Underlying Communication Network Research Articles

Nonlinear observer-based state-of-charge balancing of networked battery energy storage systems

In this paper, we propose an observer-based algorithm for balancing the state-of-charge (SoC) among battery units in a battery energy storage system (BESS). The dynamical behaviour of a battery unit is approximated by an equivalent circuit model, based on which a nonlinear SoC observer can be constructed. Power distribution laws are designed for the battery units according to the states of the battery units, the average battery state, and the average power demand. Distributed estimation algorithms for the average battery state and the average power demand, as well as SoC observers, are constructed to implement them. The BESS is shown to achieve SoC balancing among all its battery units while satisfying the power demand, as long as mild conditions on the underlying communication network and on the power demand are met. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

Learning Failure-Inducing Models for Testing Software-Defined Networks

Software-defined networks (SDN) enable flexible and effective communication systems that are managed by centralized software controllers. However, such a controller can undermine the underlying communication network of an SDN-based system and thus must be carefully tested. When an SDN-based system fails, in order to address such a failure, engineers need to precisely understand the conditions under which it occurs. In this article, we introduce a machine learning-guided fuzzing method, named FuzzSDN, aiming at both (1) generating effective test data leading to failures in SDN-based systems and (2) learning accurate failure-inducing models that characterize conditions under which such system fails. To our knowledge, no existing work simultaneously addresses these two objectives for SDNs. We evaluate FuzzSDN by applying it to systems controlled by two open-source SDN controllers. Further, we compare FuzzSDN with two state-of-the-art methods for fuzzing SDNs and two baselines for learning failure-inducing models. Our results show that (1) compared to the state-of-the-art methods, FuzzSDN generates at least 12 times more failures, within the same time budget, with a controller that is fairly robust to fuzzing and (2) our failure-inducing models have, on average, a precision of 98% and a recall of 86%, significantly outperforming the baselines.

Distributed Consensus Algorithms in Sensor Networks with Higher-Order Topology.

Information aggregation in distributed sensor networks has received significant attention from researchers in various disciplines. Distributed consensus algorithms are broadly developed to accelerate the convergence to consensus under different communication and/or energy limitations. Non-Bayesian social learning strategies are representative algorithms for distributed agents to learn progressively an underlying state of nature by information communications and evolutions. This work designs a new non-Bayesian social learning strategy named the hypergraph social learning by introducing the higher-order topology as the underlying communication network structure, with its convergence as well as the convergence rate theoretically analyzed. Extensive numerical examples are provided to demonstrate the effectiveness of the framework and reveal its superior performance when applying to sensor networks in tasks such as cooperative positioning. The designed framework can assist sensor network designers to develop more efficient communication topology, which can better resist environmental obstructions, and also has theoretical and applied values in broad areas such as distributed parameter estimation, dispersed information aggregation and social networks.

Distributed observers for LTV systems: A distributed constructibility gramian based approach

The paper addresses the problem of designing distributed observers for discrete linear time-varying (LTV) systems with distributed sensor nodes. It is shown, under the conditions of collective observability, strong connectivity of the sensor communication network, and invertibility of the state transition matrix, that the resulting observer yields exponential stability of the estimation errors with a pre-defined convergence rate and, in certain situations, requires the exchange of fewer data than other methods. It is shown that for linear time-invariant (LTI) systems this method yields fixed gains that can be computed in a few steps of a distributed algorithm with the underlying communication network. A design example is given where the asymptotic performance of the proposed observer is shown to be similar to that obtained using a distributed Kalman filtering approach.

