Network Operations Research Articles

A dynamic framework for optimizing peer-to-peer energy sharing: Enhancing local consumption and reducing power losses in smart grids

The integration of distributed energy resources has enabled peer-to-peer energy sharing (P2PES), offering an innovative approach to energy planning. While previous research has focused on the economic benefits of P2PES for users, it often overlooks strategic network responses. This paper presents a P2PES framework that incorporates dynamic network architecture. The framework's core involves developing a P2PES model aimed at increasing local energy consumption and reducing electrical losses during peer-to-peer (P2P) exchanges. Additionally, a dynamic grid structure model facilitates active grid operator participation in optimizing grid structure and reducing power losses. These dual models synergize, sharing information to optimize P2PES programs, network operations, and energy utilization. The framework's coordination is achieved through an algorithm combining a matching mechanism with a branch exchange method, refined iteratively to enhance performance. Numerical analysis demonstrates the framework's effectiveness, showing an 18.31 % improvement with joint optimization strategies. Additionally, incorporating distribution system operator switching strategies results in a 31.69 % reduction in total network power losses and a 2.81 % increase in local energy consumption. This highlights not only buyer strategies but also dynamic network evolution, emphasizing the framework's practical feasibility in electrical engineering.

Exploring the effect of supply chain integration and supply chain transparency on SME environmental performance under conditions of environmental unpredictability.

This research aims to study the impact of supply chain integration in bolstering SME supply chain transparency and environmental performance. Drawing on stakeholder theory, the study focuses customer and supplier integration as well as internal integration in the operations of the supply network. Manufacturing SMEs provided survey data suggesting that integration of both customers and suppliers improves SME supply chain transparency, while internal integration plays no role in this regard. Findings reveal that environmental unpredictability strengthens links between supply chain integration and transparency. Furthermore, strong results in terms of environmental issues depend significantly on supply chain transparency. Overall, this work provides much needed investigation into how the integration of the supply chain shapes SME supply chain transparency and environmental performance by offering insights for further research.

Memristor-based feature learning for pattern classification

Inspired by biological processes, feature learning techniques, such as deep learning, have achieved great success in various fields. However, since biological organs may operate differently from semiconductor devices, deep models usually require dedicated hardware and are computation-complex. High energy consumption has made deep model growth unsustainable. We present an approach that directly implements feature learning using semiconductor physics to minimize disparity between model and hardware. Following this approach, a feature learning technique based on memristor drift-diffusion kinetics is proposed by leveraging the dynamic response of a single memristor to learn features. The model parameters and computational operations of the kinetics-based network are reduced by up to 2 and 4 orders of magnitude, respectively, compared with deep models. We experimentally implement the proposed network on 180 nm memristor chips for various dimensional pattern classification tasks. Compared with memristor-based deep learning hardware, the memristor kinetics-based hardware can further reduce energy and area consumption significantly. We propose that innovations in hardware physics could create an intriguing solution for intelligent models by balancing model complexity and performance.

GTD‐CER: Game‐Theoretic Dynamic Clustering for Enhanced Efficiency and Reliability in Wireless Sensor Networks

ABSTRACTEfficient cluster formation is a significant issue in wireless sensor networks (WSNs) for optimizing energy consumption, enhancing data reliability, and prolonging network lifetime. This paper proposes a dynamic cluster formation scheme modeled as a noncooperative game, where each sensor node acts as a player making strategic decisions on whether to become a cluster head (CH) or remain a member node (MN). In this role selection decision‐making phase, nodes evaluate their potential utility function, which incorporates energy consumption, data reliability, and data forwarding efficiency. Nodes dynamically switch roles based on their utility to achieve an optimal network configuration. Simulations conducted using the NS‐3 network simulator demonstrate that the proposed game theory‐based clustering scheme significantly improves network performance. We analyze key measures like energy consumption, packet delivery ratio (PDR), and data forwarding, and the results shows that the proposed scheme reduces energy consumption by 25%, improves PDR by 15%, and extends the network lifetime by 20%, compared to traditional methods. The dynamic role‐switching mechanism prolongs network lifetime by preventing the rapid depletion of node energy, ensuring stable and efficient network operations. It significantly enhances network performance, stability, and longevity.

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.

Synchronization of two coupled massive oscillators in the time-delayed Kuramoto model.

