control of nonlinear cyber‐physical systems under backlash‐like hysteresis and denial‐of‐service attacks

AbstractIn this article, we investigate the problem of event‐triggered distributed consensus control for a class of complex cyber‐physical systems (CPSs), where unknown backlash‐like hysteresis inputs and denial‐of‐service (DoS) attacks are considered in actuators and communication channels, respectively. In the first place, when communication networks are subjected to malicious DoS attacks, the connection weights of network communication topologies experience disruptions. To solve this issue, this work proposes a solution by constructing topological models for communication networks employing switching topologies and utilizing observers to estimate the combined measurement errors during the time interval of DoS attacks. Secondly, to address the repercussions of DoS attacks on control units and to optimize the utilization of communication resources, a distributed control protocol based on an event‐triggered scheme and employing locally deployed estimators is designed to significantly decrease the update frequency of control signals. Meanwhile, utilizing Lyapunov stability theory, the boundedness of all signals in the closed‐loop system is validated, thereby accomplishing the goal of consensus control. At last, the efficacy of the proposed control scheme is demonstrated through two simulation examples.

Delay and Energy Efficient Offloading Strategies for an IoT Integrated Water Distribution System in Smart Cities

In the fast-moving world of information and communications technologies, one significant issue in metropolitan cities is water scarcity and the need for an intelligent water distribution system for sustainable water management. An IoT-based monitoring system can improve water distribution system management and mitigate challenges in the distribution network networks such as leakage, breakage, theft, overflow, dry running of pumps and so on. However, the increase in the number of communication and sensing devices within smart cities has evoked challenges to existing communication networks due to the increase in delay and energy consumption within the network. The work presents different strategies for efficient delay and energy offloading in IoT-integrated water distribution systems in smart cities. Different IoT-enabled communication network topology diagrams are proposed, considering the different water network design parameters, land cover patterns and wireless channels for communication. From these topologies and by considering all the relevant communication parameters, the optimum communication network architecture to continuously monitor a water distribution network in a metropolitan city in India is identified. As a case study, an IoT design and analysis model is studied for a secondary metropolitan city in India. The selected study area is in Kochi, India. Based on the site-specific model and land use and land cover pattern, delay and energy modeling of the IoT-based water distribution system is discussed. Algorithms for node categorisation and edge-to-fog allocation are discussed, and numerical analyses of delay and energy models are included. An approximation of the delay and energy of the network is calculated using these models. On the basis of these study results, and state transition diagrams, the optimum placement of fog nodes linked with edge nodes and a cloud server could be carried out. Also, by considering different scenarios, up to a 40% improvement in energy efficiency can be achieved by incorporating a greater number of states in the state transition diagram. These strategies could be utilized in implementing delay and energy-efficient IoT-enabled communication networks for site-specific applications.

Byzantine-Resilient Second-Order Consensus in Networked Systems.

This article studies the second-order consensus problem in networked systems containing the so-called Byzantine misbehaving nodes when only an upper bound on either the local or the total number of misbehaving nodes is known. The existing results on this subject are limited to malicious/faulty model of misbehavior. Moreover, existing results consider consensus among normal nodes in only one of the two states, with the other state converging to either zero or a predefined value. In this article, a distributed control algorithm capable of withstanding both locally bounded and totally bounded Byzantine misbehavior is proposed. When employing the proposed algorithm, the normal nodes use a combination of the two relative state values obtained from their neighboring nodes to decide which neighbors should be ignored. By introducing an underlying virtual network, conditions on the robustness of the communication network topology for consensus on both states are established. Numerical simulation results are presented to illustrate the effectiveness of the proposed control algorithm.

