Control Of Communication Networks Research Articles

Group Intelligent City Mobile Communication Network's Control Strategy based on Cellular Internet of Things

Mobile communication network optimization heavily depends on power control technology, which impacts the effectiveness of the network. This paper aims to enhance control over nonlinear mobile communication networks and achieve superior performance by applying the particle swarm optimization (PSO) algorithm in the control domain. Addressing limitations in the basic PSO algorithm, improvements are made and applied to urban mobile communication networks. The methodology involves modifying the PSO algorithm to address identified issues and applying the enhanced algorithm to communication network scenarios. Simulation results indicate that with an initial particle count of 10 and 100 iterations, the optimized values for and are 0.691 and 0.486, respectively, resulting in an objective function value of 55.514. This achievement validates the successful implementation of the optimization process for mobile communication network control. The findings reveal that the proposed grad particle swarm optimization (Grad-PSO) algorithm exhibits mobile network optimization by robust search capability and rapid convergence.

Theory of Timely Remote Estimation and Application toSituational Awareness

Brief Biography: Tasmeen Zaman Ornee is a PhD candidate at the Electrical and Computer Engineering Department of Auburn University. She joined the Real-time Networking lab in Fall 2018. Her PhD supervisor is Prof. Yin Sun. She completed her M.Sc. in Electrical Engineering from Auburn University in 2022. She received her B.Sc. degree in Electrical & Electronic Engineering from Bangladesh University of Engineering and Technology in 2017. Her research interests include information freshness, remote estimation, robotic control, and wireless communication networks. She has worked on designing novel sampling and scheduling policies for the remote estimation of diffusion processes involving single and multiple sources. In addition, she has worked on designing low-complexity scheduling algorithms for improving situational awareness in safety-critical systems. She received the IEEE/IFIP WiOpt Best Paper Award in 2019, one of her IEEE/ACM ToN journals was recommended for fast-tracked review, and she had an invited paper at IEEE SPAWC 2021. She is also one of the authors of a book chapter published by CUP. She is a graduate student member of IEEE and ACM.

Hierarchical Communication Network for Electric Vehicles Aggregation and Control

Hierarchical Communication Network for Electric Vehicles Aggregation and Control

Development of a neural network structure for recognition of attacks in computer systems

The integration of industrial control communication networks and the Internet into the Industrial Control System (ICS) increases their vulnerability to cyber-attacks, leading to devastating consequences. Traditional intrusion detection systems (IDS) mostly rely on predefined models and are trained mostly on specific cyber attacks, which means that traditional IDS cannot deal with unknown attacks In addition, most IDSs do not take into account the imbalance of ICS datasets, and therefore suffer from low accuracy and high false positives when used. In the article, we propose an NCO-double-layer DIFF_RF-OPFYTHON intrusion detection method for ICS, which consists of NCO modules, two-layer DIFF_RF modules, and OPFYTHON modules. Detected traffic will be divided into three categories by the two-layer DIFF_RF module: known attacks, unknown attacks, and normal traffic. Next, the known attacks will be classified by the OPFYTHON module into specific attacks according to the characteristics of the attack traffic. We use the NCO module to improve model inputs and improve model accuracy. The results show that the proposed method outperforms traditional intrusion detection methods such as XGboost and SVM. Detecting unknown attacks is also significant. The accuracy of the data set used in this article reaches 98.13%. The detection rate of unknown and known attacks reaches 98.21% and 95.1%, respectively. In this article, the basic modules of the classifier are shallow machine learning algorithms. This can be improved by using a more powerful neural network architecture. In the article, all detected unknown attacks are marked as one category. In further studies, the unknown attacks can be clustered and analyzed to further classify the detected unknown attacks. It is worth noting that the number of unknown attacks is only a small part, so it is difficult to classify unknown attacks using cluster analysis. Since training a more powerful neural network requires a lot of data, it is important to investigate how to train a new model when the number of samples belonging to that class is limited.