Improved Quality of Online Education Using Prioritized Multi-Agent Reinforcement Learning for Video Traffic Scheduling

The recent global pandemic has transformed the way education is delivered, increasing the importance of video-based online learning. However, this puts a significant pressure on the underlying communication networks and the limited available bandwidth needs to be intelligently allocated to support a much higher transmission load, including video-based services. In this context, this paper proposes a Machine Learning (ML)-based solution that dynamically prioritizes content viewers with heterogeneous video services to increase their Quality of Service (QoS) and perceived Quality of Experience (QoE). The proposed approach makes use of the novel Prioritized Multi-Agent Reinforcement Learning solution (PriMARL) to decide the prioritization order of the video-based services based on networking conditions. However, the performance in terms of QoS and QoE provisioning to learners with different profiles and networking conditions depends on the type of scheduler employed in the frequency domain to conduct the scheduling and the radio resource allocation. To decide the best approach to be followed, we employ the proposed PriMARL solution with different types of scheduling rules and compare them with other state-of-the-art solutions in terms of throughput, delay, packet loss, Peak Signal-to-Noise Ratio (PSNR), and Mean Opinion Score (MOS) for different traffic loads and characteristics. We show that the proposed solution achieves the best user QoE results.

Matrix-scaled resilient consensus of discrete-time and continuous-time networks

This paper studies the matrix-scaled resilient consensus problems over multi-agent networks as occurring in computer science and distributed control. Unlike existing works on consensus problems, where the states of agents converge to a common value or reach some prescribed proportions, we take a more general matrix-scaled approach to accommodate the interdependence of multi-dimensional states. We develop a unified analytical framework to deal with matrix-scaled resilient consensus of discrete-time and continuous-time dynamical agents, where the underlying communication network is modeled as a generic directed time-dependent random graph. We propose new distributed protocols to guarantee the matrix-scaled consensus of cooperative agents in the network in the presence of Byzantine agents, who have full knowledge of the system and pose a severe security threat to the collective consensus objective. The cooperative agents feature multiple input and multiple output, and the number and identities of Byzantine agents are not available to the cooperative ones. Our mathematical approach capitalizes on matrix analysis, control theory, graph theory, and martingale convergence. Some numerical examples are presented to demonstrate the effectiveness of our theoretical results.

Consensus-Based Multipopulation Game Dynamics for Distributed Nash Equilibria Seeking and Optimization

In this article, a consensus-based multipopulation game dynamics approach is proposed for distributed Nash equilibria seeking and optimization. The approach is fundamentally different from existing population dynamics from that: 1) the underlying communication network underlying the population game dynamics could be arbitrary undirected connected graph and more importantly 2) the proposed approach works in a distributed manner even when the objective functions of players are strongly coupled with each other. The proposed approach greatly extends the applicability of population game dynamics in distributed optimization and learning problems. A distributed constrained optimization problem and a traffic routing problem are utilized to illustrate the feasibility of the proposed multipopulation game dynamics approach.

Share and Multiply: Modeling Communication and Generated Traffic in Private WhatsApp Groups

Group-based communication is a highly popular communication paradigm, which is especially prominent in mobile instant messaging (MIM) applications, such as WhatsApp. Chat groups in MIM applications facilitate the sharing of various types of messages (e.g., text, voice, image, video) among a large number of participants. As each message has to be transmitted to every other member of the group, which multiplies the traffic, this has a massive impact on the underlying communication networks. However, most chat groups are private and network operators cannot obtain deep insights into MIM communication via network measurements due to end-to-end encryption. Thus, the generation of traffic is not well understood, given that it depends on sizes of communication groups, speed of communication, and exchanged message types. In this work, we provide a huge data set of 5,956 private WhatsApp chat histories, which contains over 76 million messages from more than 117,000 users. We describe and model the properties of chat groups and users, and the communication within these chat groups, which gives unprecedented insights into private MIM communication. In addition, we conduct exemplary measurements for the most popular message types, which empower the provided models to estimate the traffic over time in a chat group.

