Network System Research Articles

Investigation of 16 × 10 Gbps mode division multiplexed enabled integrated NGPON–FSO architecture under wired-wireless link losses

Abstract To enhance the transmission capacity for energy consumption and low cost system, mode division multiplexing (MDM) using mode 0 and mode 1 is proposed and investigated. A pair of eight downlink and uplink wavelengths is transmitted by using each MDM mode in an integrated bidirectional next generation passive optical network and free space optics (NGPON–FSO) system at 10 Gbps per channel transmission rate under the impact of FSO–fiber links impairments. Simulation results indicate that received power of −21.2 dBm with 0.2 dB power penalty can be obtained over 1 km FSO and 100 m multimode fiber under unfavourable turbulent effects. The system provides extended FSO link range, and fiber range of 150 km and 600 m respectively at threshold limit of 10−3. Besides, the mathematical analysis depicts the receiver sensitivity and splitter power budget of 42 dBm and 18 dB, respectively, for the proposed system. The system offers finest performance than other pre-existing systems.

“Hybrid MAC Methodology for Improving the Qos in Fiber Wireless Network”

The Fi-Wi (Fiber-Wireless) approach stands out as a crucial element in the realm of networks, demonstrating superiority over various technologies. With the exponential growth in Internet users, significant strides have been made in the evolution of Fi-Wi networking systems in recent years. This mechanism offers broader bandwidth and network stability, ensuring high-speed connectivity with "Anytime Anywhere" availability for end users. However, the escalating energy demand in networking systems poses a constraint on the network's lifespan, impacting transmission.
 Over the years, researchers have proposed and tested various Media Access Control (MAC) protocols to address transmission and energy consumption issues. Despite these efforts, existing protocols have encountered challenges such as overheating, delays, throughput issues, and collisions. This research paper introduces a combination of Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access (CSMA) to tackle associated challenges. The primary objective is to enhance throughput and reduce delays in Fi-Wi networks.
 To achieve this goal, the study employs techniques that involve and Utilizing an Orthogonal Frequency Division Multiplexing (OFDM) modulator, a free-space optical (FSO) communication channel, an OFDM demodulator, and Opti-system for the analysis and enhancement of received signals, our study demonstrates that the proposed MAC protocol surpasses conventional MAC protocols in terms of delay, data throughput, and transmission efficiency.

Stable reputation management system in P2P networks: An evolutionary perspective

In P2P networks, peers tend to “free-ride” on the contributions of others. Many reputation-based resource sharing mechanisms have been proposed to mitigate the problem of free riding. However, the evolutionary stability of these mechanisms that require peers to keep track of the reputations of other peers has not been investigated. We show that, without any extra benefit, systems with reputation-based incentive mechanisms are not evolutionary stable. We also show that if the fraction of peers that record reputation and reciprocate accordingly is above a certain threshold, then free riding can be controlled, and a reputation management system is beneficial only when the fraction of free riders is above a certain threshold. Furthermore, we show that imposing a round-based fee and dividing it among the peers as an incentive to keep track of reputation is an evolutionary stable strategy. In this process, we analyse all the possible equilibria in different settings of the system.

Graph neural network-based anomaly detection for river network systems

Background Water is the lifeblood of river networks, and its quality plays a crucial role in sustaining both aquatic ecosystems and human societies. Real-time monitoring of water quality is increasingly reliant on in-situ sensor technology. Anomaly detection is crucial for identifying erroneous patterns in sensor data, but can be a challenging task due to the complexity and variability of the data, even under typical conditions. This paper presents a solution to the challenging task of anomaly detection for river network sensor data, which is essential for accurate and continuous monitoring. Methods We use a graph neural network model, the recently proposed Graph Deviation Network (GDN), which employs graph attention-based forecasting to capture the complex spatio-temporal relationships between sensors. We propose an alternate anomaly threshold criteria for the model, GDN+, based on the learned graph. To evaluate the model’s efficacy, we introduce new benchmarking simulation experiments with highly-sophisticated dependency structures and subsequence anomalies of various types. We also introduce software called gnnad. Results We further examine the strengths and weaknesses of this baseline approach, GDN, in comparison to other benchmarking methods on complex real-world river network data. Conclusions Findings suggest that GDN+ outperforms the baseline approach in high-dimensional data, while also providing improved interpretability.