A Generic Algorithm Framework for Distributed Optimization Over the Time-Varying Network With Communication Delays

In this paper, we study the distributed optimization problem (DOP) over the time-varying communication network topology with communication delays. The existing distributed optimization algorithms (DOAs) cannot be directly applied to DOPs over the time-varying network with communication delays. In this paper, by adding new virtual agents and redefining communication links, some delay properties are realized and existing DOAs can then be reused to solve the DOPs. Thus, a generic algorithm framework is established which can apply to many important DOAs, such as the mirror descent algorithm and dual averaging algorithm, to solve DOPs over the time-varying network with communication delays. The convergence results of the DOAs are established and verified under the generic algorithm framework.

Distributed Model Predictive Control via Separable Optimization in Multiagent Networks

We present a distributed model predictive control method which enables a group of agents to compute their control inputs locally, while communicating with their neighbors over a communication network. While many distributed model predictive control methods require a central station for some coordination or computation of the optimization variables, our method does not require a central station, making our approach applicable to a variety of communication network topologies. With our method, each agent solves for its control inputs without solving for the control inputs of other agents, allowing for efficient optimization by each agent, unlike some other distributed methods. Further, our method attains linear convergence to the optimal control inputs in convex model predictive control problems, improving upon the sub-linear convergence rates provided by some other distributed methods such as dual decomposition methods. Moreover, our algorithm provides a closed-loop controller for convex model predictive control problems with affine constraints. We demonstrate our method in both convex and non-convex model predictive control problems in wireless transceiver alignment and satellite deployment, where we show robustness of our method to time delays.

Distributed economic dispatch algorithm in smart grid based on event-triggered and fixed-time consensus methods

In this paper, a distributed economic dispatch algorithm based on event-triggered and fixed-time consensus is proposed for optimal dispatching among generators in smart grid. Aiming to quickly achieve economic dispatch in distributed smart grid and significantly reduce the frequency of communication between generators, the algorithm can address the supply–demand balance between the generation side and the consumption side, as well as the capacity constraints of the generators. Different from the relevant work, the proposed incremental cost can converge in fixed-time and the settling time is independent of the initial state of the generators. It will provide more flexibility for the system. Meanwhile, a fully distributed event-triggered strategy is adopted, which can reduce the interactive information between generators and avoid the waste of communication resources. Furthermore, the proposed algorithm is robust to the time-varying communication network topology. Finally, several examples are presented that validate the effectiveness of the algorithm.

Social identity bias and communication network clustering interact to shape patterns of opinion dynamics.

Social influence aligns people's opinions, but social identities and related in-group biases interfere with this alignment. For instance, the recent rise of young climate activists (e.g. 'Fridays for Future' or 'Last Generation') has highlighted the importance of generational identities in the climate change debate. It is unclear how social identities affect the emergence of opinion patterns, such as consensus or disagreement, in a society. Here, we present an agent-based model to explore this question. Agents communicate in a network and form opinions through social influence. The agents have fixed social identities which involve homophily in their interaction preferences and in-group bias in their perception of others. We find that the in-group bias has opposing effects depending on the network topology. The bias impedes consensus in highly random networks by promoting the formation of echo chambers within social identity groups. By contrast, the bias facilitates consensus in highly clustered networks by aligning dispersed in-group agents across the network and, thereby, preventing the formation of isolated echo chambers. Our model uncovers the mechanisms underpinning these opposing effects of the in-group bias and highlights the importance of the communication network topology for shaping opinion dynamics.

Distributed region reaching consensus control for uncertain networked multi‐robot systems

AbstractThe main objective of this paper is to advance the concept of region reaching control for a single robot to the study of region reaching consistency for uncertain fully‐actuated multi‐robot systems formulated by Lagrange dynamics. By introducing a directed network communication topology and utilizing the pinning‐like control methods, a distributed region reaching consensus control strategy is proposed. The desired region shape can be specified by selecting an appropriate potential objective function. A feature of the developed region reaching consensus algorithm is fully distributed, which is also an important generalization of pinning‐like set point tracking scheme based on the typical proportion‐integration‐differentiation control scheme. More importantly, the scope of consistency in this paper can be explicitly defined, which is different from traditional consistency control algorithms. Subsequently, numerical examples are presented to illustrate the performance of the designed controllers.