We examine the impact of the time delay on two coupled massive oscillators within the second-order Kuramoto model, which is relevant to the operations of real-world networks that rely on signal transmission speed constraints. Our analytical and numerical exploration shows that time delay can cause multi-stability within phase-locked solutions, and the stability of these solutions decreases as the inertia increases. In addition to phase-locked solutions, we discovered non-phase-locked solutions that exhibit periodic and chaotic behaviors, depending on the amount of inertia and time delay.

Invited paper: Network digital twins for optical networks

Network Digital Twins (NDTs) are recognized as important components of network operations automation systems. As analytical utilities within such systems, NDTs provide data-driven information that is useful in many operations optimization applications. The nature and role of NDTs, as well as their generic use cases, are discussed in detail, reflecting contemporary industry work and understanding. The specific nature of NDTs for optical transmission networks – optical NDTs – as well as their specific use cases of interest, are reviewed. Optical NDT function, architecture and constituent models are discussed, largely through the lens of a particular – now product – implementation, with reference to alternatives and other industry sources. Performance, function and utility are validated and demonstrated, including operational use of available applications directly supported by an optical NDT. Continuing areas of research interest and industry work are commented.

Infrastructure-as-Code for 5G RAN, Core and SBI Deployment: A comprehensive review

The advent of 5G technology marks a significant transformation in the telecommunications landscape, promising to deliver ultra-fast data speeds, minimal latency, and widespread connectivity that supports many new services and applications, including IoT, augmented reality, and autonomous systems. However, achieving the full potential of 5G networks involves overcoming substantial infrastructure challenges. Deploying and managing 5G's intricate architecture, encompassing the Radio Access Network (RAN), Core network, and Service-Based Interfaces (SBI)—requires a shift from traditional, labor-intensive methods to more advanced, automated processes. Infrastructure-as-Code (IaC) has emerged as a game-changing methodology that addresses these needs by codifying infrastructure setup and management into programmable scripts. This comprehensive review delves into how IaC facilitates the efficient deployment and management of 5G components, enhancing agility, consistency, and scalability. By automating resource provisioning, configuration, and updates, IaC empowers network operators to rapidly deploy infrastructure that meets the demanding requirements of 5G services. The paper discusses how IaC enables seamless integration and scaling of network functions, improves operational efficiency through reduced human error, and supports consistent infrastructure management across distributed environments. Key tools such as Terraform, Ansible, and Kubernetes are explored, showcasing their applications in orchestrating various layers of the 5G network, from RAN deployment to core network orchestration and SBI configurations. Despite its advantages, adopting IaC in 5G deployment comes with its challenges. These include managing the complexities inherent in multi-vendor environments, ensuring robust security for automated provisioning scripts, and maintaining compliance with regional regulatory standards. The paper explores these obstacles in-depth, offering insights into best practices and potential solutions to navigate these hurdles. This review highlights emerging trends and research opportunities, such as the potential for zero-touch automation, which leverages IaC for autonomous network operations with minimal human intervention. It also discusses the role of artificial intelligence (AI) and machine learning (ML) in predictive resource management and automated fault detection, proposing a future where AI-enhanced IaC can revolutionize the deployment and operation of 5G networks. By examining these elements, the review aims to provide a holistic view of the current state and future trajectory of IaC in 5G RAN, Core, and SBI deployment, setting the stage for ongoing innovation and development in the field.

Performance Evaluation of Fault Tolerance in 6G Software Defined Network (SDN)

Traditional Operations and Maintenance (O&M) have made real-time fault location of devices challenging due to the rapid growth of network devices. Therefore, Intelligent O&M in 6G networks leveraged using Software Defined Network (SDN) are expected to optimise network operations through fault tolerance mechanisms. SDN is an emerging paradigm that offers dynamic and efficient solutions to the complex network environment. This paper provides a comprehensive overview of fault tolerance as a critical algorithm for SDN under Intelligent O&M, evaluating the performance of fault tolerance mechanisms and investigating the effectiveness of fault tolerance mechanisms towards TCP, UDP and ICMPv4 with two different scenarios. Simulation results demonstrate that the proposed fault tolerance mechanisms under Intelligent O&M within a 6G network can reduce the delay by 98.79%, ensuring more reliable network infrastructure.

Sparse Convolution FPGA Accelerator Based on Multi-Bank Hash Selection.