An Approach for Detecting Security Attacks using Machine Learning in IoT Environment

The strong security measures are becoming even more necessary to protect these networked systems as a result of the proliferation of Internet of Things (IoT) devices. The dynamic and varied nature of IoT networks frequently makes it impossible for traditional security solutions to be effective. The method suggested in this research uses machine learning to identify security attacks in IoT contexts. The suggested method makes use of the capabilities of machine learning algorithms to examine the enormous amounts of data produced by IoT devices. The system can develop the ability to recognise possible security threats and take immediate action by training models on labelled datasets that include both normal and attack patterns. In this paper multiple ML models for security threat detection in IoT environments in this study. Our evaluation uses both binary and multiclass classification models in an effort to accurately reflect the variety of assaults that can be found in IoT environments. The proposed method provide a new specialised IoT dataset that was created especially to reflect the characteristics of actual IoT environments in order to assure the validity of our assessment. By completing this extensive analysis, we hope to shed light on how well AI-based methods for security attack detection in IoT contexts work. The results of this study can help researchers and professionals decide which ML models and feature engineering techniques are best for IoT security. At the end of the day, we want to help with the creation of reliable security systems that safeguard IoT devices and shield user data from harmful attacks.

Development of Digital Literacy and Digital Empathy with Micro-learning via Activities on Metaverse

This research is related mainly to the study of the results on the development of digital literacy and digital empathy with micro-learning via activities on metaverse. The main concept of this study is based on the integration of micro-learning process with metaverse technology in order to encourage and provide learners with opportunities to create bodies of knowledge and engage in joint activities through the network system that can be accessed anywhere and anytime. The objectives of this research are (1) to synthesize the conceptual framework of the micro-learning via activities on metaverse, (2) to design the micro-learning process via activities on metaverse, and (3) to study the results of the development of digital literacy and digital empathy with the micro-learning via activities on metaverse. Thereby, this study relies on the pre-experimental research method with one-shot case study, in which the research participants are 30 undergraduate students of Pakse Teacher Training College, Lao People's Democratic Republic, who were derived by means of cluster sampling and well protected under the policy of confidentiality and anonymity. The research results show that (1) the students’ digital literacy and digital empathy, after learning with the micro-learning via activities on metaverse, are at very good level (mean = 43.20, SD = 2.35), and (2) the overall satisfaction towards the micro-learning via activities on metaverse is at high level (mean = 4.42, SD = 0.78). In reference to the above research results, it is evident that the micro-learning via activities on metaverse enables the students to quickly develop their digital literacy and digital empathy after receiving new experiences and new knowledge because the knowledge gained from the learning of this style is easy to remember and can be applied in an effective manner.

Analyzing global trade network resilience during economic crises: A model-based exploration of countries' interactions, USDX variation, and number of exporting countries

The global trade network can be viewed from different perspectives. The common ones are the volume of exports and the number of countries that export products. The variation in number of exporting countries for products has been studied over the years. The U.S. dollar has been used for invoicing exportation in global trade, and its variation in values against global currencies negatively affects the volume of exports and the number of exporters. United States dollar indices (USDX) have been globally used to measure the values of global currencies against the U.S. dollar. Many studies have been done on the effect of the global economic crisis on global trade networks. However, scholars have not yet developed a complex dynamical model and established the critical transitional or threshold values that separate the resilient network of exporting countries from a non-resilient one. This study modelled the dynamic representation of a complex network of exporting countries by modifying Lotka-Volterra models. Using the developed model and empirical data USDX and export data of 161 countries from 1971 to 2020, the critical transition values have been calculated for different phases of economic crises. This study references five major crises, revealing that the variation of U.S. dollar indices significantly impacts global network system resilience. It suggests that policies reducing trade restrictions among trading partners are more important than controlling variation in U.S. dollar indices. This information will be crucial in addressing future crises like the Russian-Ukraine conflict.