Robust consensus tracking control for switched multiple Lagrangian systems by UDE-based control method

The robust consensus tracking control of the switched multiple Lagrangian systems is investigated by the uncertainty and disturbance estimator (UDE)-based control method. Firstly, the switching control method based on the average dwell time (ADT) algorithm is utilized for parameters and network communication topology switching of multiple Lagrangian systems to reduce the impact of load variation and enhance robustness. Then, in two parts, a proposed adaptive UDE-based control method for multiple Lagrangian systems with lumped uncertainty and load variation. In the initial stage, a consensus tracking control of the nominal switched multiple Lagrangian systems is realized via an adaptive backstepping tracker. The UDE control method is integrated and used in the second stage for the exact adjustment of model uncertainties and disturbances as well as asymptotic estimates. It is worth noting that by designing different filters for UDE, it is possible to handle several specified external disturbances that are unbounded. The unified analysis framework of the closed-loop switched control system is realized through Lyapunov analysis. Therefore, the proposed consensus tracking algorithm has a simple structure and good robustness in handling load changes, model uncertainties, and external disturbances. Finally, illustrative examples with computer simulation are provided to verify the effectiveness and correctness of the obtained results.

Deep Graph Embedding for IoT Botnet Traffic Detection

Botnet attacks have mainly targeted computers in the past, which is a fundamental cybersecurity problem. Due to the booming of Internet of things (IoT) devices, an increasing number of botnet attacks are now targeting IoT devices. Researchers have proposed several mechanisms to avoid botnet attacks, such as identification by communication patterns or network topology and defence by DNS blacklisting. A popular direction for botnet detection currently relies on the specific topological characteristics of botnets and uses machine learning models. However, it relies on network experts’ domain knowledge for feature engineering. Recently, neural networks have shown the capability of representation learning. This paper proposes a new approach to extracting graph features via graph neural networks. To capture the particular topology of the botnet, we transform the network traffic into graphs and train a graph neural network to extract features. In our evaluations, we use graph embedding features to train six machine learning models and compare them with the performance of traditional graph features in identifying botnet nodes. The experimental results show that botnet traffic detection is still challenging even with neural networks. We should consider the impact of data, features, and algorithms for an accurate and robust solution.

Optimization of Communication Network for Distributed Control of Wind Farm Equipped With Energy Storages

Consensus based distributed control schemes have been widely applied to different operation scenarios of wind farms (WFs). However, few studies concern the influences of WF communication network topology to the consensus control performances. This paper proposes an optimal design method of the WF communication network for the consensus based re/active power regulation control of the WF, in which each individual wind turbine (WT) is equipped with an energy storage (ES) unit. Firstly, the basic model of the WT equipped with ES is presented, and the consensus based control scheme of the power regulation is described. Secondly, the optimization model for the communication network is constructed, so as to speed up the convergence rate, improve the communication survivability and reduce the communication links in the control. Thirdly, this paper proposes a bi-level alternating direction method of multipliers (ADMM) based solving framework, which decomposes the optimization into sub-problems. Finally, the performances of the control using the designed optimal communication network are tested and compared with other networks in the control, and the results verify the superiority of the proposed method.

Recent Advancements in IoT Implementation for Environmental, Safety, and Production Monitoring in Underground Mines

Internet of Things (IoT) technology has been widely used for real-time monitoring of the environment, safety and production in underground mines. This paper presents the basic structure of a Mine Internet of Things (MIoT) system based on a widely used three-layer IoT architecture, classifies types of sensors commonly used in underground mines by specific application, and introduces available wired and wireless communication technologies and network topologies that can be applied in underground mines. This paper provides a comprehensive review of recent developments in IoT applications in underground mines to monitor various environmental parameters, including mine gas and dust concentrations, temperature, humidity and airflow, groundwater, ground support and seismic activity. MIoT applications for fire and hazard detection, personnel and equipment positioning, and production safety management have also been investigated. This paper highlights key challenges for the broad application of IoT technology in underground mines such as operation disruption, additional investment, limited battery life, poor quality of underground communication, and difficulty in data management. Further research on novel advanced techniques, such as self-powered sensors, MIoT standardization and underground wireless communication technologies, is essential to improve the applicability and effectiveness of IoT applications in underground mines.