Reconfigurable processor-based acceleration of deep convolutional neural network (DCNN) algorithms has emerged as a widely adopted technique, with particular attention on sparse neural network acceleration as an active research area. However, many computing devices that claim high computational power still struggle to execute neural network algorithms with optimal efficiency, low latency, and minimal power consumption. Consequently, there remains significant potential for further exploration into improving the efficiency, latency, and power consumption of neural network accelerators across diverse computational scenarios. This paper investigates three key techniques for hardware acceleration of sparse neural networks. The main contributions are as follows: (1) Most neural network inference tasks are typically executed on general-purpose computing devices, which often fail to deliver high energy efficiency and are not well-suited for accelerating sparse convolutional models. In this work, we propose a specialized computational circuit for the convolutional operations of sparse neural networks. This circuit is designed to detect and eliminate the computational effort associated with zero values in the sparse convolutional kernels, thereby enhancing energy efficiency. (2) The data access patterns in convolutional neural networks introduce significant pressure on the high-latency off-chip memory access process. Due to issues such as data discontinuity, the data reading unit often fails to fully exploit the available bandwidth during off-chip read and write operations. In this paper, we analyze bandwidth utilization in the context of convolutional accelerator data handling and propose a strategy to improve off-chip access efficiency. Specifically, we leverage a compiler optimization plugin developed for Vitis HLS, which automatically identifies and optimizes on-chip bandwidth utilization. (3) In coefficient-based accelerators, the synchronous operation of individual computational units can significantly hinder efficiency. Previous approaches have achieved asynchronous convolution by designing separate memory units for each computational unit; however, this method consumes a substantial amount of on-chip memory resources. To address this issue, we propose a shared feature map cache design for asynchronous convolution in the accelerators presented in this paper. This design resolves address access conflicts when multiple computational units concurrently access a set of caches by utilizing a hash-based address indexing algorithm. Moreover, the shared cache architecture reduces data redundancy and conserves on-chip resources. Using the optimized accelerator, we successfully executed ResNet50 inference on an Intel Arria 10 1150GX FPGA, achieving a throughput of 497 GOPS, or an equivalent computational power of 1579 GOPS, with a power consumption of only 22 watts.

ADVANCED APPLICATIONS OF PHYSICS-INFORMED NEURAL NETWORKS (PINNS) IN R FOR SOLVING DIFFERENTIAL EQUATIONS

Deep learning, a powerful machine learning technique leveraging artificial neural networks, excels in identifying complex patterns and relationships within data. Among its innovations is the emergence of Physics-Informed Neural Networks (PINNs), which have revolutionized the field of applied mathematics by enabling the solution and discovery of differential equations through neural networks. PINNs address two key challenges: data-driven solutions, where the model approximates the hidden solutions of differential equations with fixed parameters, and data-driven discovery, where the network learns parameters that best describe observed data. This study explores the implementation of PINNs within the R programming environment to solve two differential equations: one with boundary conditions y^'-y=0 with y(0)=0 and y(e)=1 boundaries and the Burgers’ Equation. The research utilizes R libraries, including reticulate for Python integration and torch for neural network operations, to demonstrate the versatility and efficacy of PINNs in addressing both data-centric solutions and parameter discovery. The results showcase the ability of PINNs to handle complex, high-dimensional problems, offering a promising alternative to traditional numerical methods for solving differential equations.

Автоматическая настройка операции пулинга в сверточных нейронных сетях для классификации угольных пород

Convolutional neural networks are used to automate image processing tasks such as classification, segmentation, object detection, style transfer, etc. These networks are actively applied in coal mining industry for automatic classification of coal rocks with high accuracy based on raw images. Accurate classification of the coal rocks is important for coal quality assessment, optimization of coal mining, preparation and processing. The main mathematical operations of convolutional networks are convolution and pooling. The paper discusses a generalization of the pooling operation. Usually the type of pooling is specified in advance by some aggregation operation, i.e. the average pooling or max pooling. The size of the aggregated area is also specified in advance. The type of pooling and the size of the aggregated region significantly affect the quality of coal-bed image processing. This paper proposes several parametric generalizations of the pooling operation, which cover the average and max pooling as special cases. A parametric generalization is also proposed for max pooling, which allows to vary the size of the aggregation region. Parameters of the proposed pooling generalizations are automatically trained along with the rest of the network weights.