Robust Optical Mode Converter Based on Topological Waveguide Arrays

Abstract Optical mode converters are essential for enhancing the capacity of optical communication systems. However, fabrication errors restrict the further improvement of conventional mode converters. To address this challenge, we have designed an on-chip TE0-TE1 mode converter based on topologically protected waveguide arrays. The simulation results demonstrate that the converter exhibits a mode coupling efficiency of 93.5% near 1550 nm and can tolerate a relative fabrication error of 30%. Our design approach can be extended to enhance the robustness for other integrated photonic devices, beneficial for future development of optical network systems.

Hierarchical co-optimization of EV scheduling considering customer and system in distribution networks

The global surge in the adoption of electric vehicles (EV) has affected distribution networks, with uncoordinated charging increasing peak load and loss. EV scheduling has the potential to become a business that utilizes vehicle-to-grid (V2G) technology to mitigate system impacts while providing financial incentives to customers. Efficient EV scheduling is a multi-objective optimization problem that simultaneously considers network and customer perspectives for co-optimization. This study introduces a hierarchical EV charging management platform (EVMP) with day-ahead and real-time stages that satisfy customer preferences while ensuring efficient system operation. In this process, in order to solve the computational issues of the optimal power flow (OPF) problem, including integer variables, the computational burden was alleviated by removing integer variables from the OPF problem of the day-ahead stage, and EV dynamics were effectively handled using the rolling-horizon framework in the real-time stage. This study focuses on the relationships between the objective functions at each stage and utilizes the augmented ɛ-constraint method (AUGMECON) to obtain a Pareto front that assists in stakeholder decision making. The Pareto front is constructed by considering stakeholders from both two- and three-way perspectives, allowing the system operator to select a solution from the Pareto front for decision making.

Adaptive Event-Triggered Fuzzy Control for Unreliable Networked Control Systems with Time-Varying Delay

Adaptive Event-Triggered Fuzzy Control for Unreliable Networked Control Systems with Time-Varying Delay

Advanced Authentication and Energy-Efficient Routing Protocol for Wireless Body Area Networks

Recently, wireless body area network (WBAN) becomes a hot research topic in the advanced healthcare system. The WBAN plays a vital role in monitoring the physiological parameters of the human body with sensors. The sensors are small in size, and it has a small-sized battery with limited life. Hence, the energy is limited in the multi-hop routing process. The patient data is collected by the sensor, and the data are transmitted with high energy consumption. It causes failure in the data transmission path. To avoid this, the data transmission process should be optimized. This paper presents an advanced authentication and energy-efficient routing protocol (AAERP) for optimal routing paths in WBAN. Patients’ data are aggregated from the WBAN through the IoMT devices in the initial stage. To secure the patient’s private data, a hybrid mechanism of the elliptic curve cryptosystem (ECC) and Paillier cryptosystem is proposed for the data encryption process. Data security is improved by authenticating the data before transmission using an encryption algorithm. Before the routing process, the data encryption approach converts the original plain text data into ciphertext data. This encryption approach assists in avoiding intrusions in the network system. The encrypted data are optimally routed with the help of the teamwork optimization algorithm (TOA) approach. The optimal path selection using this optimization technique improves the effectiveness and robustness of the system. The experimental setup is performed by using Python software. The efficacy of the proposed model is evaluated by solving parameters like network lifetime, network throughput, residual energy, success rate, number of packets received, number of packets sent, and number of packets dropped. The performance of the proposed model is measured by comparing the obtained results with several existing models.

Improving the Water Distribution System to Support a Future Water Demand in the Western area of Chao Phaya River

Bangkok and its vicinities’ city areas are expanding and are requiring the higher water supply from the Maha Sawat and Bang Bua Thong service areas of the Metropolitan Waterworks Authority. As a result, the existing water network system’s water pressure may decrease and be inadequate to supply the water to the service areas. The study utilizes the water pipe network model, EPANET, of the Maha Sawat and Bang Bua Thong service areas that has been calibrated previously. The model was satisfyingly calibrated and accepted by balancing the water volume and pressure level. In this research, the model is used to evaluate the existing pipe network system of the study area under the future water demand condition in the next five years. Then, the model is used to define optimal location of a new pumping station that needs to increase the water distribution system efficiency and its stability. Five proposed location that need to increase the water distribution system are assumed to use the same pumping pattern and supply pressure. Each pumping station supplies a pressure head at least eight meters and use the water pressure multipliers from the Phet Kasem pumping station for each period. By using the average water pressure and the contour pressure of water distribution area as an indicator comparing the five pumping locations individually, the result showed that the best location of the new pumping station is located on Highway No.345 km.5+735.00.