A robust De-Noising Autoencoder imputation and VMD algorithm based deep learning technique for short-term wind speed prediction ensuring cyber resilience

The intermittent and stochastic characteristics of wind speed make its predictions difficult. Further, forecasting systems are dependent on the communication network for coordination and control that are far more susceptible to cyber-attacks. In order to ensure system reliability, safety & cyber resilience, different situations that may arise as a result of cyber-attacks like unexpected device power outages, sensor or communication failures, etc. which may further lead to corrupted data/outliers in datasets/missing of actual values in the datasets required for forecasting, are investigated and addressed here with the help of De-Noising Autoencoder (DAE) algorithm. Afterwards, with the aim to increase the forecast accuracy, Variational mode decomposition (VMD) algorithm is coupled with the Bidirectional long short term memory (BiDLSTM) deep learning algorithm. In order to evaluate the precision of suggested forecasting approach, this research scrutinizes number of datasets related to wind speed for two Indian cities i.e. Sadodar and Bhogat, located in State of Gujarat. In comparison to other hybrid decomposition-based forecasting algorithms presented in the literature, empirical findings acquired using the suggested hybrid DAE-VMD-BiDLSTM approach is found to be more significant and accurate.

Design of a distributed offloading and real-time data unified access platform for IoT within command and control communication networks

Design of a distributed offloading and real-time data unified access platform for IoT within command and control communication networks

Signal Isolation Characterization of ITCN In-Band Full-Duplex Communications

Future digital terrestrial television (DTT) systems are introducing revolutionary changes to their architectures and operation modes. One of the best examples is ATSC 3.0, which is pushing to incorporate the Internet Protocol (IP) into the protocol stack. This way, implementing new applications (e.g., target advertisement, connected cars, 4k/8k video content) is closer than ever. Indeed, recent works have suggested designing and implementing an Inter-Tower Communications Network (ITCN) and wireless In-band Distribution Link (IDL) inside the ATSC 3.0 forums. ITCN will interconnect all broadcast towers to form a communication network for control, monitoring, data communication, and localized datacast and broadcast services. Nevertheless, this application has a significant challenge: the self-interference (SI) or loopback signal that is generated and added to the received signal. To reduce the impact of the SI, the transmission centers combine different signal isolation and cancellation techniques. This paper focuses on characterizing the signal power isolation between the transmitted and the received signals under different conditions. In particular, this paper shows the signal isolation values measured during various field trials in real broadcast transmission centers. The results show that the total signal isolation varies depending on the transmission/reception configuration between 70-90 dB. Moreover, due to the measurements, we have demonstrated that the antenna distance is a more relevant parameter for signal isolation than the center frequency. Finally, the possible antenna pattern discrimination has been calculated and discussed.

A Novel ZSV-Based Detection Scheme for FDIAs in Multiphase Power Distribution Systems

Modern power systems are vulnerable to false data injection attacks (FDIAs) due to the widespread applications of two-way communication networks for system operation and control. Diagnosing such malicious attacks are of great significance for the resilient operations of power systems. Yet, in literature, the majority of studies in FDIA detection focus on power transmission systems. In this paper, a novel FDIA detection scheme is proposed for three-phase distribution systems based on zero-sequence voltage (ZSV). From the voltage and power measurements, the bus voltages are estimated, and then the estimated ZSV is calculated as the sum of the estimated bus voltages on the three phases to represent the degree of unbalance of the distribution system. Via mathematical analysis of the linear distribution system state estimation (DSSE) model, the distribution of the estimated ZSV under the normal condition is derived, based on which a whitening process is adopted on the estimated ZSV to weaken the effect of measurement noises. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{2}$ </tex-math></inline-formula> -norm of the whitened ZSV vector is then compared with a predefined threshold for FDIA detection. Moreover, the probability of false alarm of the proposed scheme is derived, which can be utilized to determine the detection threshold for a desired tolerance of false alarm rate. The proposed scheme is validated on several IEEE Test Feeders and simulation results show the effectiveness of the proposed scheme in detecting FDIAs in three-phase distribution systems.

