Network Load Research Articles

Techno-economic modeling of a preventive-corrective strategy based on black-start services to resilience enhancement of distribution networks

High-impact with low-probability (HILP) events such as natural disasters or cyber-physical attacks severely affect power systems. The distribution networks experience significant challenges during HILP events. So, strategies need to be developed to enhance distribution networks' resilience. Hence, in this paper, a resilience enhancement strategy based on the potential of multi-microgrids is developed. The proposed strategy has a two-layered structure consisting of preventive and corrective measures within the hierarchical centralized energy management framework. The preventive measures are based on the black-start bilateral contract between the active distribution network operator and the multi-microgrids owner. The corrective measures are based on islanding microgrids during the event. The LP-Metric-based multi-objective optimization approach is employed for economic-resilient operational planning of the active distribution network. Moreover, a scenario-based stochastic approach is utilized to deal with renewable resources and load uncertainties. On the modified IEEE 33-bus active distribution network, the effectiveness of the proposed strategy is evaluated using the supplied load as a resilience index. The proposed black-start strategy has resulted in an increase of 181.57 % in the supplied load of the distribution network, while a reduction of 83.12 % in its curtailed load has been achieved. Consequently, the resilience index increased by 84 %. Additionally, the distribution system operation cost was reduced by 87.44 %, while the multi-microgrid owner's profit increased by 1004.68 %. The obtained results demonstrate that distribution system resilience has been enhanced within an economic framework, which validates the effectiveness of the proposed strategy.

Distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimization

The increasing complexity of Compute Continuum environments calls for efficient resource optimization techniques. In this paper, we propose and evaluate three distributed designs of a genetic algorithm (GA) for resource optimization, within an increasing degree of distribution. The designs leverage the execution of the GA in the infrastructure devices themselves by dealing with the specific features of this domain: constrained resources and wide geographical distribution of the devices. For their evaluation, we implemented a benchmark case using the NSGA-II for the specific problem of optimizing the application placement, according to the guidelines of our three distributed designs. These three experimental scenarios were compared against a control case, representing a traditional centralized version of this GA algorithm, evaluating solution quality and network overhead. The results show that the design with the lowest distribution degree, which keeps centralized storage of the objective space, achieves comparable solution quality to the traditional approach but incurs a higher network load. The second design, which completely distributes the population between the workers, reduces network overhead but exhibits lower solution diversity while keeping enough good results in terms of optimization objective minimization. The second design demonstrates the highest overall efficiency in optimization performance and network cost. Finally, the proposal with a distributed population that only interchanges solutions between the workers’ neighbors achieves the lowest network load but with compromised solution quality.

Optimized DM-RS Configuration for Improved 5G New Radio Network Capacity and Performance

Network load in mobile networks is continuously growing, putting pressure on mobile operators to deliver target network performance and user experience. This paper focuses on network capacity improvement through increased spectral efficiency, which can be achieved with overhead reduction by optimizing the demodulation reference signal (DM-RS) configuration, which is one of the channels/reference signals with the largest contribution to overhead. In a high-speed scenario, the Doppler effect noticeably influences the temporal nature of the channel such that a channel interpolation process is required within a slot. However, increasing the number of symbols used for DM-RS has a negative impact on capacity. The Doppler effect was analyzed for various 5G NR configurations of operating frequency and subcarrier spacing (SCS), and various use cases were considered using user equipment (UE) speed as the main parameter. For suitable use cases, the DM-RS configuration was optimized in networks with live traffic. The impact of the configuration change on 5G/NR spectral efficiency, user experience and link adaptation performance was assessed through a deep-dive analysis of active measurements, available performance Management (PM) counters and key performance indicators. An optimized DM-RS configuration is proposed, and it is demonstrated to achieve gains of 5–15%, depending on the metric used, use case analyzed, network load, traffic mix and other relevant network characteristics such as topology and clutter type.

