Network Demand Research Articles

A Multi-Objective Direction of Arrival Estimation Technique MinimizingEnergy Consumption in Wireless Sensor Network

Wireless Sensor Network (WSN) demand for secure communication is challenging due to its limited resource constraints. Hence, this research developed an effective distributed DOA estimation model through Direction Optimization Integrated Ranking Voting (DOIRV) to improve the performance of WSNs. The developed model comprises the multi-objective optimization model with the Whale technique for the WSN. The constructed DOIRV model uses objective function estimation with branch-and-bound for effective data transmission. The developed DOIRV model uses the Artificial Intelligence-based machine learning model for the DOA routing path computation and estimation. Finally, the DOIRV estimation is evaluated for the performance analysis with the consideration of the Cramer-Rao analysis for the data transmission in the WSN model. Simulation results stated that the proposed weighted technique significantly improves the network performance such as packet delivery ratio, throughput, and network delay. Analysis of the technique expressed that the proposed DOIRV technique exhibits effective performance rather than the conventional technique in terms of network delay, throughput, and packet delivery ratio. The comparative analysis stated that the performance of the proposed DOIRV model is ~13% higher than the conventional technique in terms of packet delivery ratio and ~18% higher for the throughput.

Towards sustainable industry 4.0: A survey on greening IoE in 6G networks

The dramatic recent increase of the smart Internet of Everything (IoE) in Industry 4.0 has significantly increased energy consumption, carbon emissions, and global warming. IoE applications in Industry 4.0 face many challenges, including energy efficiency, heterogeneity, security, interoperability, and centralization. Therefore, Industry 4.0 in Beyond the Sixth-Generation (6G) networks demands moving to sustainable, green IoE and identifying efficient and emerging technologies to overcome sustainability challenges. Many advanced technologies and strategies efficiently solve issues by enhancing connectivity, interoperability, security, decentralization, and reliability. Greening IoE is a promising approach that focuses on improving energy efficiency, providing a high Quality of Service (QoS), and reducing carbon emissions to enhance the quality of life at a low cost. This survey provides a comprehensive overview of how advanced technologies can contribute to green IoE in the 6G network of Industry 4.0 applications. This survey provides a comprehensive overview of advanced technologies, including Blockchain, Digital Twins (DTs), Unmanned Aerial Vehicles (UAVs, a.k.a. drones), and Machine Learning (ML), to improve connectivity, QoS, and energy efficiency for green IoE in 6G networks. We evaluate the capability of each technology in greening IoE in Industry 4.0 applications and analyse the challenges and opportunities to make IoE greener using the discussed technologies.

Design and simulation of an enhanced optical demultiplexer using photonic crystal resonators

The purpose of this paper is to examine the design, simulation, and optimization of a demultiplexer optical system using photonic crystal resonators. Utilizing the distinct properties of photonic crystals, the proposed device achieves high-quality resonances, efficient wavelength separation, and minimal crosstalk. These characteristics are crucial for enhancing the performance of dense wavelength division multiplexing (DWDM) systems, which require precise and efficient channel separation to manage increasing data traffic. Extensive simulations conducted with the RSoft CAD tool explore the impact of varying resonator parameters such as refractive index, geometric dimensions, and material composition on the device's performance. The results indicate substantial improvements in transmission efficiency and quality factor, with average values of 98.675% and 3131.125, respectively. Furthermore, the ability to fine-tune these parameters allows for the customization of the demultiplexer to specific wavelength ranges, making it highly adaptable for various telecommunications applications. This research aims to propel the development of compact, high-performance photonic devices that meet the growing demands of modern communication networks, ultimately contributing to more efficient and reliable optical communication systems.

New model to evaluate the impact of quartz sand crushing on fracture conductivity in a proppant monolayer

The formation of a complex fracture network with sufficient proppant conductivity is crucial for the efficient development of deep shale gas. Due to cost considerations, quartz sand has commonly replaced ceramic proppant in the field. However, the significant crushing of quartz sand and its distribution as multiple particle sizes both before and after crushing, termed as particle size redistribution, raises concerns about its ability to meet the demands of the fracture network. Based on contact mechanics, we developed an evolutionary equation for fracture width that considers particle size redistribution. The particle crushing is described using a fragmentation replacement method, and changes in specific surface area before and after crushing are used to correct the permeability model of the fracture. By integrating models of fracture width and permeability evolution, we have established an analytical model for proppant conductivity that accounts for quartz sand crushing and its particle size redistribution. Numerical simulations further validate the predictive accuracy of our model. Sensitivity analyses investigate the impact of quartz sand degree of crushing, compressive strength, particle size, particle combination ratio, and closure pressure on fracture conductivity, demonstrating the reliability of quartz sand as a substitute for ceramic proppant in deep shale applications.