Robust Optimization for Supply Chain with Routing Problem: A Learning-based Approach

Effective supply chain management is crucial for businesses to remain competitive in today's dynamic market. Despite extensive research, there is a lack of integrated approaches that simultaneously address resource allocation, routing, and delivery scheduling under uncertain conditions. This study develops a hybrid framework that combines robust optimization, simulated annealing, and reinforcement learning to enhance supply chain operations in complex networks involving fixed suppliers, distribution centers, and customers. Empirical results from rigorous testing demonstrate significant efficiency improvements and adaptability across diverse scenarios. A real-world case study from the logistics sector highlights the practical benefits, achieving notable cost savings and operational robustness. Sensitivity analysis further underscores the model’s capability to adapt to parameter variations. These findings emphasize the value of incorporating learning-based strategies into supply chain optimization, offering a powerful tool for organizations to address uncertainty and enhance decision-making efficiency.

A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks.

This paper proposes a green computing strategy for low Earth orbit (LEO) satellite networks (LSNs), addressing energy efficiency and delay optimization in dynamic and energy-constrained environments. By integrating a Markov Decision Process (MDP) with a Double Deep Q-Network (Double DQN) and introducing the Energy-Delay Ratio (EDR) metric, this study effectively quantifies and balances energy savings with delay costs. Simulations demonstrate significant energy savings, with reductions of up to 47.87% under low business volumes, accompanied by a minimal delay increase of only 0.0161 s. For medium business volumes, energy savings reach 26.75%, with a delay increase of 0.0189 s, while high business volumes achieve a 4.36% energy reduction and a delay increase of 0.0299 s. These results highlight the proposed strategy's ability to effectively balance energy efficiency and delay, showcasing its adaptability and suitability for sustainable operations in LEO satellite networks under varying traffic loads.

MHTAPred-SS: A Highly Targeted Autoencoder-Driven Deep Multi-Task Learning Framework for Accurate Protein Secondary Structure Prediction.

Accurate protein secondary structure prediction (PSSP) plays a crucial role in biopharmaceutics and disease diagnosis. Current prediction methods are mainly based on multiple sequence alignment (MSA) encoding and collaborative operations of diverse networks. However, existing encoding approaches lead to poor feature space utilization, and encoding quality decreases with fewer homologous proteins. Moreover, the performance of simple stacked networks is greatly limited by feature extraction capabilities and learning strategies. To this end, we propose MHTAPred-SS, a novel PSSP framework based on the fusion of six features, including the embedding feature derived from a pre-trained protein language model. First, we propose a highly targeted autoencoder (HTA) as the driver to encode sequences in a homologous protein-independent manner. Second, under the guidance of biological knowledge, we design a protein secondary structure prediction model based on the multi-task learning strategy (PSSP-MTL). Experimental results on six independent test sets show that MHTAPred-SS achieves state-of-the-art performance, with values of 88.14%, 84.89%, 78.74% and 77.15% for Q3, SOV3, Q8 and SOV8 metrics on the TEST2016 dataset, respectively. Additionally, we demonstrate that MHTAPred-SS has significant advantages in single-category and boundary secondary structure prediction, and can finely capture the distribution of secondary structure segments, thereby contributing to subsequent tasks.

TENSAI - Practical and Responsible Observability for Data Quality-aware Large-scale Analytics

Given a large-scale mobile network with a variety of equipment and radio access network technologies for an approximate 20 million subscribers, there are many types of data that can be used for big data analytics and machine learning (ML) tasks for network operations, monitoring, and optimization. However, a variety of data is measured, collected, and propagated through numerous complex data and software systems. Thus, people, software components, and data-driven operations for big data and ML pipelines face great challenges in dealing with data quality impacts. Data quality related problems occur and are propagated through complex operations involving different types of data, people, software components, and analytics that cannot be solved purely through data quality engineering. This paper discusses our TENSAI framework, as a practical and responsible observability for ensuring data quality in such a mobile network. TENSAI focuses on methods of communication, strategy specifications, and data quality engineering for diverse types of data and analytics among different types of operations. TENSAI presents techniques for capturing and communicating causes/effects of data quality problems clearly to all relevant stakeholders, developing data quality-aware adaptation strategies for actions on data that can be integrated into analytics processes, and engineering data quality awareness in software and data pipelines. Thus, TENSAI supports full visibility of data quality problems and impacts among related systems to empower the utilization and adaptation of data analytics for different types of operations. We will illustrate our TENSAI with several real-world data types, pipelines, and cases based on a real-world mobile network.

