Network Load Research Articles

SOFCs integrated with SMES under dynamic power control using Chernobyl disaster optimizer

The current study uses the Chernobyl disaster optimizer (CDO), a new metaheuristic optimizer, to identify the seven unknown parameters of solid oxide fuel cells (SOFCs). The procedures of the CDO is based on physical behavior of the elaborated radiations from the well-known Chernobyl disaster according to their mass, speed, frequency, and degree of ionization. The sum of square errors (SMSE) among the estimated and the real measured output voltage datasets of SOFCs is minimized employing the CDO. Set of boundaries of the SOFC’s process is taken into consideration with the problem formulation. SOFCs stack’s model is examined at 800οC and 900οC and its performance is confirmed. The CDO extracts more precise SOFCs’ parameters compared to other competitors. The CDO’s convergence patterns and the SOFCs unit’s performance are studied and proved at steady-state by comparing its results to a number of recognized algorithms under varied operating scenarios. A significant SMSE’s values of 3.46 µV2 and 7.38 µV2 are attained at 800οC and 900οC, respectively by the CDO. As a result, the polarization principal curves of the measured and estimated voltage datasets are checked and verified with very close matching. The dynamic behavior of the SOFCs stack is examined in relation to direct load, electric networks, and superconducting magnetic energy storage devices (SMES) for additional validation and illustration. The role of the SOFCs stack in controlling the active and reactive power delivered to the network and direct load is investigated using two controllers: one to control the inverter, which converts the SOFC’s dc output to the main network, and the other to control the SMES. The Simulink/MATLAB environment is used to indicate the validity of the proposed framework under both steady-state and dynamical conditions. The comprehensive assessments show that the CDO capabilities are very effective when used with microgrids.

Application of simulated annealing algorithm in multi-objective cooperative scheduling of load and storage of source network for load side of new power system

To improve the adaptability of grid load collaborative scheduling, a multi-objective collaborative scheduling method based on a simulated annealing algorithm for the load storage of grid loads on the load side of a new power system is proposed. Local bus transmission technology is adopted to collect the dynamic parameters of energy network load energy storage on the load side of the new power system. The collected load dynamic parameters are fused with energy distribution state parameters to extract the state characteristics of energy network load storage. The simulated annealing algorithm is adopted to realize the load characteristics fusion and adaptive scheduling processing of energy network on the load side of the power system, and the spectral characteristics of the load dynamic parameters are extracted. The dynamic scheduling method of simulated annealing is used to realize the multi-objective optimization of dynamic load of energy network. Based on the co-optimization results of simulated annealing, the optimization application of the simulated annealing algorithm in the multi-objective co-scheduling of loads and energy storage in a new power system is realized. The experimental results show that after 400 iterations, the control convergence accuracy of the proposed method reaches 0.980, which is significantly better than that of the comparison method, and performs well in terms of scheduling efficiency improvement, load scheduling stability, scheduling time and energy waste ratio, proving that the method has good multi-objective integration and strong optimization ability in the scheduling process, and improves the load balanced scheduling and adaptive control ability of the power system.

LIA: Latency-Improved Adaptive routing for Dragonfly networks

Low-diameter network topologies require non-minimal routing, such as Valiant routing, to avoid network congestion under challenging traffic patterns like the so-called adversarial. However, this mechanism tends to increase the average path length, base latency, and network load. The use of shorter non-minimal paths has the potential to enhance performance, but it may also introduce congestion depending on the traffic patterns. This paper introduces LIA (Latency-Improved Adaptive), a routing mechanism for Dragonfly networks which dynamically exploits minimal and non-minimal paths. LIA harnesses the traffic counters already present in contemporary switches to determine when it is safe to shorten non-minimal paths and to adjust routing decisions based on their information about the network conditions. Evaluations reveal that LIA achieves nearly optimal latency, outperforming state-of-the-art adaptive routing mechanisms by reducing latency by up to 30% while maintaining stable throughput and fairness.