Guest Editorial: Special Issue on Recent Advances on Blockchain for Network and Service Management

With the rapid adoption of new technologies and applications, e.g., the Internet of Things, 5G/6G communication networks, big data analytics, and artificial intelligence, a deluge of devices are being connected to the network, thus generating a large amount of data. The collection, processing, and analysis of this vast amount of data are essential to help people and enterprises gain valuable information, make sensible decisions, and subsequently improve the quality of people’s lives. However, the underlying communication networks are thus facing a new number of unprecedented challenges. Managing these large numbers of devices in a scalable and secure manner is bringing significant challenges to the infrastructure construction, maintenance, and management of the communication networks. Recurring data privacy breaches and the lack of control make Internet users and enterprises less willing to provide valuable data for processing and analysis.

Event-Driven Data Acquisition for Electricity Metering: A Tutorial

This paper provides a tutorial on the most recent advances of event-driven metering (EDM) while indicating potential extensions to improve its performance. We have revisited the effects on signal reconstruction of (i) a fine-tuned procedure for defining power variation events, (ii) consecutive-measurements filtering that refers to the same event, (iii) spike filtering, and (iv) timeout parameter. We have illustrated via extensive numerical results that EDM can provide high-fidelity signal reconstruction while decreasing the overall number of acquired measurements to be transmitted. Its main advantage is to only store samples that are informative based on predetermined events, avoiding redundancy and decreasing the traffic offered to the underlying communication network. This tutorial highlights the key advantages of EDM and points out promising research directions.

A Distributed Least-Squares Solver for Linear Systems of Algebraic Equations

This paper introduces a consensus-based continuous-time distributed algorithm to find the least-squares solution to overdetermined systems of linear algebraic equations over directed multi-agent networks. It is assumed that each agent has only access to a subsystem of the algebraic equations, and the underlying communication network is strongly connected. We show that, along the flow of the proposed algorithm, the local estimate of each agent converges exponentially to the exact least-squares solution, provided that the aggregate system of linear equations has full column rank, and each agent knows an upper bound on the total number of the participating agents in the network.

A methodology for the evaluation of the message transmission delay over IEC 61850 communication network — a real-time HV/MV substation case study

The growing intelligence of the nowadays distribution grids is due to the integration of Information and Communication Technologies (ICTs). Such a development is converting the traditional grid into the smart grid concept. Communicating over a reliable ICT infrastructure, distributed Intelligent Electronic Devices (IEDs) perform supervision, protection, and control of the smart grid. The communication network is not errorless, and any error of the underlying communication network can degrade the power grid performance or even cause instability in it. Hence, there is a necessity to obtain a modeling method to analyze its Quality of Service (QoS). One of the most important quality measures of the Substation Communication Network (SCN) traffic is the message transmission delay. Thus, this paper proposes a methodology to evaluate the message transmission delay through an analytical model. To estimate the transmission delay, a port connection model represents the message distribution model. Then a traffic-flow-source model, and a traffic-flow-service model are applied to characterize each flow while it is served through different ports. Finally, delay is calculated based on the corresponding flow and service information. The estimation model is applied on an IEC-61850-MMS (Manufacturing Message Specification)-based communication scenario for a load-shedding goal that is considered over a real reduced-scale HV/MV smart substation test bench. The estimated results are compared to the measured values by the Wireshark network analyzer that indicates an estimation error of less than 10%.

Decentralized learning of randomization-based neural networks with centralized equivalence

We consider a decentralized learning problem where training data samples are distributed over agents (processing nodes) of an underlying communication network topology without any central (master) node. Due to information privacy and security issues in a decentralized setup, nodes are not allowed to share their training data and only parameters of the neural network are allowed to be shared. This article investigates decentralized learning of randomization-based neural networks that provides centralized equivalent performance as if the full training data are available at a single node. We consider five randomization-based neural networks that use convex optimization for learning. Two of the five neural networks are shallow, and the others are deep. The use of convex optimization is the key to apply alternating-direction-method-of-multipliers with decentralized average consensus. This helps us to establish decentralized learning with centralized equivalence. For the underlying communication network topology, we use a doubly-stochastic network policy matrix and synchronous communications. Experiments with nine benchmark datasets show that the five neural networks provide good performance while requiring low computational and communication complexity for decentralized learning. The performance rankings of five neural networks using Friedman rank are also enclosed in the results, which are ELM < RVFL< dRVFL < edRVFL < SSFN.

