Network Demand Research Articles

Ethernet Passive Mutual Authentication Scheme on Quantum Networks

In the context of increasing demand for secure and efficient communication networks, addressing the issue of mutual authentication in ethernet passive optical networks (EPONs) has become both valuable and practically significant. This paper proposes a solution based on ideal lattices. The proposed scheme leverages the security of the ring learning with errors (RLWE) problem to establish a robust public-key cryptosystem. By involving ONUs, OLTs, and an SDN controller in the authentication process, it enables mutual authentication through a series of message exchanges facilitated by the SDN controller. Utilizing approximate smooth projection hash functions for secure key exchange and verification, the scheme ensures robust security performance against various attacks, including man-in-the-middle, impersonation, replay, and known key secrecy attacks. Simulation results demonstrate that the proposed solution introduces minimal delay and maintains a high registration success rate compared to traditional authentication methods. Additionally, this paper explores the convergence of quantum network protocols with EPONs, highlighting their potential to achieve unprecedented levels of communication security. Integrating quantum technology with EPON networks, due to the unique security properties of quantum, can also better prevent man-in-the-middle attacks. Secure interception detection techniques based on fundamental quantum properties provide a fundamental security direction for future communication systems, aligning with the growing interest in quantum-resistant cryptographic protocols.

Multi-Objective Optimization in Disaster Backup with Reinforcement Learning

Disaster backup, which occurs over long distances and involves large data volumes, often leads to huge energy consumption and the long-term occupation of network resources. However, existing work in this area lacks adequate optimization of the trade-off between energy consumption and latency. We consider the one-to-many characteristic in disaster backup and propose a novel algorithm based on multicast and reinforcement learning to optimize the data transmission process. We aim to jointly reduce network energy consumption and latency while meeting the requirements of network performance and Quality of Service. We leverage hybrid-step Q-Learning, which can more accurately estimate the long-term reward of each path. We enhance the utilization of shared nodes and links by introducing the node sharing degree in the reward value. We perform path selection through three different levels to improve algorithm efficiency and robustness. To simplify weight selection among multiple objectives, we leverage the Chebyshev scalarization function based on roulette to evaluate the action reward. We implement comprehensive performance evaluation with different network settings and demand sets and provide an implementation prototype to verify algorithm applicability in a real-world network structure. The simulation results show that compared with existing representative algorithms, our algorithm can effectively reduce network energy consumption and latency during the data transmission of disaster backup while obtaining good convergence and robustness.

Demand forecasting in clearance season for fast fashion retail: a residual learning approach

PurposeThis study focuses on forecasting demand for markdown pricing in situations where historical data are limited and proposes using a hybrid knowledge-based residual (KRL) network to enhance the accuracy of demand predictions in fast fashion retailing.Design/methodology/approachWe use a hybrid KRL network structure to increase forecasting accuracy in fast fashion data by combining artificial neural network (ANN) and theoretical demand models (TDMs) such as linear, exponential and multinomial logit. We used a linear demand model (LDM) as a theoretical demand model, which is one of the popular TDMs in the literature, and combined it with neural networks utilizing the residual network structure. We tested it on real fast fashion data to estimate the next week’s demand at the clearance seasons of 5 years.FindingsThe results underscore KRL’s capability to derive mutual benefits from both neural networks and LDM, especially in the specific context of limited fast fashion data. Furthermore, KRL outperforms LDM and ANN models when used individually in forecasting accuracy.Originality/valueThe research paper proposes a scientific, quantitative method for forecasting the sales for markdown settings, combining data driven and TDMs using residual learning thereby resolving the issue of insufficient sales data in the fashion industry.