A Function-Oriented Electronic and Electrical Architecture of Remote Control Ship on Inland River: Design, Verification, and Evaluation

The remote control is an important function of intelligent ships. In this article, first, the functions and communication requirements of remote control ships are sorted out based on the “Rules for Intelligent Ships (2020)” issued by the China Classification Society (CCS). Second, a domain controller-based electronic and electrical architecture (EEA) for inland remote control ships is proposed. The functional domain of the ship is divided, combined with the physical layout of the ship, into the perception domain, communication domain, motion control domain, marine bridge domain, deck domain, cargo management domain, and cabin domain. Third, the real-time network communication system and the central gateway controller are designed, and the efficient information interaction between domain controllers is realized through a service-oriented communication architecture. The simulation experiment shows that the designed domain controller architecture and communication network can meet the information interaction requirements of the ship. Compared with the traditional distributed architecture, the ship EEA based on the domain controller has apparent advantages in system cost, system redundancy, functional load distribution, architecture scalability, communication load distribution, and information security and can give a design reference for inland remote control ships.

Distributed Finite-Time Control of Islanded Microgrid for Ancillary Services Provision

This paper presents a hierarchical cyber-physical multi-agent model for an AC microgrid (MG). A new distributed finite-time secondary controller is designed in the provision of ancillary services, such as voltage and frequency synchronization and active and reactive power regulation. The control algorithm developed is fully distributed and intended to restore the system voltage and frequency to their nominal value finite time. The existing distributed controllers achieve a consensus in an infinite-time horizon, whereas the proposed control provides a quick convergence to a consensus even in the face of disturbances and restores the voltage and frequency in less than 0.25 s. For accurate active and reactive power regulation, a distributed control algorithm is proposed that corrects the power mismatch among neighboring distributed generator units and eventually for the entire MG network. A Lyapunov analysis is used to establish the upper limit on the convergence time. The proposed control scheme is evaluated and compared with the previously reported asymptotic control technique based on a neighborhood tracking error using time-domain simulations under load variation and communication constraints. The controller withstands a communication link delay up to 2 s with a small deviation and settles down to the nominal values within 0.4 s. Further, the convergence analysis shows that the performance of the proposed algorithm depends exclusively on the controller parameters and communication network connectivity, not on the line and load parameters of the AC MG. The proposed controller enables the plug-and-play capability of the AC MG and effectively reduces the load change-induced disturbance.

A Systemic Review of the Cybersecurity Challenges in Australian Water Infrastructure Management

Cybersecurity risks have become obstinate problems for critical water infrastructure management in Australia and worldwide. Water management in Australia involves a vast complex of smart technical control systems interconnected with several networks, making the infrastructure susceptible to cyber-attacks. Therefore, ensuring the use of security mechanisms in the control system modules and communication networks for sensors and actuators is vital. The statistics show that Australia is facing frequent cyber-attacks, most of which are either undetected or overlooked or require immediate response. To address these cyber risks, Australia has changed from a country with negligible recognition of attacks on critical infrastructure to a country with improved capability to manage cyber warfare. However, little attention is paid to reducing the risk of attacks to the critical water infrastructure. This study aims to evaluate Australia’s current cybersecurity attack landscape and the implemented controls for water infrastructure using a systematic literature review (SLR). This study also compares Australia in the context of global developments and proposes future research directions. The synthesis of the evidence from 271 studies in this review indicates the importance of managing security vulnerabilities and threats in SCADA water control systems, including the need to upgrade the contemporary water security architecture to mitigate emerging risks. Moreover, human resource development with a specific focus on security awareness and training for SCADA employees is found to be lacking, which will be essential for alleviating cyber threats to the water infrastructure in Australia.

Microgrids: A review on optimal hybrid technologies, configurations, and applications

This study aims to provide a comprehensive review about the configurations, operation, and integration of multiple energy sources for microgrid (MG) system. The applications of renewable and non-renewable energy sources have been discussed and analysed. Similarly, the several control strategies, protection schemes, and communication networks used for MG have been defined to provide a unique and distinct perspective for an optimal and real-time project. During the review analysis, different used software tools and techniques were observed and applied broadly. However, the most frequently used software and technique are hybrid optimization model for electric renewables (HOMER) Pro and particle swarm optimization algorithm. Additionally, the total global installed capacity for renewable sources is enhanced up to 2588 GW, including 627 GW of PV source, along with the global investment of USD 316.7 Billion on clean energy sources. Furthermore, the least levelized cost of energy for on-grid and off-grid hybrid systems using HOMER Pro was found as $0.072 and $0.145/kWh, respectively. Finally, the work has provided thorough study and future directions about MGs to assist engineers and researchers for their upcoming prospects in the field of renewable energy.