COMPARATIVE ANALYSIS OF FEDERATED MACHINE LEARNING ALGORITHMS

In this paper, the authors propose a new machine learning paradigm, federated machine learning. This method produces accurate predictions without revealing private data. It requires less network traffic, reduces communication costs and enables private learning from device to device. Federated machine learning helps to build models and further the models are moved to the device. Applications are particularly prevalent in healthcare, finance, retail, etc., as regulations make it difficult to share sensitive information. Note that this method creates an opportunity to build models with huge amounts of data by combining multiple databases and devices. There are many algorithms available in this area of machine learning and new ones are constantly being created. Our paper presents a comparative analysis of algorithms: FedAdam, FedYogi and FedSparse. But we need to keep in mind that FedAvg is at the core of many federated machine learning algorithms. Data testing was conducted using the Flower and Kaggle platforms with the above algorithms. Federated machine learning technology is usable in smartphones and other devices where it can create accurate predictions without revealing raw personal data. In organizations, it can reduce network load and enable private learning between devices. Federated machine learning can help develop models for the Internet of Things that adapt to changes in the system while protecting user privacy. And it is also used to develop an AI model to meet the risk requirements of leaking client's personal data. The main aspects to consider are privacy and security of the data, the choice of the client to whom the algorithm itself will be directed to process the data, communication costs as well as its quality, and the platform for model aggregation.

Experimental test results of an automatic voltage regulator with independent phase voltage controller

The growing number of distributed renewable energy sources and dynamic constant-power loads (e.g. electric vehicle charging stations) pose new challenges for network operators. These changes result in alterations to network load profiles and load flows, leading to greater voltage volatility. One effective solution to these problems can be the use of automatic voltage regulators (AVRs), which stabilize and symmetrize voltage output, whether at distribution transformers (DTs) or elsewhere in the distribution network. The device developed by the authors consists of two bidirectional power converters and three single-phase transformers connected in series to the low-voltage grid as a stabilizer. The proposed control system provides accurate and fast regulation of the AVR’s output voltage (within the range of ±10% of the nominal grid voltage), with each phase being independently adjusted, regardless of the type of power load. The article includes test results demonstrating selected functionalities of the developed AVR. The physical model of the device discussed in the article is a research component of the LINTE2 laboratory of the Gdansk University of Technology.

A Regeneration Model for Mitigation Against Attacks on HTTP Servers for Mobile Wireless Networks

The widespread expansion of the internet has fueled a global surge in the utilization of various online transactions, with a significant portion of such services operating on mobile web platforms. Simultaneously, the deployment of innovative Mobile Ad Hoc Network (MANET) technologies and mobile applications has grown as solutions for diverse tasks. Unfortunately, this progress has attracted the attention of hackers, who continually devise new strategies to exploit the vulnerabilities inherent in mobile networks. This study aims to address the escalating challenges posed by cyber threats in the era of widespread internet expansion, particularly focusing on securing mobile web platforms against sophisticated attacks such as Structured Query Language Injection (SQLi) for web-based database solutions and Denial of Service (DoS/DDoS) for various applications. In response to the identified vulnerabilities, this paper proposes an HTTP regeneration (HReg) model that not only detects various cyber-attacks but also ensures the uninterrupted provision of critical services during such incidents. The model introduces an innovative regeneration algorithm capable of scanning both the connection channel and web application to detect attacks, creating survivable connections within the underlying TCP engine to replace compromised ones during an ongoing attack. In simulated environments using OMNeT++, where the server is subjected to attacks, the experimental results demonstrate the efficacy of the model. The response and performance metrics, including throughput (73%), delivery ratio (68.8%), delay (3s), and network load, showcase the model's ability to detect and neutralize attacks. A comparison with state-of-the-art approaches highlights the superior performance of the regeneration model, attributed to its additional survivability layer. While the regeneration model proves robust in simulated environments, its real-world application may encounter limitations. Future research should explore these limitations to enhance the practical applicability of the proposed model. The proposed HReg model's resilient performance under attack conditions ensures the survivability of web-based applications. This innovative approach offers practical implications for securing mobile web platforms, providing continuous delivery of critical services even in the face of persistent and evolving cyber threats. This research addresses a significant gap in existing efforts by not only focusing on attack detection but also emphasizing the development of a survivable TCP connection for HTTP servers during attacks. The introduced regeneration model stands out for its unique ability to maintain service continuity, showcasing originality in the approach to cybersecurity in the context of web-based applications.