Comparative Analysis of PI and PR Control Strategies for Enhancing Voltage Quality in D-Statcom-Based Distribution Networks

In recent years, the rise in the number of sensitive loads in distribution networks has highlighted the importance of not only ensuring the continuity of electricity supplies but also maintaining high power quality for consumers. In the present era, one of the most prevalent power quality issues in power systems is voltage sag, which is responsible for over 80% of the observed problems. Although the duration of the disturbance is relatively brief, typically between 10 ms and 1 minute, it can have a significant impact on the production process due to a minor deviation from the rated voltage. The disturbance can occur within a second, but the restoration process can take several hours. In order to enhance the power quality of the grid, it is essential to regulate reactive power. The objective of this study is to utilise a D-STATCOM for the correction of voltage by connecting it in parallel to the bus connection point between the grid and the loads. Modelling and simulation studies have been conducted to investigate the contribution of D-STATCOM with PI (proportional-integral) or PR (proportional resonance) controllers to the system in response to various fault scenarios that cause voltage sags by commissioning various loads at low voltage (0.4 kV). The simulation of both models was conducted using the PSCAD/EMTDC program. The performance of both controllers is evaluated through a comparison with the PSCAD/EMTDC simulation program. The results demonstrate that the D-STATCOM responds to short-term voltage sags caused by a fault or other system disturbances with a high degree of efficiency, rapidly restoring the bus voltage to a level close to the nominal value. This effectively mitigates the potential disruptions associated with voltage collapse. Furthermore, the analysis reveals that D-STATCOMs employing PI and PR controllers exhibit a combination of advantages and limitations in their performance.

StegoEDCA: An Efficient Covert Channel for Smart Grids Based on IEEE 802.11e Standard

Smart grids are continuously evolving, incorporating modern technologies such as Wi-Fi, Zigbee, LoRaWAN or BLE. Wi-Fi are commonly used to transmit data from measurement systems, distribution control and monitoring systems, as well as network protection systems. However, since Wi-Fi networks primarily operate on unlicensed frequency bands, this introduces significant security risks for sensitive data transmission. In this paper, we propose a novel and highly efficient covert channels that utilize IEEE 802.11 Enhanced Distributed Channel Access (EDCA) for data transmission. It is also the first ever covert channel that employ three or four independent covert mechanisms to enhance operational efficiency. The proposed mechanism is also the first to exploit the Transmission Opportunity (TXOP) period and the access categories of the EDCA function. The protocol was developed and tested using the ns-3 simulator, achieving excellent performance results. Its efficiency remains consistent even under heavy network load with additional background traffic. These covert channels provide an innovative solution for securely transmitting large volumes of data within the smart grid.

Evaluating the Potential and Challenges of LPWAN Technologies like LoRa and Sigfox for Long-range Communication in IoT Systems

In this paper, we consider the performance of two Low Power Wide Area Network technologies, namely LoRa and Sigfox, placing special emphasis on the possibility of efficiently coping with massive numbers of devices in IoT settings with high densities. Finally, we apply MATLAB simulation for evaluating all mentioned performance figures: power consumption, data throughput, latency, and network congestion for various network loads. LoRa has advantages in terms of high data throughput and range but has the disadvantage of high power consumption and latency in a highly populated urban environment. Sigfox is beneficial for low-power, low-data-rate applications - providing long battery life but with strict data throughput and increased latency. LoRa has a much higher latency, which is the time taken for a packet to travel from the sender to the receiver, as it is prone to congestion and interference in dense IoT setups compared to Wi-Fi. Furthermore, the performance of both technologies shows different inefficiencies as the node density increases. We discuss the pros and cons of these technologies followed by the implementation guidelines for smart cities and industrial automation. The technology choice of LPWAN will depend upon the type of applications and requirements in terms of data size, latency, and power consumption.