Scheduled lightpath demand provisioning in fiber space division multiplexing optical networks

Space division multiplexing (SDM) and spatial super-channel (Spa SCh) technology offer scalable solutions to meet the increasing capacity demands in optical networks. This study focuses on multi-fiber optical networks integrated with the innovative Spa SCh technology, which creates different granularities of Spa SChs by spatially combining the same spectrum across multiple fibers, known as fiber-SDM (FSDM). Most existing studies have concentrated on provisioning dynamic or static lightpath demands in FSDM networks. However, for several applications in real-world networks, scheduled lightpath demands—where setup and teardown times are pre-determined—are more common. To address this, we consider the provisioning of scheduled lightpath demands in FSDM networks and aim to solve the routing, fiber, spectrum, and time-slot assignment (RFSTA) problem for these demands. We propose two integer linear programming (ILP) models: one optimizing only the spectrum resource utilization (SRU) and the other jointly optimizing both SRU and capital expenditure (CAPEX). In addition, we develop several efficient heuristic algorithms for large-scale optical networks, based on the concepts of the spatial spectrum window (SSW), SSW plane, and time-slot window (TW). Furthermore, an evolutionary joint optimization strategy is introduced to explore optimal Spa SCh configurations, aiming to jointly optimize network SRU and CAPEX. Simulation results demonstrate that the proposed algorithms closely match the performance of ILP models in small networks. Among the heuristic approaches, the adaptive least-load algorithm performs best under an optimal Spa SCh configuration, which bundles half of the fibers to form link fiber bundles.

Integrating sustainability into cybersecurity: insights from machine learning based topic modeling

The increasing emphasis on sustainability in computing systems, ranging from small devices to large data centers, is fueled by environmental concerns. Moreover, ensuring cybersecurity in these interconnected networks demands the use of technologies such as resource-intensive cryptography and advanced intrusion detection systems. Our unique study investigates the integration of environmental sustainability into cybersecurity practices by identifying six pivotal themes through a textual analysis of related publications via machine learning based topic modeling. These themes highlight the convergence of cybersecurity with sustainable development goals (SDGs), particularly SDGs 7 (Affordable and Clean Energy), 9 (Industry, Innovation, and Infrastructure), and 8 (Decent Work and Economic Growth). These include the integration of sustainable cybersecurity measures in smart cities, sustainable digital protection strategies, the application of blockchain for cybersecurity in smart grids, and cybersecurity solutions for SMEs aimed at minimizing resource consumption. Additionally, the study explores multidisciplinary strategies and innovations across four perspectives: adaptive frameworks that prioritize resilience and environmental consciousness, policy shifts for coordinated protection, the power of AI in intelligent threat detection, and the impact of emerging technologies on both security and environmental efficiency. These strategies advocate for an evolved approach that incorporates advanced technologies such as AI, IoT, and blockchain into resilient, sustainable cybersecurity frameworks. This study not only provides a comprehensive overview of the intersection of cybersecurity and sustainability but also serves as a guide for future research and practical applications in creating robust, environmentally friendly cybersecurity practices.

Enabling Power System Restoration from Offshore Wind Power Plants in the UK

This paper presents the findings from the initial phases of the SIF BLADE project, focused on demonstrating the capabilities of an offshore wind power plant (OWPP) for power system restoration (PSR). It provides an overview of PSR, highlighting its challenges and operational requirements, alongside the various scenarios considered in the project. The study includes a steady-state analysis to assess whether the OWPP can meet local network demands for both active and reactive power. Results indicate that the OWPP can operate within an envelope that covers all local power requirements. Additionally, electromagnetic transient (EMT) analysis was conducted to evaluate different percentages of grid-forming (GFM) converter penetration during the energisation process. These analyses aimed to determine compliance with transmission system operator (TSO) requirements. Findings demonstrate that all GFM penetration levels met the necessary TSO standards. Furthermore, a novel small-signal analysis was performed to identify the optimal percentage of GFM converters for enhancing system stability during block loading. The analysis suggests that for top-up scenarios, a GFM penetration between 20% and 40% is optimal, while for anchor scenarios, 40% to 60% GFM penetration enhances stability and robustness.