Detecting CAN overlapped voltage attacks with an improved voltage-based in-vehicle intrusion detection system

The in-vehicle voltage-based intrusion detection system (VIDS) can recognize the identity of the ECUs and detect the physical layer attacks that cannot be detected by the content-based or frequency-based IDS. However, recent research in VIDS has discovered an overlapped voltage attack that can bypass the existing VIDS by generating overlapped voltage signals from two compromised ECUs to distort the fingerprint of the target ECU. The existing solutions need to change the communication protocol or network topology to mitigate the attack, which may involve additional development costs or scheduling mechanisms to coordinate the tasks. In this paper, motivated by not changing the network topology and protocols, we propose an improved VIDS that can efficiently detect the overlapped voltage attack of the in-vehicle CAN network. Before retraining the VIDS classifier model, we add the mechanism for detecting and filtering overlapped signals in the voltage samples by applying the Long Short-Term Memory (LSTM) autoencoder network to preserve the fingerprint characteristics of the benign waveform in the original samples. First, we establish an LSTM autoencoder model with benign voltages collected in a usual scenario and then apply the automatic thresholding approach to obtain the threshold to separate the overlapped and benign voltages. Next, we filter the overlapped voltages and extract the fingerprint features from the filtered voltage samples to predict the classification of the masquerade CAN packet during the attack. Finally, we evaluate the performance and attack success rate of the proposed VIDS on a CAN bus testbench. The results show that the proposed VIDS can effectively detect the overlapped voltage attack and identify the compromised ECU mimicking the distorted voltage fingerprints with an accuracy of 99.4%. The attack success rate of the proposed VIDS drops to 0∼18%, which is about 60∼95% for the existing VIDS.

Collision-Risk-Based Event-Triggered Optimal Formation Control for Mobile Multiagent Systems Under Incomplete Information Conditions

This article deals with collision-risk-based event-triggered optimal formation control problems for mobile multiagent systems. First, several collision-risk-related definitions, such as collision-free margin, moving direction angle, collision risk angle, and collision risk level, are proposed for the moving agents. Then, a collision-risk dependent, time-varying, event-triggered heterogeneous communication network topology is developed, where the agent starts obtaining information of the neighboring agents only when collision risks occur among them. Third, an anti-collision control law, which is composed of a switch function, a control force direction function, and a control strength function, is designed to guarantee the collision avoidance formation of multiagents. Fourth, to ensure the formation quality and save control cost of the multiagent system, an optimal formation control scheme with feedforward compensation is designed. Simulation results illustrate that: 1) by using the collision risk information of mobile agents, the proposed control scheme is effective to realize the collision avoidance optimal formation task and 2) the anti-collision formation controller can be implemented with incomplete information of the agents.

Survey on Application of Communication Network Topology Simulation Technology

As an important part of the study of communication networks, network topology clearly reflects many structural features of communication networks from the physical level and is a major research hotspot in this field. Simulation techniques are becoming more and more widely used in the study of communication networks as computer technology advances and becomes more convenient and intuitive. This paper collates the current research results on the topology of communication networks and the effectiveness of the applications of simulation technology, reviews the current status of research in related fields at home and abroad, and summarises the application of simulation technology in topology research to provide some reference for subsequent researchers in related fields.