Sensors and Communication Simulation for Unmanned Traffic Management.

Unmanned traffic management (UTM) systems will become a key enabler to the future drone market ecosystem, enabling the safe concurrent operation of both manned and unmanned aircrafts. Currently, these systems are usually tested by performing real scenarios that are costly, limited, hardly scalable, and poorly repeatable. As a solution, in this paper we propose an agent-based simulation platform, implemented through a micro service architecture, which may simulate UTM information sources, such as flight plans, telemetry messages, or tracks from a surveillance network. The final objective of this simulator is to use these information streams to perform a system-level evaluation of UTM systems both in the pre-flight and in-flight stages. The proposed platform, with a focus on simulation of communications and sensors, allows to model UTM actors’ behaviors and their interactions. In addition, it also considers the manual definition of events to simulate unexpected behaviors/events (contingencies), such as communications failures or pilots’ actions. In order to validate our architecture, we implemented a simulator that considers the following actors: drones, pilots, ground control stations, surveillance networks, and communications networks. This platform enables the simulation of the drone trajectory and control, the C2 (command and control) link, drone detection by surveillance sensors, and the communication of all agents by means of a mobile communications network. Our results show that it is possible to truthfully recreate complex scenarios using this simulator, mitigating the disadvantages of real testbeds.

Intelligent anomaly identification in cyber-physical inverter-based systems

Modern cyber-physical systems have become more autonomous and distributed with the inclusion of advanced control architectures and communication networks. Power electronics-based inverters that employ extensive communication structures are integral part of such systems. The controllers for inverter-based systems rely on communication networks that make them vulnerable to cyber-physical anomalies. The cyber anomalies occur due to malicious attacks targeting the communication layer and physical anomalies are caused by power system faults in the physical layer of the microgrid. In this work, an intelligent anomaly identification (IAI) technique for such systems is presented utilizing data driven artificial intelligence tools that employ multi class support vector machines (MSVM) for anomaly classification and localization. The effects of cyber anomalies such as false data injection and denial of service attacks that target the communication network are considered in this work. In addition, the physical anomalies due to power system faults are also considered. The proposed technique utilizes statistical features extracted from measurements for optimal learning of a dual of MSVM classifiers. The mean absolute percentage error is used as a performance metric and the results are validated by comparing to artificial neural network, Naive Bayes classification and using real time simulations in OPAL-RT.

Inter-UAV Routing Scheme Testbeds

With the development of more advanced and efficient control algorithms and communication architectures, UAVs and networks thereof (swarms) now find applications in nearly all possible environments and scenarios. There exist numerous schemes which accommodate routing for such networks, many of which are specifically designed for distinct use-cases. Validation and evaluation of routing schemes is implemented for the most part using simulation software. This approach is however incapable of considering real-life noise, radio propagation models, channel bit error rate and signal-to-noise ratio. Most importantly, existing frameworks or simulation software cannot sense physical-layer related information regarding power consumption which an increasing number of routing protocols utilize as a metric. The work presented in this paper contributes to the analysis of already existing routing scheme evaluation frameworks and testbeds and proposes an efficient, universal and standardized hardware testbed. Additionally, three interface modes aimed at evaluation under different scenarios are provided.

AoI-Aware Control and Communication Co-Design for Industrial IoT Systems

A mass of data generated by the widespread smart devices is transmitted through wireless communication networks for estimation and control in the Industrial-Internet-of-Things (IIoT) systems. The frequent data transmission needs to meet the high reliability and real-time demand of IIoT applications because the freshness of status updates influences the system performance. In this work, we use the Age of Information (AoI) to characterize the information freshness since it is a powerful metric to capture the randomness of state updates. Meanwhile, AoI is very useful in the control and communication co-design to improve the control performance considering communication disturbance. In order to analyze the control cost, we first derive the specific expression average AoI under the packet loss with finite retransmission times. Then, we investigate the influence of average AoI on control performance and obtain the joint cost combining communication energy consumption and control cost. According to the certainty equivalent principle, we design the optimal control law separately. Besides, we prove that the optimal joint infinite horizon cost is bounded by the linear function of average AoI. The communication policy, including the data interarrival rate and code length, is designed by optimizing the mixed-integer nonlinear programming (MINP) problem. Finally, simulation results reveal that, by employing the optimal data interarrival rate and code length, the joint control and communication cost is significantly reduced.