Evaluating the Implementation, Challenges, and Solutions of Policies for Cultivating New Professional Farmers: A Policy Network Theory Perspective

Cultivating new professional farmers is key to promoting agricultural and rural modernisation. The governments issue policies for cultivating new professional farmers and have gradually shown a network structure in the implementation process. With the help of policy network theory, this paper analyses the policy implementation network structure of the new professional farmer cultivation policy, including policy communities, professional networks, intergovernmental networks, producer networks and issue networks. Under the influence of the interaction between various networks, my country's new professional farmer cultivation has achieved specific results regarding overall goals, influence and recognition. However, there are also difficulties, such as prominent contradictions between policy communities and intergovernmental networks, weak internal coordination of intergovernmental networks, and marginalisation of professional and issue networks. To this end, it is necessary to regulate and balance the interactive relationship between various network entities to seek a solution: first, clarify the responsibility boundaries of policy communities and intergovernmental networks, improve the quality of interaction, enhance the collaborative ability of intergovernmental networks, and innovate governance models; third, pay attention to the diverse demands of professional networks and issue networks, and increase public discourse power.

Improvement of Internet of Things (IoT) Interference Based on Pre-Coding Techniques over 5G Networks

The advent of 5G technology has revolutionized wireless communication, offering unprecedented data rates, reduced latency, and enhanced connectivity. A critical component driving these advancements is Multiple-Input Multiple-Output (MIMO) technology. MIMO utilizes multiple antennas at both the transmitter and receiver ends to improve communication performance. In the context of the Internet of Things (IoT), MIMO plays a pivotal role in enhancing network efficiency, reliability, and capacity and can improve system capacity and reduce interference between different users. By leveraging MIMO, IoT devices can achieve higher data throughput and better signal quality, even in challenging environments. This is particularly important for IoT applications that require real-time data transmission and low latency, such as smart cities, autonomous vehicles, and industrial automation. Additionally, MIMO technology helps in mitigating interference and improving spectrum utilization, making it an essential enabler for the massive connectivity demands of IoT networks in the 5G era. However, due to the high channel dimension, complex channel estimation and precoding algorithms in the system, the system hardware and software overhead will increase. The precoding algorithms of massive MIMO systems are divided into three types: digital, analog and hybrid. The three types of precoding algorithms are summarized and compared, and the advantages and disadvantages of different precoding algorithms and applicable scenarios are summarized. The channel estimation schemes are divided into training estimation and blind estimation, and the advantages and disadvantages of the two types of schemes are summarized. It is pointed out that the reasonable use of the channel sparsity of massive MIMO can improve the quality of channel estimation and reduce the estimation overhead.

Sparse Code Multiple Access With Time Spreading and Repetitive Transmissions

ABSTRACTFor the next‐generation communication systems, to improve spectral efficiency and increase the data rate, new multiple access techniques have been investigated. Orthogonal multiple access techniques are widely used in traditional communication systems while nonorthogonal multiple access (NOMA), proposed in 5G, has been a promising technology for satisfying the demand for future wireless communication networks. Sparse code multiple access (SCMA) is a code‐domain NOMA method that provides diversity gain with signal constellation coding. However, to increase the performance of SCMA, there are only limited works provided in the literature in terms of codebook design and receiver design. In this paper, a new multiple‐access model is proposed by applying various diversity techniques for downlink SCMA. The performance of the proposed model is evaluated with both computer simulations and theoretical analysis. Results show that the proposed model provides a 1.6 dB gain in terms of the bit error rate (BER) under the Rayleigh fading channel.

An information gap decision theory and improved gradient-based optimizer for robust optimization of renewable energy systems in distribution network

In this paper, a robust fuzzy multi-objective framework is performed to optimize the dispersed and hybrid renewable photovoltaic-wind energy resources in a radial distribution network considering uncertainties of renewable generation and network demand. A novel multi-objective improved gradient-based optimizer (MOIGBO) enhanced with Rosenbrock’s direct rotational technique to overcome premature convergence is proposed to determine the problem optimal decision variables. The deterministic optimization framework without uncertainty minimizes active energy loss, unmet customer energy, and renewable generation costs. The study also examines the impact of dispersed and hybrid renewable resources on solving the problem. In the robust optimization framework considering the deterministic obtained results, the focus is on determining the maximum uncertainty radius (MUR) of renewable resource generation and network demand based on the uncertainty risk. The MURs and system robustness are optimally determined using information gap decision theory (IGDT) and the MOIGBO, considering various uncertainty budgets under worst-case scenarios. The deterministic results indicate that the MOIGBO effectively balances the objectives and identifies the final solution within the Pareto front, according to fuzzy decision-making. The results also reveal that the dispersed case yields better objective values than the hybrid case. Furthermore, the MOIGBO outperforms MOGBO and multi-objective particle swarm optimization (MOPSO) in improving distribution network operations. The robust results show that maximum system robustness is achieved at 30% uncertainty risk due to forecasting errors, with MUR values of 0.54% for resource production and 12.56% for load demand.