Communication delay-aware cooperative adaptive cruise control with dynamic network topologies-a convergence of communication and control

Wireless communication-enabled Cooperative Adaptive Cruise Control (CACC) is expected to improve the safety and traffic capacity of vehicle platoons. Existing CACC considers a conventional communication delay with fixed Vehicular Communication Network (VCN) topologies. However, when the network is under attack, the communication delay may be much higher, and the stability of the system may not be guaranteed. This paper proposes a novel communication Delay Aware CACC with Dynamic Network Topologies (DADNT). The main idea is that for various communication delays, in order to maximize the traffic capacity while guaranteeing stability and minimizing the following error, the CACC should dynamically adjust the VCN network topology to achieve the minimum inter-vehicle spacing. To this end, a multi-objective optimization problem is formulated, and a 3-step Divide-And-Conquer sub-optimal solution (3DAC) is proposed. Simulation results show that with 3DAC, the proposed DADNT with CACC can reduce the inter-vehicle spacing by 5%, 10%, and 14%, respectively, compared with the traditional CACC with fixed one-vehicle, two-vehicle, and three-vehicle look-ahead network topologies, thereby improving the traffic efficiency.

Intermittent output tracking control of heterogeneous multi-agent systems over wide-area clustered communication networks

In this paper, an output tracking problem is investigated for a wide-area heterogeneous multi-agent system with multiple leaders through intermittent communication, where the communication network consists of clustered subnetworks distributed in a wide area. Firstly, a hierarchical clustered network model is built to describe the communication network topology of heterogeneous agents communicated by an intermittent communication mechanism. Subsequently, dynamic compensators are designed for the heterogeneous agents to estimate the leaders’ state information and a dynamic state feedback hybrid control strategy is designed for each agent to ensure the output tracking consensus. Furthermore, the proposed hybrid control strategy is extended to a case where the complete state information of the agent itself is also not available. Then, under the proposed hybrid control strategy, the closed-loop tracking error system is modeled by a switched system. Consequently, sufficient conditions are derived for the stability of the closed-loop tracking error system under the proposed hybrid control strategy by using a piecewise Lyapunov function and convex synthesis methods. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed hybrid control strategy.

Distributed Multirobot Task Assignment via Consensus ADMM

In this article, we present a distributed algorithm to solve a class of multirobot task assignment problems. We formulate task assignment as a mathematical optimization and solve for optimal solutions with a variant of the consensus alternating direction method of multipliers (C-ADMM). We provide C-ADMM-based algorithms for both the primal and dual problem formulations and show the advantages of each form depending on the problem specifics. In our algorithm, each robot solves a series of local optimization problems and communicates the results to its local neighbors, ultimately converging to an optimal task assignment in problems with linear objective functions and an optimal solution of the relaxed problem in convex problems. While many other distributed algorithms require a central station for their implementation, in our algorithm, each robot only communicates with its one-hop neighbors. In linear task assignment problems, our algorithm converges to the optimal task assignment, unlike many other distributed algorithms for this problem, which yield suboptimal solutions. We demonstrate our algorithms in task assignment problems over a variety of communication network topologies, where we show that our inexact dual algorithm is at least 60% faster than other distributed algorithms, which produce an optimal task assignment. In addition, our dual algorithm attains a 69% speedup in computation time compared to a notable distributed variant of the Hungarian method (Chopra et al., 2017). We also apply our algorithm to a multi-unmanned-aerial-vehicle persistent surveillance problem, showing its suitability for problems involving periodic task assignments.

Leaderless Time-To-Go Protocols for Same-Time Position Consensus

We propose three time-to-go consensus protocols to effect the same-time position consensus of multiple agents (or vehicles) to a destination. We prove same-time position consensus to occur through the use of control generated using each of the three protocols viz max-time, min-time, and achievable-time protocols using a backstepping-inspired speed modulation approach for a system of double integrators. Next, we formulate sufficient conditions on the graph communication network topology for consensus to occur through each protocol and propose a tokenized algorithm to effect same-time consensus in systems with arbitrary connected graph communication topologies using the achievable-time protocol. Finally, we verify our conclusions through numerical simulations to further ascertain the relevance of these protocols to diverse scenarios.