Review on development prospect of operation scheduling strategies for flexible interconnected distribution network in renewable energy-penetrated power system

As the renewable energy integrates in the power system, the interactions between source, load, and storage equipment in the distribution network become frequent. The original distribution network could not meet the growing demand of the power grid. The use of controlled power electronic devices for flexible interconnection transformation of the distribution network could help improve the controllability, reliability, and safety of the power system and promote the consumption of distributed power sources. It is an important means of evolving towards future intelligent distribution networks. Typical devices for flexible interconnection are discussed of the operating characteristics. In addition, the core technologies of flexible interconnected distribution network system are introduced from the view of point of dispatching. Finally, to achieve a wider application of flexible interconnected distribution networks, the development trend is prospected.

Modelling of Timestamp Processing within Transport Network Equipment

The modern telecommunication networks are forming on the basis of different transmission and data processing technologies. High load on the transport layer nodes demands quality synchronization of equipment that works on it. The transport network is an important element which delivers the time scale to slave nodes. Using the time-division multiplexing for the high-speed channel processing with the use of wavelength division multiplexing (OTN, Optical Transport Network) and classic Ethernet networks as a transport layer systems needs the precision time synchronization which can be achieved with the use of Precision Time Protocol (PTP). The modeling apparatus of PTP messages exchanging within OTN multiplexers and IP-network based on the routers are suggested. The packet transferring through the network are given as sequence of simple processing steps. The impact of a network load on PTP information processing capacity on the routers and efficiency of the Forward Error Correction algorithm for OTN equipment are analyzed. The experimental distribution of variable delays on that network elements are given.

Comprehensive Evaluation of Urban Road Network Capacity Using Fractal Theory in the Context of the Internet of Things

Abstract In order to make a good assessment of road traffic status, this paper proposes a comprehensive evaluation method of urban road network capacity based on soft computing under the background of Internet of Things. The focus of the research is to use a variety of soft computing technologies such as Hausdorff dimension simplification method, branch dimension measurement algorithm, multifractal method, etc., to analyze the overall traffic load and guide the traffic channelization in an optimal way, considering various factors comprehensively. The model helps to optimize infrastructure and reduce road traffic congestion. This avoids traffic congestion, ultimately reducing the carbon footprint and promoting a green environment. This method analyzes the network capacity, network load and corresponding conversion coefficient of all levels of highway. Based on the multi-component linear regression method, this paper evaluates the urban road network capacity, and constructs the urban road network capacity planning model according to the traffic volume constraints and travel time constraints. The test results show that under the application of this method, the road capacity of the planned route exceeds 4000 pcu. The pcu is a passenger vehicle unit that measures the extent to which different types of vehicles have an impact on road capacity. When the capacity of a planned road exceeds 4000 pcu, it means that the road can carry a large number of different types of vehicles at the same time, and the average operating speed of the line can reach 42.3 km/h. After 5 years of planning, the efficiency of road use is still above 75 %. It can be proved that this method can obtain the optimal traffic path through better route planning and avoid traffic congestion and pollution.

A context-aware energy saving information centric (CAES) model for wireless communication network

The CASE model improves the traditional wireless communication approach by deploying an energy-efficient information-centric algorithm that also enables the tiny low-powered communication nodes of the wireless network to work efficiently with the context-aware energy-saving interface. CAES introduces buffer optimization and a smart routing process among all the low-capacity tiny nodes. Moreover, the gathering of Context-aware energy-saving information also takes care of the performance as well as the management of the traffic load of the entire wireless network. Toward the objectives of the proposed algorithm, which ensure a high ratio of content delivery with an efficient energy saving and energy utilizing scheme, this paper is trying to design a type of Wi-Fi environment that carry the data packet and delivery services carefully without falling down the connection or losing the data contents. In addition to this, this paper concentrate on the measurement of Expected Production Quality (EPQ) values, using loss and quality functions based on various quality parameters to be calculated as one of the measured parts of this research work that defines the ratio of reliability and efficiency of CAES. Further, the implementation of CAES defines the improvement of this research work in the form of managing various quality of communication parameters such as packets delivery ratio, end-to-end Delay and throughput measurement. This manuscript proposes a novel version of the context-aware routing mechanism in a wireless network since traditional wireless communication protocols like SADODV, RAEED-EA are not vulnerable to offer best of communication services. This work aims to manage common communication quality parameters such as Packet Delivery Ratio, End to End Delay and Throughput to be improved using information-centric route management services under denial-of-service (DoS) attacks by implementing context-aware energy saving (CAES), which simultaneously deploys energy efficiency and buffer optimization. This paper simulates and compare the resulting parameters with traditional protocols which defines the importance and role of CAES model for the future of Communication Technology.