A Local–Transit Percolation and Clustering-Based Method for Highway Segment Importance Ranking

The impact of disturbances on a transportation network varies depending on the location and characteristics of the affected highway segments. Given limited resources, it is crucial to prioritize the protection and repair of highway segments based on their importance to maintaining overall network performance during disruptions. This paper proposes a novel method for ranking the importance of highway segments, leveraging a novel local–transit percolation and clustering-based method. Initially, the highway network is constructed by Graph theory, and the k-means clustering method is applied considering each segment’s transit and local traffic flows. Subsequently, a local–transit percolation model is constructed to generate an initial ranking of segments based on the size of the second-largest clusters during the percolation phase transition. A secondary ranking is performed by refining the results from the clustering phase. Results of a control experiment show that, compared to baselines, the proposed ranking approach demonstrates a significantly improved ability to sustain network demand and connectivity when high-ranked segments are moved. The model uncertainty analysis was conducted by adding noise to the gantry records, and the experiments demonstrated that the model exhibits robustness under noisy conditions. These findings highlight the effectiveness and superiority of the proposed method.

Coordinated allocation of distributed generation, soft open points and energy storage systems in unbalanced distribution networks considering flexibility deficiency risk

Optimizing the location and capacity of flexible resources is critical. The increase in the permeability of distributed generation (DG) causes the net load to drastically fluctuate, which sharply increases flexibility demand for the distribution network. The asymmetric access of distributed generators, asymmetric line parameters, and unbalanced loads aggravate the three-phase voltage unbalance of distribution systems. Energy storage systems (ESSs) and soft open points (SOPs) can flexibly adjust power flow direction, which can address the problem. This study analyzed the characteristics of flexibility resources based on the flexibility theory. Furthermore, the potential loss of flexibility demand is quantitatively assessed by conditional value-at-risk, and the coordination planning model of DG, SOP, and ESS for the unbalanced distribution network is constructed considering flexibility and economy. Because the resulting mathematical formulation belongs to a nonconvex quadratic programming problem, an adaptive linear relaxation programming method is proposed to improve computational efficiency. The adaptive linear relaxation technique is constructed based on the properties of quadratic functions and the mean value theorem, and the non-convex quadratic programming problem is converted to a series of parametric linear relaxation programming problems by the technique. Finally, the effectiveness of the proposed model is verified on IEEE 33-bus and IEEE 123-bus systems.

An Improved Freight Transportation Planning System for Less‐Than‐Truckload Operations of a Third‐Party Logistics Carrier

ABSTRACTLess‐than‐truckload (LTL) transportation is a widely used shipping modality within the logistics industry, facilitating the movement of loads insufficient in size to occupy the entirety of a truck's capacity. Drawing on the real‐life problem of a third‐party logistics (3PL) carrier, which navigates an expansive transportation network and unpredictable demand patterns, we propose a centralized planning approach for LTL transportation, taking advantage of consolidation opportunities at cross‐dock hubs to minimize long‐term freight costs while ensuring on‐time delivery. We present an integer linear programming (ILP) formulation to generate a multi‐day plan with known demands and a daily planning problem (DPP) model to be solved daily, emulating the available order information in a real planning scenario. DPP optimizes truck routes and loading plans, incorporating a penalty cost for postponed transportation requests. We devise a novel multi‐stage matheuristic integrating geographical decomposition with tractable integer programming models and column generation‐based heuristics. With this approach, the DPP is solved in fewer than 2 h of run‐time for real problem instances with as high as 1400 transportation orders. We benchmark our results against a baseline heuristic that mirrors the firm's existing planning approach through a multi‐period simulation. Our comparative evaluations reveal a noteworthy 7.8% improvement in long‐term freight costs, concurrently maintaining adherence to the 98% on‐time delivery target.