Exploration of Intelligent Traffic Control Network Strategy for Large-scale Traffic in Xiongan New Area

Aiming at the low integration of the large-scale traffic control network in Xiongan New District and the current demand for intelligent traffic development, this paper designs an intelligent traffic control network communication model by integrating various traffic control terminal devices. The model combines relevant documents such as the urban planning outline of the Xiongan New District, and is based on the concept of smart city development. The designed intelligent traffic control network strategy can be effectively applied to large-scale traffic flow scenarios and provides a powerful tool for the realization of intelligent traffic control in Xiongan New Area.

Research on stable control communication network platform for multi-source big data fusion

With the rapid development of large power grid, the demand for real-time, efficient and reliable applications of different services is also increasing. In this paper, based on the architecture of control and protection private network, we studied the new stable control test system and communication network scheme, proposed the multi-source data real-time transmission and interactive processing architecture of machine side, network side and load side. And we built a digital analog hybrid simulation test platform to verify its overall architecture, functions and key technologies in the test environment. Finally, we tested the reliability and rapidity indexes of communication network transmission protocol, interface and information transmission, at the same time gave some feasible suggestions for engineering application in actual power grid by taking business flow simulation as an example. It lays a foundation for engineering application and popularization.

A3D: Attention-based auto-encoder anomaly detector for false data injection attacks

With the influx of more advanced and more connected computing and control devices, the electric power grid has continuously evolved to rely on communication networks for efficient operation and control. A challenge with these new technologies is that they may introduce new and unforeseen avenues of access, making the grid more susceptible to cyber attacks. False Data Injection Attacks (FDIA) are a particular type of attack that aims to cause disruptions in the operation of the power grid by affecting the feedback mechanism to control the grid. This is carried out by modifying the measurements which enable a state estimator to approximate the state of the system. These attacks are designed in such a way that they preserve the system equations on which the state estimator operates; therefore, they cannot be detected by a simple residual-based detection mechanism. In this paper, we propose monotonic attention based auto-encoders, an unsupervised learning technique to detect FDIAs. The auto-encoder is trained under normal operating conditions, and we hypothesize that it will produce outputs which are close to the true system values at normal operation even if the measurements are modified by an adversary. Based on this hypothesis, that high reconstruction error occurs for the attacked conditions, the intrusion detection is performed by a threshold mechanism using Precision-Recall curve. We validate the efficacy of our proposed attention-based auto-encoder anomaly detector (A3D) over other variants of auto-encoders such as ANN and RNN based auto-encoders, and a few supervised learning techniques, by performing FDIAs on a IEEE 14 bus system.

Security Control Assessment of Supervisory Control and Data Acquisition for Power Utilities in Tanzania

The primary purpose of this research was to assess the adequacy and effectiveness of security control of the Supervisory Control and Data Acquisition (SCADA) communication network used by infrastructure companies. Initially, the SCADA networks were physically separated from other networks connected to the internet and hence assumed secure. However, the modern SCADA are now integrated with other network resulting in new security vulnerabilities and attacks similar to those found in traditional IT. Thus, it is important to reassess the security controls of the SCADA because it is operated in an open network environment. In this research, a case of the SCADA security controls in the power sector in Tanzania was assessed, whereby a specific SCADA implementation was studied. The data were gathered using observation, testing, interviews, questionnaire and documentation reviews. The results were analyzed using the Cyber Security Evaluation Tool (CSET) and checked for compliance based on the National Institute of Standards and Technology (NIST) and North America Electric Reliability Corporation (NERC) standards. The findings have shown that there exist security vulnerabilities both in security compliance of the standard and component-based vulnerabilities. Additionally, there is inadequate of audit and accountability, personnel security and system and information integrity. Also, for the component-based security compliance, the finding shows that identification and authentication, security management and audit and accountability. On the basis of the results, the research has indicated the areas that require immediate action in order to protect the critical infrastructure.