A review of distributed optimization: Problems, models and algorithms

With the development of big data and artificial intelligence, distributed optimization has emerged as an indispensable tool for solving large-scale problems. In particular, the multi-agent system based on distributed information processing can be elaborately designed for distributed optimization, in which the agents collaboratively minimize a global objective function made up of a sum of local objective cost functions subject to some local and/or global constraints. Inspired by the applications involving resource allocation, machine learning, power systems, sensor networks and cloud computing, a variety of distributed optimization models and algorithms have been investigated and developed. The optimization models include unconstrained and constrained problems in continuous and discontinuous systems with undirected and directed communication topology graphs. The constraints include bounded constraint, separable and inseparable equality and inequality constraints. Meanwhile, in distributed algorithms, every agent executes its local computation and updating on basis of its own data information and that exchanging with its neighboring agents by means of the underlying communication networks, in order to deal with the optimization problems in a distributed way. This paper is designed to provide a comprehensive overview of extant distributed models and algorithms for distributed optimization.

MMTC Deployment over Sliceable Infrastructure: The Megasense Scenario

Massive Machine Type Communication (mMTC) has long been identified as a major vertical sector and enabler of the industry 4.0 technological evolution that will seamlessly ease the dynamics of machine-to-machine communications while leveraging 5G technology. To advance this concept, we have developed and tested an mMTC network slice called Megasense. Megasense is a complete framework that consists of multiple software modules, which is used for collecting and analyzing air pollution data that emanates from a massive amount of air pollution sensors. Taking advantage of 5G networks, Megasense will significantly benefit from an underlying communication network that is traditionally elastic and can accommodate the on-demand changes in requirements of such a use case. As a result, deploying the sensor nodes over a sliceable 5G system is deemed the most appropriate in satisfying the resource requirements of such a use case scenario. In this light, in order to verify how 5G-ready our Megasense solution is, we deployed it over a network slice that is totally composed of virtual resources. We have also evaluated the impact of the network slicing platform on Megasense in terms of bandwidth and resource utilization. We further tested the performances of the Megasense system and come up with different deployment recommendations based on which the Megasense system would function optimally.

Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions

The Smart grid (SG), generally known as the next-generation power grid emerged as a replacement for ill-suited power systems in the 21st century. It is in-tegrated with advanced communication and computing capabilities, thus it is ex-pected to enhance the reliability and the efficiency of energy distribution with minimum effects. With the massive infrastructure it holds and the underlying communication network in the system, it introduced a large volume of data that demands various techniques for proper analysis and decision making. Big data analytics, machine learning (ML), and deep learning (DL) plays a key role when it comes to the analysis of this massive amount of data and generation of valuable insights. This paper explores and surveys the Smart grid architectural elements, machine learning, and deep learning-based applications and approaches in the context of the Smart grid. In addition in terms of machine learning-based data an-alytics, this paper highlights the limitations of the current research and highlights future directions as well.

A decentralized adaptive momentum method for solving a class of min-max optimization problems