Optimizing FSO systems for 6G network using particle swarm optimization

Abstract Free Space Optics (FSO) is an innovative optical communication technology that leverages light propagation in free space for direct line-of-sight interactions. This technology is characterized by several benefits, including secure transmission, high bandwidth, and rapid data transfer rates, making it a promising candidate for integration into emerging 6G networks. However, the efficiency of FSO systems is adversely affected by various weather conditions, such as rain, temperature fluctuations, snow, and fog, as well as atmospheric disturbances. This paper presents a Particle Swarm Optimization (PSO)-based approach to optimize the divergence angle of FSO systems. PSO is a type of artificial intelligence that excels at managing complex settings and adapting to changes in the environment, thus making FSO systems more robust against such challenges. By optimizing this critical parameter, the proposed method significantly improves the reliability and efficiency of data transmission in FSO systems, supporting the advanced performance demands of future 6G networks. This optimization ensures FSO systems can achieve faster and more reliable connectivity, essential for the next generation of wireless communication.

Mobility as a Resource (MaaR) for Resilient Human-Centric Automation – A Vision Paper

With technological advances, mobility has been moving from a product (i.e., traditional modes and vehicles), to a service (i.e., Mobility as a Service, MaaS). However, as observed in other fields (e.g., cloud computing resource management) we argue that mobility will evolve from a service to a resource (i.e., “Mobility as a Resource”, MaaR). Further, due to increasing scarcity of shared-mobility spaces across traditional and emerging modes, the transition must be viewed within the critical need for ethical and equitable solutions for the traveling public (i.e., research is needed to avoid hyper-market driven outcomes for society). The evolution of mobility into a resource requires novel conceptual frameworks, technologies, processes and perspectives of analysis. A key component of the future MaaR system is the technological capacity to observe, allocate and manage (in real-time) the smallest envisionable units of mobility (i.e., atomic units of mobility capacity) while providing prioritized attention to human movement and ethical metrics related to access, consumption and impact. To facilitate research into the envisioned future system, this paper proposes initial frameworks which synthesize and advance methodologies relating to highly dynamic capacity reservation systems. Future research requires synthesis across transport network management, demand behavior, mixed-mode usage, and equitable mobility.

The Bitcoin yield gap in Colombia: unraveling the influence of FX and Bitcoin convenience yields

This study examines why Bitcoin consistently traded at a discount in Colombia compared to the US market between April 2020 and July 2023. We introduced the “Bitcoin yield gap,” representing the difference between Bitcoin’s trading price in Colombia and its global price. Using robust ordinary least squares regression, we found a positive relationship between foreign exchange (FX) convenience yields and the yield gap, while Bitcoin convenience yields did not show a significant relationship. Additionally, Bitcoin network demand was significantly associated with the yield gap without affecting other regression parameters. These findings highlight that the yield gap is predominantly driven by dynamics in the domestic FX market, illustrating the interplay between traditional FX markets and the digital cryptocurrency landscape in Colombia.

TinyNIDS: CNN‐Based Network Intrusion Detection System on TinyML Models in 6G Environments

ABSTRACTWith the evolution of network technologies from 5G to 6G, security has become increasingly critical due to the vast number of connected devices and the decentralized nature of network architectures. TinyNIDS addresses these challenges by leveraging a lightweight CNN (TinyCNN) optimized through quantization and pruning, enabling real‐time intrusion detection on resource‐constrained edge devices. This paper presents TinyNIDS, a CNN‐based network intrusion detection system designed for deployment on TinyML models in 6G environments. The system aligns with the demands of 6G networks, including ultra‐low latency, high throughput, and decentralized processing, ensuring robust threat detection without centralized bottlenecks. Extensive experiments demonstrate the superior performance of TinyCNN compared to traditional models, achieving higher accuracy, lower false positive and false negative rates, and reduced latency. The results validate the effectiveness of TinyNIDS as a scalable and efficient solution for real‐time network security in next‐generation networks.