Improving High Availability Services Using KVM Full Virtualization

In the digital era, critical sectors like banking, ISPs, and cloud-based services depend on robust, secure networks to ensure uninterrupted service availability. Key operations such as financial transactions, customer support, and online services rely on high system uptime, essential for business continuity and customer trust. Network and server administrators play a vital role in maintaining high availability (HA) for systems like file servers, web servers, database servers, backup systems, and enterprise applications. HA infrastructures often use multiple servers working collaboratively to ensure continuous service. If one server fails, another takes over seamlessly, preventing significant disruptions. Virtualization technology enhances HA systems by hosting services on virtual machines (VMs) that can migrate between physical nodes within a cluster. This ensures uninterrupted service even during hardware failures. This paper examines the use of open-source tools for managing HA services, focusing on cost efficiency and system optimization. The Heartbeat program facilitates real-time VM migration across cluster nodes, maintaining service continuity. To improve this process, the Perf+ algorithm is introduced. Perf+ optimizes CPU performance, reduces memory usage, and minimizes downtime by transferring fewer bytes of modified memory pages during migration, reducing CPU and network load. The proposed solution is implemented in an experimental HA system to evaluate the performance of real-time VM migrations. The research analyzes the impact of these migrations on system efficiency, aiming to advance HA infrastructures in virtualized environments through optimized resource utilization and reduced operational interruptions.

Effects of negative spot market prices on electricity distribution network loading, experiences from the Finnish distribution system

Effects of negative spot market prices on electricity distribution network loading, experiences from the Finnish distribution system

End-to-end latency upper bounds and service chain deployment algorithm based on industrial internet network

The diverse service requests in industrial Internet networks require flexible and efficient service chain deployment to ensure the quality of service (QoS). However, current deployment algorithms for service chains are primarily designed to guarantee only low end-to-end latency; they often overlook the amount of service chains that can be accommodated by the network and could lead to severe network load imbalances, significantly reducing service efficiency and causing serious network congestion issues. To address the above issues, we develop a mathematical model of the network topology and service request chains by integrating Network Function Virtualization (NFV) and Software Defined Networking (SDN). Utilizing network calculus theory, we derive the upper bound of end-to-end delay for service chain routing and analyzed the relationship between the upper bound of service chain routing delay and the resource allocation of Virtual Network Function (VNF) nodes. Based on the aforementioned model, we propose a novel service chain deployment algorithm named the Delay-Aware Load-Balanced Routing Algorithm (DLBRA). DLBRA comprehensively considers network traffic load balancing and end-to-end latency of service chains, rationally allocating VNF node resources to complete the determined service chain routing deployment. Experimental results indicate that, compared to the shortest path and load balancing algorithms, DLBRA not only ensures that the end-to-end delay of the service chain meets its QoS requirements, but also effectively reduces network load imbalance, significantly increasing the number of service chain requests that the network can accommodate. Additionally, DLBRA provides tailored deployment guidance for different types of service chains, such as latency-sensitive and data-intensive service chains, ensuring optimal utilization of network resources. This algorithm enhances the efficiency of service chain deployment in industrial internet scenarios and possesses broad application potential in other network environments where delay optimization and load balancing are critical, such as intelligent transportation, cloud computing, and 5G networks.

Multi-rate sampled-data observer-based networked load frequency control for multi-area interconnected power systems under multiple cyber-attacks

This paper addresses the problem of adaptive event-triggered load frequency control (LFC) for multi-rate sampling multi-area interconnected power systems with integrated renewable energy. Different network channels can be vulnerable to multiple cyber-attacks, including deception attacks and DoS attacks. First, a matching mechanism is constructed to fuse the multi-rate sampled data, and a set of observers based on multi-rate sampled data is designed to independently estimate the states of the individual power systems. A networked sampled-data PI controller is then developed by introducing a new synchronous sampling scheme and an adaptive event-triggered mechanism. A co-design approach is proposed that integrates decentralized PI controllers, observers, and adaptive event-triggered mechanisms to achieve exponential mean-square stability with a given H∞ performance level for the networked LFC system in the presence of multiple cyber-attacks and multi-rate sampling. Finally, the effectiveness of the proposed method is verified through an example of an interconnected power system.