Sustainability using Artificial Intelligence: Transformative Solutions in the IT Sector

Sustainability has become a critical priority for businesses and governments as we address environmental changes such as climate change, resource depletion, waste management, and water level increases. This paper investigates the transformative role of artificial intelligence (AI) in sustainable product development, highlighting how advancements in AI technologies are reshaping traditional practices. Traditional product development processes often relied on resource-intensive methods that generated significant waste and offered limited adaptability to sustainability goals. As a cornerstone of the digital economy, the IT sector faces increasing pressure to address its environmental impact, particularly in areas like server management, network optimization, and data transmission. This paper explores the transformative role of Artificial Intelligence (AI) in driving sustainable practices within IT operations. AI-powered solutions, such as dynamic workload management, predictive maintenance, and traffic optimization, enable energy-efficient server and network operations, significantly reducing power consumption. Advanced AI-driven algorithms facilitate data compression, prioritize transmission, and optimize content delivery, minimizing carbon emissions from data-intensive activities. Additionally, AI supports renewable energy integration, tracks carbon footprints, and provides actionable insights for developing sustainable software and IT infrastructure. These innovative applications of AI enhance operational efficiency and contribute to global sustainability goals by reducing the environmental footprint of IT systems. This research highlights the potential of AI to create a greener IT landscape while maintaining the sector’s growth and performance imperatives.

AI in Network Infrastructure: Transforming Telecommunications with Intelligent Systems

AI technologies are drastically changing the backbone of telecommunications networks and bringing unheard-of security, network management, and optimization capabilities. From dynamic resource allocation to predictive maintenance and intelligent traffic management, this article shows how artificial intelligence and machine learning technologies are being used to solve important problems in modern telecommunications. By means of a thorough investigation of AI-driven solutions, this article shows that technologies are offering real-time network optimization, automatic threat identification, and improved service dependability while drastically lowering operating costs. While addressing important technical issues and operational considerations, the conversation covers pragmatic solutions across several network domains, including 5G infrastructure, edge computing, and autonomous networking systems. Examining real-world case studies and developing trends helps this article show how artificial intelligence is not only transforming present network operations but also opening the path for future telecommunications innovations, enabling more resilient, efficient, and intelligent network infrastructure that can adapt to changing technological demand and user expectations.

Fault Tolerance Management Implementation from Medium-to-Large-Scale Networks

As network infrastructures grow in complexity, ensuring high availability and resilience becomes critical, especially for medium-to-large scale networks. This study focuses on the development and implementation of fault tolerance management within Software-defined networking (SDN) environments, aimed at minimizing downtime and enhancing network reliability. SDN’s centralized control and dynamic programmability provide an ideal framework for implementing efficient fault detection and recovery mechanisms. The proposed model leverages real-time monitoring, redundancy protocols, and adaptive rerouting strategies to mitigate the impact of node or link failures. Key components of the model include failover mechanisms, load balancing, and traffic rerouting algorithms, designed to maintain seamless network operations during failures. Through simulation and testing, the model demonstrates significant improvements in network resilience, reducing recovery time and ensuring uninterrupted service delivery. This research provides a comprehensive guide to implementing fault-tolerant networks using SDN, offering scalable solutions that can be adapted to various network sizes and configurations. The findings emphasize the potential of SDN to revolutionize fault management in modern network infrastructures, making it a crucial consideration for future network design and operations.

An Energy-Efficient ECG Processor with Ultra-Low-Parameter Multi-Stage Neural Network and Optimized Power-of-Two Quantization.

This work presents an energy-efficient ECG processor designed for Cardiac Arrhythmia Classification. The processor integrates a pre-processing and neural network accelerator, achieved through algorithm-hardware co-design to optimize hardware resources. We propose a lightweight two-stage neural network architecture, where the first stage includes discrete wavelet transformation and an ultra-low-parameter multilayer perceptron (MLP) network, and the second stage utilizes group convolution and channel shuffle. Both stages leverage neural networks for hardware resource reuse and feature a reconfigurable processing element array and memory blocks adapted to the proposed two-stage structure to efficiently handle various convolution and MLP layers operations in the two-stage network. Additionally, an optimized power-of-two (OPOT) quantization technique is proposed to enhance accuracy in low-bit quantization, and a multiplier-less processing element structure tailored for the OPOT weight quantization is introduced. The ECG processor was implemented on a 65nm CMOS process technology with 4KB of SRAM memory, achieving an energy consumption per interference of 0.15 uJ with a power supply of 1V, 64% energy saving compared to the recent state-of-the-art work. Under 4-bit weight precision, the 5-class ECG signal classification accuracy reached 98.59% on the MIT-BIH arrhythmia dataset.