Set stabilization of logical control networks: A minimum node control approach

In network systems, control using minimum nodes or pinning control can be effectively used for stabilization problems to cut down the cost of control. In this paper, we investigate the set stabilization problem of logical control networks. In particular, we study the set stabilization problem of probabilistic Boolean networks (PBNs) and probabilistic Boolean control networks (PBCNs) via controlling minimal nodes. Firstly, an algorithm is given to search for the minimum index set of pinning nodes. Then, based on the analysis of its high computational complexity, we present optimized algorithms with lower computational complexity to ascertain the network control using minimum node sets. Moreover, some sufficient and necessary conditions are proposed to ensure the feasibility and effectiveness of the proposed algorithms. Furthermore, a theorem is presented for PBCNs to devise all state-feedback controllers corresponding to the set of pinning nodes. Finally, two models of gene regulatory networks are considered to show the efficacy of obtained results.

Effects of Concentrated Mass on the In-Plane Dynamic Behaviors of Two-Cable Networks with a Cross-Tie

This study proposes a two-cable network model, which includes a cross-tie and two concentrated masses installed at the connection points of cross-tie and cable. The characteristic equation of such a cable network system is formulated via the complex modal analysis method. Then, dynamics of a twin-cable network is discussed in details, and the effect of concentrated mass on the system modal frequencies, damping and mode shapes is investigated. Furthermore, a general two-cable network system is analyzed. Results show that the concentrated mass always reduces the modal frequency, as it causes an increase in the modal mass of the system. When installing a concentrated mass in a twin-cable network, the difference of vibration modes between the cables can be increased in the in-phase modes, thereby achieving the damping effect of the damping type cross-tie on these modes. For a twin-cable network with a viscous damper, a small concentrated mass can significantly increase the modal damping of the in-phase modes as long as the damping coefficient of the damper is properly set. Meanwhile, when there is a two-cable network with unequal length cables, the presence of concentrated masses may reduce differences in vibration modes between cables, thus leading to decreased damping of some modes.

Planning for a network system with renewable resources and battery energy storage, focused on enhancing resilience

The growing significance of network resilience underscores the importance of research in integrating Renewable Energy Resources (RESs) and battery energy storage Systems (BESS) with electrical networks. This paper presents a real-time simulation for systematically integrating renewable energy sources (RESs) and battery energy storage systems (BESS) in electrical networks, focusing on resilience metrics that involve a multi-objective optimization approach that considers the relative battery capacity to the total system cost. In addition, the Demand Response Program (DRP) is considered an additional layer to manage seven different loads that provide a comprehensive and accurate representation of energy consumption during 24 h. The scheduling model, which incorporates the IDR strategy, reduces the total load of data center performance by 40 %. Additionally, optimizing battery capacity can lead to an 89 % reduction in total costs, representing a substantial success in enhancing the economic resilience metric. This study has been conducted on the IEEE 24-bus RTN to assess the required investment for deploying BESS and to gauge its effectiveness in enhancing the resilience of the electrical network. The results confirm the effectiveness of the proposed model for enhancing the economic and performance metrics of resilience.