Competitive Pricing for Resource Trading in Sliced Mobile Networks: A Multi-Agent Reinforcement Learning Approach

The emergence of network slicing as a flagship technology in 5G networks has not only enhanced network expansion and flexibility in resource management for service continuity, but also provided an avenue for establishing a viable market for resource sharing. To optimize the network's resource usage, stakeholders are encouraged to take pragmatic steps toward dynamic resource sharing. This paper designs a techno-economic model for the strategic interactions among multiple competing mobile virtual network operators (MVNOs) and their users in a trading marketplace. We formulate the dynamic pricing problem as a two-stage Stackelberg game, where the MVNOs are leaders, and the users are followers. In the first stage, the MVNOs compete to set their differentiated unit prices using a negotiation mechanism while considering system-level network load. Then, the users decide their purchasing volumes to match the prices of the MVNOs. We transform the game-based optimization problem into a stochastic Markov decision process (MDP) problem and propose a multi-agent deep Q-network (MADQN) method that obtains an optimal solution for the formulated game. Simulation results and analysis reveal that the proposed algorithm achieves convergence under the competitive pricing scheme (CPS) and independent pricing scheme (IPS) while enhancing MVNOs and users' utilities at acceptable levels.

СПРОЩЕНА ІМІТАЦІЙНА МОДЕЛЬ НАВАНТАЖЕННЯ В ЗАМКНЕНІЙ МЕРЕЖІ КОМУТАЦІЇ ПАКЕТІВ

An approach to simplified simulation modeling of packet switching network load has been developed. The simulated representation of network processes is based on a simplified mathematical model of the packet switching network, which is based on a dis-cretized description of the load states of the network nodes and corresponds to the repre-sentation of the current state of the network in the tasks of managing its load. Changes in the node's filling state are modeled as a process of reproduction and death, and in the full load model, two factors of change are taken into account: the node's exchange of load with network users and transit load flow passing through it. To study an artificially se-lected "part of the network functioning process" - an approach to transit load modeling in a load-locked network is proposed. The approach allows for significant simplifications and ensures the leveling of the negative impact of simplifications on "load closure" (en-sures the rule of constant amount of load in a closed network). The developed simulation model, firstly, creates the basis for the improvement of the complete model, and secondly, it is convenient for working out routing algorithms when analyzing them from the point of view of indicators of the ability to "influence" the load in the network. The obtained re-sults are generalized, but attention is focused on the example of satellite packet switching networks.

Assessing the Psychometric Properties of the Functional Analytic Psychotherapy Intimacy Scale (FAPIS) in a Spanish-Speaking Population

Intimacy is an interpersonal repertoire related to physical and behavioral health indicators. Functional analytic psychotherapy (FAP) is a behavioral intervention that has demonstrated utility in improving intimacy repertoires, particularly in experimental single-case designs. The FAP Intimacy Scale (FAPIS) was developed with the aim of using a theoretically sound measure to evaluate FAP effects on a large scale. This study sought to culturally adapt and validate the FAPIS for Spanish-speaking populations. A total sample of 509 Spanish-speaking participants completed the survey. Exploratory and confirmatory factor analyses were conducted to examine FAPIS psychometric properties. A network analysis was also performed to assess the relation among scale factors. As expected, the FAPIS Spanish version demonstrated a good fit, meeting the same three-factor structure as the original version. Network loading along three factors was also found, and item 3 was identified as the most central item in the scale. Low and negative correlations among FAPIS, experiential avoidance, emotional sensitivity, and social impact were found. Findings showed that FAPIS is a valid and reliable measure for assessing intimacy in Spanish-speaking populations.