Dynamic switching of transmission modes hydroacoustic communication MAC protocols

In recent years, the hydroacoustic communication MAC (Medium Access Control) protocol has attracted wide attention. Hydroacoustic communication networks suffer from issues such as long propagation delays and rapid dynamic changes in network load, which prevent the maximization of network performance through the use of a single transmission mode. In this paper, we propose a Dynamic Switching Transmission MAC protocol called the DSTM-MAC protocol. Firstly, the protocol realizes the design of dynamic switching transmission mode by adapting to changes in network load, introduces a more accurate model for total system delay, and computes parameters such as the switching threshold L and the optimal fixed contention window. Secondly, it carries out parameter design for different transmission modes and realizes dynamic concurrent transmission based on these modes, ultimately achieving the goal of maximizing network performance improvement. Finally, experiments demonstrate that the DSTM-MAC protocol surpasses the traditional DCF protocol in terms of end-to-end delay and system throughput.

Reliable Data Delivery and Load Balancing in Improved Energy Efficient RPL Routing Protocol for Low Power and Lossy Networks

ABSTRACTThe energy‐constrained nodes and tree‐like topology of Low‐Power and Lossy Networks (LLNs) frequently result in serious problems when energy bottlenecks occur. This occurrence affects the network performance in terms of node failure, lower connectivity, and higher energy consumption. This paper proposes a novel solution to these problems by integrating an Energy Efficient Multipath Routing Protocol (EM‐RPL) with a Load‐Based Energy Efficient RPL (LBEE‐RPL) protocol. To address energy bottlenecks, the proposed EM‐RPL protocol initiates the crucial mechanisms as subnode switching mechanism and an improved Trickle timer. The network's energy load is balanced by the subnode switching mechanism, which redistributes traffic away from overloaded nodes. The enhanced Trickle timer guarantees prompt identification and response to energy shortage conditions. This method enhances overall network performance while addressing the issue of data imbalance in energy consumption. An extensive simulation shows the proposed EM‐RPL significantly increases network lifetime and energy efficiency. Specifically, integrating these protocols leads to a notable reduction in power consumption and a rise in the network's operational lifespan. The important problem of energy management in LLNs is effectively resolved by this work, which offers improved performance and sustainability for a range of Internet of Things (IoT) and related fields applications.

Использование энергии рекуперации для обогрева стрелочных переводов от контактной сети постоянного тока с напряжением 3 кВ

Purpose: to create a system for the use of energy recovery on sections of railways electrified with direct current with a voltage of 3 kV. The proposed system should make it possible to use the energy of regenerative braking generated by an electric locomotive or a DC electric train for heating switches during regenerative braking and insufficient power of traction consumers or their absence in the traction network. Methods: analysis of statistical data on the modes of operation of electric rolling stock and traction network, execution of train schedules, analysis and synthesis of circuit solutions, study of domestic and foreign experience in the utilization of energy recovery in railway transport. Results: the principle of using energy recovery, which cannot be used due to the low load in the contact network, for heating switches, increasing the energy efficiency of transportation on electrified rail transport, is proposed. Practical significance: the possibility of using electric energy recovery of electric rolling stock for heating switches in winter is shown, if it is impossible to consume it by other electric locomotives and electric trains. This system can be used in areas electrified with direct current with freight and passenger traffic, which operates electric rolling stock (EPS) with regenerative and regenerative-rheostatic braking.

Network structure of phasic and tonic irritability in adults: A Bayesian Gaussian graphical model.

Tonic (i.e., irritable mood) and phasic (i.e., temper outbursts) irritability are distinct constructs in childhood, yet there has been little work investigating their relations in adulthood. In particular, no study has examined the relations between these symptoms in the daily lives of adults. The present study seeks to address these gaps by applying a Bayesian Gaussian graphical model to daily diary data of tonic and phasic irritability in adults. A total of 122 participants (Mage = 34.94 [SD = 12.3] years, 52 % female) completed up to 18 days of daily diary, with questions on both tonic and phasic irritability. Contemporaneous, temporal, and between-person networks were constructed using 5 items of tonic and 11 items of phasic irritability. Predictability, centrality, density, and network loadings were also calculated. Five main findings emerged. First, phasic and tonic irritability were connected in our contemporaneous and temporal networks but distinct in our between-person network. Second, phasic irritability symptoms demonstrated the most importance and influence across networks. Third, tonic irritability symptoms best connected the two subnetworks. Fourth, phasic irritability symptoms appeared to have the potential to be more responsive to interventions. Fifth, tonic irritability demonstrated robust associations with daily functional impairment. Tonic and phasic irritability symptoms demonstrate unique associations in the daily lives of adults. Future work should examine the differential potential of these symptoms to respond to targeted interventions.