Advanced hybrid color image encryption utilizing novel chaotic neural network and 5D-hyperchaotic system

Advanced hybrid color image encryption utilizing novel chaotic neural network and 5D-hyperchaotic system

An Approach to Maximize the Admitted Device-to-Device Pairs in MU-MIMO Cellular Networks

Due to the shortage of wireless resources and the emergence of a large number of users, determining how to guarantee the quality-of-service (QoS) requirements of users and make more users work in the same spectrum has become an urgent research topic. In this paper, we study a multi-user MIMO (MU-MIMO) cellular network system model in which cellular users (CUs) share the same spectrum resource with multiple device-to-device (D2D) pairs. To maximize the number of admitted D2D pairs sharing the same spectrum with the CUs, a joint power allocation and channel gain (JPACG) algorithm is proposed. The optimization problem is divided into two steps to be solved. First, the power allocation of CUs without D2D pairs admitted is solved. Then, the optimization problem is transformed into minimizing the interference to CUs when CUs are treated as primary users. The admittance order of D2D pairs is determined by the transmission power and channel gain. The proposed algorithm uses a convex optimization algorithm to solve the problem of power allocation joint interference channel gain in order to maximize the number of admitted D2D pairs under the constraints of the signal-to-interference-plus-noise ratio (SINR) threshold and maximum transmission power. In addition, the effect of the number of admitted D2D pairs on the total sum rate of all users is also analyzed. The simulation results show that the proposed JPACG algorithm can achieve better performance in admitting D2D pairs.

A novel vehicle collision detection system: Integrating audio-visual fusion for enhanced performance

In vehicular accidents, the swift and accurate identification of car crashes is paramount, as it serves as the linchpin for prompt responses from emergency services and search-and-rescue operations. This study introduces an innovative multimodal car crash detection system that capitalizes on audio-visual data sourced from dashboard cameras, thus significantly enhancing the precision of automobile collision detection. In contrast to car crash detection systems relying on single-modal inputs, this research employs advanced multimodal methodologies with a neural network architecture that integrates Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for car crash detection. This system concurrently leverages audio spectrogram and audio feature data, video RGB, video optical flow, and video differential information, enabling collision recognition through a neural network system, resulting in markedly improved collision detection accuracy. A comprehensive comparative analysis is conducted, benchmarking our system against the most cutting-edge existing car crash detection systems. The system employs a unique fusion approach, allowing it to apply its inference to audio-visual information of varying durations compared to the training set. The empirical findings unequivocally affirm our proposed solution’s substantial enhancement in car crash detection.

Performance evaluation of communication networks for networked microgrids

The main goal of cyber-physical power systems (CPPSs) is to provide seamless collaboration between the physical and cyber systems, bridging disciplines, such as communication networks and control systems. Controlling the operation of Microgrids (MGs) as a cyber-physical power system must include both the physical and cyber layers. Generally, the microgrid control center MGCC is employed for reliable operation management between the different local controllers within the networked microgrids (NMGs). Therefore, an excessive number of communication channels between the MGCC and the local controllers will exist, affecting the control performance of the NMGs. This paper focuses on developing an innovative communication network emulation model for standalone DC networked microgrids using a Network Simulator (NS3), miming the communication infrastructure to control the networked microgrid system. Through the real-time simulation, the performance of the proposed communication network model was evaluated by employing two transmission protocols, User Datagram Protocol (UDP) and Transmission Control Protocol (TCP), to facilitate the data exchange between the MGCC and the local controllers in each MG while operating under different traffic loads. The comparative results of using these protocols in terms of the key performance indicators (KPIs) of the communication performance metrics, such as the mean delay, percentage of packet loss, and transmitted and received bitrate, reveal notable insights into their suitability in data transmission for controlling and managing the operation of the NMGs.

Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise

This paper considers learning the hidden causal network of a linear networked dynamical system (NDS) from the time series data at some of its nodes -- partial observability. The dynamics of the NDS are driven by colored noise that generates spurious associations across pairs of nodes, rendering the problem much harder. To address the challenge of noise correlation and partial observability, we assign to each pair of nodes a feature vector computed from the time series data of observed nodes. The feature embedding is engineered to yield structural consistency: there exists an affine hyperplane that consistently partitions the set of features, separating the feature vectors corresponding to connected pairs of nodes from those corresponding to disconnected pairs. The causal inference problem is thus addressed via clustering the designed features. We demonstrate with simple baseline supervised methods the competitive performance of the proposed causal inference mechanism under broad connectivity regimes and noise correlation levels, including a real world network. Further, we devise novel technical guarantees of structural consistency for linear NDS under the considered regime.