Power generation–network–load–energy storage co-planning under uncertainty

With the aggregation of renewable energy in the power system, the uncertainty caused by the renewable energy affects the planning and operation of power systems. Meanwhile, the existing planning models fail to consider renewable energy uncertainty methods, specifically concerning renewable energy confidence and future possible scenarios; thus, a confidence-based scenario cluster method is presented. A novel generator, network, load, and energy storage (GNLS) co-planning model is proposed in the paper. First, a confidence-based scenario cluster is built, which can reflect uncertainties by clustering and analyzing wind, solar, and load. Second, the proposed model focuses on load and energy storage co-planning, and in addition, relevant flexible indices are used to assess the model. Finally, the GNLS co-planning model is built as a bi-level stochastic model on continuous time scales. The model is solved using the Benders decomposition algorithm. The method in this paper is validated using an IEEE RTS 24-bus and a real test system in China to demonstrate the reduction in renewable energy curtailment and optimization of economic factors in power system planning.

Optimal adaptive decentralized under-frequency load shedding for islanded smart distribution network considering wind power uncertainty

The increasing share of renewable distributed generations has raised a challenge about the frequency stability. Under-frequency load shedding (UFLS) can deal with this challenge, but it usually considers a prorated load-shedding strategy and neglects the impact of uncertainty from renewable generation. This may result in unnecessary additional economic losses from the system blackout. In this paper, a risk-decision-based optimal adaptive decentralized UFLS method is proposed to coordinate the distribution of shedding load in the smart distribution network to avoid unnecessary additional economic losses, while considering the wind power uncertainty. Firstly, the uncertainty from wind power generation is modeled by confidence intervals and evaluated by the calculation of the conditional value at risk (CVaR). Then, to economically distribute the power deficit among load agents, a decentralized consensus on the incremental cost among decentralized agents is reached, where the interruption cost of the local agent varies with its load as a nonlinear relationship. Finally, a two-layer adaptive risk-decision decentralized UFLS control framework is proposed to enable the frequency stability and economic operation of the smart distribution network. With the proposed strategy, local information sharing and important global information discovery are achieved in a distributed way. Simulations on an improved IEEE 33-bus system show that the proposed strategy can effectively avoid rigid shedding and implement the optimal allocation of the system power deficit.

An adaptive detection model for IPv6 extension header threats based on deterministic decision automaton

The IPv6 extension header mechanism, a new feature of the IPv6 protocol, enhances flexibility and scalability but introduces numerous security threats like firewall evasion and covert channels. Existing threat detection methods face limitations in detection types, universality, and speed. Hence, an adaptive detection model for IPv6 extension header threats (ADM-DDA6) is proposed. Firstly, standard rule sets are designed for common IPv6 extension headers, successfully detecting 70 types of threats from THC-IPv6 and ExtHdr tools using only 20 rules. Secondly, by parsing IPv6 extension headers, matching rules, establishing transition relationships, and deciding packet threat status based on final states (Normal or Abnormal), complex threats like header disorder and header repetition can be detected. Finally, an adaptive rule matching method is introduced, which dynamically selects rule sets based on IPv6 extension header types, effectively reducing rule matching time. Experimental results show that under different threat magnitudes, ADM-DDA6 is 32% faster than Suricata v6.0.12 and 21.2% faster than Snort v3.1.61.0 in detection speed. Additionally, as the number of threats increases, on commodity hardware, ADM-DDA6 incurs only a 0.7% increase in CPU overhead with no significant memory consumption increase, maintains maximum throughput, and exhibits minor performance changes under low and moderate network load conditions.

An Effective Intrusion Detection System for Edge Computing Using ConvNeXt and ResNet152V2

The proliferation of edge computing, driven by network applications and wireless devices, increases the vulnerability of confidential information to security risks. In this environment, existing intrusion detection algorithms fail to satisfy the requirements of prompt responses, heavy network load management, inadequate extraction of features, and imprecise model classification. In this work, the imbalanced data problem in the input dataset is mitigated using the Data Augmentation Generative Adversarial Network (DAGAN). Next, an efficient ConvNeXt-based feature extraction method is created to retrieve the key characteristics from the dataset for every class. Last, multi-attack intrusion detection is achieved through the deployment of an optimized deep learning classifier based on ResNet152V2. Furthermore, simulation experiments are carried out on the ToN-IoT and BoT-IoT datasets, and the outcomes demonstrate that our suggested model performs better than the existing models, with accuracy levels of 99.20% and 99.31%, respectively. These findings show that this approach is successful in building and refining large-scale IDS in the edge computing framework.