Optimization Problem for Probabilistic Time Intervals of Quasi-Deterministic Output and Self-Similar Input Data Packet Flow in Telecommunication Networks

Introduction. When managing traffic at the packet level in modern telecommunication networks, it is proposed to use methods that transform a self-similar stochastic packet flow into a quasi-deterministic one. To do this, it is required to apply complex probabilistic laws of distribution of self-similar flows. From the literature, methods of balancing the network load are known, which, with the problem indicated above, contribute to increasing the efficiency of telecommunication systems. However, there is no strictly mathematical solution to find out the optimal probabilistic characteristics of the output flow, based on the input flow. The presented research is intended to fill this gap. Its objective is to create a method for determining the optimal probabilistic characteristics of the packet flow, using the minimum value of the proximity measure of the self-similar input and quasi-deterministic output flows.Materials and Methods. To solve the research problem, the parameters of the output flow distribution were selected so that the approximation function was close to 𝛿𝛿-function. The Kullback-Leibler divergence was used as a proximity measure of the input and output distributions of time intervals. Methods of set theory, metric spaces, multidimensional optimization, and teletraffic were used. The solution algorithm included minimization of the Kullback-Leibler divergence and the limit passage to 𝛿𝛿-function.Results. A probability distribution is shown — an approximation of 𝛿𝛿-function, which maintains the equality of time intervals of a quasi-deterministic output packet flow. A method for transforming a self-similar input flow into a quasideterministic output flow is presented. The Kullback–Leibler divergence was used as a measure of their proximity. The minimum of the Kullback-Leibler divergence between the input and output flows with a normal distribution was achieved in the case of equality of the mathematical expectations of these flows. Using the passage to the limit, it has been established that time interval T between packets of the quasi-deterministic output flow must be equal to the mathematical expectation of the time intervals between packets of the input self-similar flow. To obtain a quasi-deterministic flow, the passage to the limit is performed for the found value of the mathematical expectation at σ → 0.Discussion and Conclusion. The application of this method will reduce the negative impact of self-similarity of network traffic on the efficiency of the telecommunication network. The use of quasi-deterministic flows makes it possible to predict the load of network resources, which can be the basis for improving the quality of user service. Two difficulties associated with calculations and practical implementation of the solution are eliminated. Firstly, it is difficult to use the delta function as a function of the output flow distribution density. Secondly, there are no ideal deterministic flows in the operation of telecommunication networks. The proposed method has great potential in the design and optimization of communication networks.

A Data Alignment Method for Network Packet Capture Based on DBSCAN

This paper investigates the issues of packet alignment and consistency among PLC devices based on industrial network environments, aiming to ensure the integrity and accuracy of packets from sender to receiver. To achieve this goal, we propose an anomaly detection method that combines the DBSCAN clustering algorithm with the 3-sigma principle to identify and handle abnormal packets that may occur during transmission. By comparing the data between the sending and receiving ends, and analyzing based on timestamps and data content, we validate the alignment of packets in the network environment. Experimental results demonstrate that the proposed method effectively detects and corrects packet loss or delay jitter, thereby enhancing the reliability of communication between PLC devices and the consistency of data transmission. The scheme presented in this paper enables quicker and more precise identification of packet loss and delays, adapting well to various network load conditions. Further experimental analysis indicates that this method excels in reducing both false positive and false negative rates, and it exhibits good scalability, making it applicable to data alignment and consistency verification in other industrial automation scenarios. Ultimately, this novel solution provides stability and accuracy for data transmission among devices in a network environment.

Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction.