Min-max saddle point games have recently been intensely studied, due to their wide range of applications, including training Generative Adversarial Networks (GANs). However, most of the recent efforts for solving them are limited to special regimes such as convex-concave games. Further, it is customarily assumed that the underlying optimization problem is solved either by a single machine or in the case of multiple machines connected in centralized fashion, wherein each one communicates with a central node. The latter approach becomes challenging, when the underlying communications network has low bandwidth. In addition, privacy considerations may dictate that certain nodes can communicate with a subset of other nodes. Hence, it is of interest to develop methods that solve min-max games in a decentralized manner. To that end, we develop a decentralized adaptive momentum (ADAM)-type algorithm for solving min-max optimization problem under the condition that the objective function satisfies a Minty Variational Inequality condition, which is a generalization to convex-concave case. The proposed method overcomes shortcomings of recent non-adaptive gradient-based decentralized algorithms for min-max optimization problems, that do not perform well in practice and require careful tuning. In this paper, we obtain non-asymptotic rates of convergence of the proposed algorithm (coined Dadam3 ) for finding a (stochastic) first-order Nash equilibrium point and subsequently evaluate its performance on training GANs. The extensive empirical evaluation shows that Dadam3 outperforms recently developed methods, including decentralized optimistic stochastic gradient for solving such min-max problems.

Decentralized Cooperative Online Estimation With Random Observation Matrices, Communication Graphs and Time Delays

We analyze convergence of decentralized cooperative online estimation algorithms by a network of multiple nodes via information exchanging in an uncertain environment. Each node has a linear observation of an unknown parameter with randomly time-varying observation matrices. The underlying communication network is modeled by a sequence of random digraphs and is subjected to nonuniform random time-varying delays in channels. Each node runs an online estimation algorithm consisting of a consensus term taking a weighted sum of its own estimate and neighbours' delayed estimates, and an innovation term processing its own new measurement at each time step. By stochastic time-varying system, martingale convergence theories and the binomial expansion of random matrix products, we transform the convergence analysis of the algorithm into that of the mathematical expectation of random matrix products. Firstly, for the delay-free case, we show that the algorithm gains can be designed properly such that all nodes' estimates converge to the true parameter in mean square and almost surely if the observation matrices and communication graphs satisfy the stochastic spatio-temporal persistence of excitation condition. Secondly, for the case with time delays, we introduce delay matrices to model the random time-varying communication delays between nodes. It is shown that under the stochastic spatio-temporal persistence of excitation condition, for any given bounded delays, proper algorithm gains can be designed to guarantee mean square convergence for the case with conditionally balanced digraphs.

Convergence of Distributed Gradient-Tracking-Based Optimization Algorithms with Random Graphs

This paper studies distributed convex optimization over a multi-agent system, where each agent owns only a local cost function with convexity and Lipschitz continuous gradients. The goal of the agents is to cooperatively minimize a sum of the local cost functions. The underlying communication networks are modelled by a sequence of random and balanced digraphs, which are not required to be spatially or temporally independent and have any special distributions. The authors use a distributed gradient-tracking-based optimization algorithm to solve the optimization problem. In the algorithm, each agent makes an estimate of the optimal solution and an estimate of the average of all the local gradients. The values of the estimates are updated based on a combination of a consensus method and a gradient tracking method. The authors prove that the algorithm can achieve convergence to the optimal solution at a geometric rate if the conditional graphs are uniformly strongly connected, the global cost function is strongly convex and the step-sizes don’t exceed some upper bounds.

Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions

In the 21st century, the Smart Grid (SG), also known as the next-generation power grid, arose as a substitute for inefficient power systems, ensuring a reliable and efficient power supply. It is projected to improve the reliability and efficiency of energy distribution while having minimal side effects because it is coupled with modern communication and computation capabilities. The huge infrastructure it possesses, as well as the system's underlying communication network, has resulted in a large number of data that necessitates the use of diverse approaches for proper analysis and decision making. When it comes to analyzing this huge amount of data and generating significant insights from it, big data analytics, machine learning (ML), and deep learning (DL), all play a key role. These insights are useful for anomaly detection, fraud detection, price confirmation, fault detection, monitoring energy consumption, and so on. Hence constant and continuous data analysis is an essential part, of the modern smart grid, for its existence. Inspired by providing a reliable and efficient energy distribution, this paper explores and surveys the smart grid architectural elements, ML and DL based applications, and approaches in the context of SG. In addition in terms of ML and DL based data analytics, this paper highlights the limitations of the current research and, highlights future directions as well.