Motif‐based resiliency assessment for cyber‐physical power systems under various hazards

AbstractCyber‐physical power systems (CPPS) are integral to meeting society's demand for secure, sustainable, affordable and resilient critical networks and services. Given the convergence of decarbonising, heating, cooling, and transportation networks onto cyber‐physical power systems (CPPS), this takes on increased significance. This paper introduces an innovative approach to the open challenge of how we evaluate CPPS resilience, presenting the use of network motifs and Monte Carlo simulations. We demonstrate how our methodology enables a comprehensive analysis of CPPS by capturing the interdependence between cyber and physical networks and by accounting for inherent uncertainties in cyber and physical components. Specifically, this method incorporates the dynamic interplay between the physical and cyber networks, presenting a time‐dependent motif‐based resilience metric. This metric evaluates CPPS performance in maintaining critical loads during and after diverse extreme events in cyber and/or physical layers. The resilience status of the system is determined using the prevalence of 4‐node motifs within the system's network, offering valuable redundant paths for critical load supply. The study models a variety of natural events, including earthquakes, windstorms, and tornadoes, along with cyber‐attacks while accounting for their inherent uncertainties using Monte Carlo simulation. The proposed approach is demonstrated through two test CPPS, specifically the IEEE 14‐bus and IEEE 30‐bus test systems, affirming its effectiveness in quantifying CPPS resilience. By comprehensively addressing system dynamics, interdependencies, and uncertainties, the proposed technique advances our understanding of CPPS and supports resilient system design.

Real-time cooperative voltage regulation strategy of distribution network based on approximate dynamic programming

Large-scale distributed renewable energy in the distribution network can result in reliability issues such as exceeding voltage limits and overloading power lines. Additionally, the rapid growth of electric vehicles has caused a surge in power demand in the distribution network. Therefore, how to guarantee the real-time stability of distribution network voltage under uncertain environment using available resources is an urgent problem to be solved. To this end, this paper proposes a cooperative voltage regulation of an on-load voltage regulator and electric vehicles for a distribution network considering multiple uncertainties. Firstly, an optimization model of the electric vehicle clusters considering the charging location and power is established, and the traditional on-load voltage regulator of distribution networks is also considered along with network operational constraints. Subsequently, a real-time cooperative voltage regulation strategy based on approximate dynamic programming is proposed, which employs segmented linear functions to process the value function to reduce the distribution network voltage offset and to ensure optimization accuracy. Numerical simulation results validate the feasibility and the effectiveness of the proposed cooperative voltage regulation technology involving electric vehicles and on-load tap changer in the distribution network.

ST-YOLO: A defect detection method for photovoltaic modules based on infrared thermal imaging and machine vision technology.

Photovoltaic panels are the core components of photovoltaic power generation systems, and their quality directly affects power generation efficiency and circuit safety. To address the shortcomings of existing photovoltaic defect detection technologies, such as high labor costs, large workloads, high sensor failure rates, low reliability, high false alarm rates, high network demands, and slow detection speeds of traditional algorithms, we propose an algorithm named ST-YOLO specifically for photovoltaic module defect detection. This algorithm is based on YOLOv8s. First, it introduces the C2f-SCconv convolution module, which is based on SCconv convolution. This module reduces the computational burden of model parameters and improves detection speed through lightweight design. Additionally, the Triplet Attention mechanism is incorporated, significantly enhancing detection accuracy without substantially increasing model parameter computations. Experiments on a self-built photovoltaic array infrared defect image dataset show that ST-YOLO, compared to the baseline YOLOv8s, achieves a 15% reduction in model weight, a 2.9% improvement in Precision, and a 1.4% increase in mAP@0.5. Compared to YOLOv7-Tiny and YOLOv5s, ST-YOLO also demonstrates superior detection performance and advantages. This indicates that ST-YOLO has significant application value in photovoltaic defect detection.