In network function virtualization, the resource demand of network services changes with network traffic. SFC migration has emerged as an effective technique for preserving the quality of service. However, one important problem that has not been addressed in prior studies is how to manage network load while maintaining service-level agreements for time-varying resource demands. Therefore, we propose the Resource Predictive Load Balancing SFC Migration (RP-LBM) algorithm in this paper. The algorithm uses CNN-AT-LSTM to predict VNF resource demands in advance, eliminating the delays associated with dynamic migrations and determining the optimal migration timing. It leverages the PPO algorithm's perceptual capabilities in complex environments to develop SFC migration strategies and ensure network load balancing. Additionally, it reduces the number of subsequent migrations and minimizes the service interruption rate. The simulation results show that the service interruption rate of the RP-LBM algorithm is on average 27.3% lower than that of the passive migration method. The PPO-based migration algorithm has lower SFC migration times and service interruption rates compared to the DQN algorithm, ensuring service continuity with low migration costs.

Green Optimization with Load balancing in Wireless Sensor Network using Elephant Herding Optimization

Wireless sensor networks (WSNs), which provide sensing capabilities to Internet of Things (IoT) equipment with limited energy resources, are made up of specialized transducers. Since substitution or re-energizing of batteries in sensor hubs is extremely difficult, power utilization becomes one of the pivotalmattersin WSN. Clustering calculation assumes a significant part in power management for the energy-compelled network. Optimal cluster head selection suitably adjusts the load in the sensor network, thereby reduces the energy consumption and elongates the lifetime of assisted sensors. This article centers around to an appropriate load balancing and routing technique by the utilization recently developed of Elephant Herding Optimization (EHO) algorithm that alternates the cluster location amongst nodes with the highest energy. The Scheme considered various parameter residual energy, initial energy and an optimum number of cluster head for the next cluster heads selection. The proposed model increases the lifetime of the network by keeping more nodes active even after the 2700th round. The experiment results of the trials show that the proposed EHO-based CH selection strategy outperforms the cutting-edge CH selection models.

Optimizing System Performance with Adaptive Algorithms for Signal Processing in Wireless Communication Networks

In modern wireless communication networks, the need for high-speed, reliable, and efficient data transmission is paramount. Signal processing algorithms play a critical role in optimizing network performance, especially under dynamic conditions such as varying network loads and environmental interference. This paper presents an in-depth study of adaptive signal processing algorithms designed to improve the performance of wireless communication systems. We examine various techniques, including adaptive filtering, channel estimation, and interference cancellation, and assess their effectiveness in optimizing signal quality and network throughput. Through simulation and real-world case studies, the paper demonstrates how these algorithms can be applied to mitigate signal distortion and reduce data transmission errors in wireless networks. The results show that adaptive algorithms significantly enhance the overall system performance, particularly in environments with fluctuating signal conditions. The paper concludes with recommendations for further development in adaptive algorithms for future wireless communication standards, particularly 5G and beyond.

Towards Deterministic-Delay Data Delivery Using Multi-Criteria Routing over Satellite Networks

The satellite Internet can cover up to 70% of the surface of our planet Earth to provide network services for nearly 3 billion people. As such, it is promising to become the building block of future 6G networks. The satellite Internet is capable of providing uniform communication capacity to every part of the Earth’s surface, due to its uniform and symmetrical constellation structure, while the uneven distribution of ground populations leads to globally uneven traffic delivery requests, incurring a mismatch between the capacity and traffic transmission demands. As such, traditional single-criteria (e.g., shortest delay) routing algorithms can lead to severe network congestion and cannot provision delay-deterministic data delivery. To overcome this bottleneck, we propose a multi-criteria routing and scheduling scheme to redirect time-tolerant data, thus preventing congestion for time-sensitive data, based on the spatiotemporal distribution of data traffic. First, we construct a traffic spatiotemporal distribution model, to indicate the network load status. Next, we model the satellite network multi-criteria routing problem as an integer linear programming one, which is NP-hard and challenging to solve within polynomial time. A novel link weight design based on both the link delay and load is introduced, transforming the mathematical programming problem into a routing optimization problem. The proposed correlation scheduling algorithm fully utilizes idle network link resources, significantly improving network resource utilization and eliminating resource competition between non-time-sensitive and time-sensitive services. Simulation results show that compared with traditional algorithms, the proposed method can increase the throughput of time-sensitive data by up to 20.8% and reduce the packet loss rate of time-sensitive services by up to 76.8%.