SIMPLIFIED SIMULATION MODEL OF LOAD IN A CLOSED PACKET SWITCHING NETWORK

An approach to simplified simulation modeling of packet switching network load has been developed. The simulated representation of network processes is based on a simplified mathematical model of the packet switching network, which is based on a discretized description of the load states of the network nodes and corresponds to the representation of the current state of the network in the tasks of managing its load. Changes in the node's filling state are modeled as a process of reproduction and death, and in the full load model, two factors of change are taken into account: the node's exchange of load with network users and transit load flow passing through it. To study an artificially selected "part of the network functioning process" - an approach to transit load modeling in a load-locked network is proposed. The approach allows for significant simplifications and ensures the leveling of the negative impact of simplifications on "load closure" (ensures the rule of constant amount of load in a closed network). The developed simulation model, firstly, creates the basis for the improvement of the complete model, and secondly, it is convenient for working out routing algorithms when analyzing them from the point of view of indicators of the ability to "influence" the load in the network. The obtained results are generalized, but attention is focused on the example of satellite packet switching networks.

Resilience-Oriented Planning of Urban Distribution System Source–Network–Load–Storage in the Context of High-Penetrated Building-Integrated Resources

Building-integrated flexible resources can offer economical availability to accommodate high-penetrated renewable energy sources (RESs), which can be potentially coordinated to achieve cost-effective supply. This paper proposes a resilience-oriented planning model of urban distribution system source–network–load–storage in the context of high-penetrated building-integrated resources. In this model, source–network–load–storage resources are cost-optimally planned, including the lines, soft open point (SOP), building-integrated photovoltaics (BIPVs), building-integrated wind turbine (BIWT), building-integrated energy storage system (ESS), etc. To enhance fault recovery capability during extreme faults, fault scenarios are incorporated into the distribution system operation via coupled multiple recovery stages. The resilience-oriented planning is a thorny problem due to its source–network–load–storage couplings, normal-fault couplings, etc. The original resilience-oriented planning is reformulated as a mixed-integer linear programming (MILP) problem, which can then be solved with a two-stage method and evaluated via a multi-dimensional evaluation metrics. The proposed planning methodology is benchmarked over a Portugal 54-node urban distribution system to verify the superiority and effectiveness on the system economy and resilience levels. Case studies show that the proposed methodology can exploit the optimal synergies of different source–network–load–storage components and enhance system dispatchability.

Optimizing information support technology for network control: a probabilistic-time graph approach

In modern telecommunications and computer networks, efficient and reliable information collection is essential for effective decision-making and control task resolution. Current methods, such as periodic data transmission, event-driven data collection, and on-demand requests, have distinct advantages and limitations. The object of the paper: The study focuses on developing a comprehensive model to optimize information collection processes in network environments. Subject of the paper: This paper investigates various information collection methods, including periodic data transmission, event-driven data collection, and on-demand requests, and evaluates their efficiency under different network conditions. This study proposes a flexible and accurate model that can optimize information support technologies for network control tasks. The key tasks include 1. Developing a probabilistic-time graph model to evaluate the efficiency of different information collection methods. 2. Analyzing model performance through mathematical relationships and simulations. 3. Comparing the proposed model with existing methodologies. Results. The proposed model demonstrated significant variations in the efficiency of the information collection methods. Periodic data transmission increased network load, while event-driven data collection was more responsive but could miss infrequent changes. On-demand requests balanced timely data needs with resource constraints but faced delays due to packet loss. The probabilistic time graph effectively captured these dynamics, providing a detailed understanding of the trade-offs. Conclusions. This study developed a flexible and accurate model for optimizing information support technologies during network control tasks. The model's adaptability to varying network conditions has significant practical implications for improving network efficiency and performance. Future research should explore the integration of machine learning techniques and extend the model to more complex network environments.