Predictive Modelling of Network Capacity Demands: A Machine Learning Approach for Global Enterprise Backbone Networks

In the dynamic landscape of global enterprise networks, accurate capacity forecasting is paramount for ensuring optimal resource allocation and preventing service disruptions. This paper presents a hybrid machine learning methodology that combines Autoregressive Integrated Moving Average (ARIMA) models with additional techniques to enhance the accuracy and reliability of network capacity forecasts. By leveraging historical traffic data and incorporating external factors, we develop a predictive model that outperforms traditional methods and adapts to the evolving demands of modern networks. The effectiveness of our approach is validated through rigorous testing against established benchmarks, demonstrating significant improvements in forecasting accuracy.

Optimizing Drone-Based IoT Base Stations in 6G Networks Using the Quasi-opposition-Based Lemurs Optimization Algorithm

The rapid evolution and integration of next-generation Internet-of-things (NG-IoT) applications present new complexities for sixth-generation (6G) mobile communication networks, including the need for extensive connectivity, increased network capacity, and ultralow-latency communications. While ultradense networking, characterized by deploying numerous base stations, offers a potential solution, practical and financial constraints limit its feasibility. Drone-based stations (DBSs) emerge as a flexible alternative, but their optimal positioning remains a critical challenge due to finite energy sources and potential signal degradation. This study introduces a new quasi-opposition-based lemurs optimizer (QOBLO) to address the optimal placement of DBSs in NG-IoT networks. QOBLO combines lemur foraging behaviour with quasi-opposition-based learning to enhance exploration and exploitation in the optimization process. The performance of QOBLO was evaluated across three scenarios and compared with other swarm intelligence algorithms using metrics such as Friedman’s ranking test (FRT) and the Wilcoxon signed-rank test (WSRT). Rigorous simulations emulated real-world conditions and varying network demands, testing QOBLO's adaptability and robustness. Results indicate that QOBLO significantly outperforms other algorithms, achieving a top FRT value of 1.234 and demonstrating superior p values in the WSRT. These findings highlight QOBLO's capability to enhance connectivity, coverage, and energy efficiency in 6G environments. The primary contribution of this research is the development of QOBLO, a scalable and efficient method for optimizing DBS deployment. This new approach improves network performance in complex NG-IoT applications, offering a robust solution to the challenges of 6G networks and ensuring enhanced reliability and sustainability of future communication infrastructures.

An object-oriented implementation of a recursive “quantum network” solver and its application to district heating networks

With the increasing importance of energy efficiency and sustainability, the demand for high-performance district heating networks is also on the rise. As traditional engineering methods were often no longer sufficient, several packages for numerical simulation have evolved. Many of them offer high accuracy at the cost of considerable manual preparation and computational effort. However, there is still no user-friendly software that is equally suitable for simplified studies in the early project phase, for the rapid optimisation of multiple concepts and for subsequent detailed planning and dimensioning. For this reason and in cooperation with some companies from the energy sector, we had conceived a novel, highly flexible modular approach, the so-called “quantum networks”, where all parts of the district heating network are appropriately abstracted into quantum elements. Now we present our recent implementation of this model in an object-oriented C++ library. Starting from a generalised base class and making use of the concepts of inheritance and polymorphism, the idea of different levels of detail for the same element type is directly realised and can always be further refined. In addition, as all elements are derived from the same base class, they all share the same outer appearance and can thus be easily combined or interchanged. Based on these prerequisites, a dedicated thermo-hydraulic solver has then been developed. Thanks to its recursive design requiring neither outer iterations nor matrix inversions, it proves to be extremely fast and is therefore suitable for rapid design or optimisation studies in which a vast number of configurations has to be computed. To conclude this part of the work, the developed C++ library was benchmarked on two use cases covering a full year of operation that can now be computed in approximately one second of runtime.

Investigating the Impacts of Autonomous Vehicles on the Efficiency of Road Network and Traffic Demand: A Case Study of Qingdao, China.

Rapid urbanization has led to the development of intelligent transport in China. As active safety technology evolves, the integration of autonomous active safety systems is receiving increasing attention to enable the transition from functional to all-weather intelligent driving. In this process of transformation, the goal of automobile development becomes clear: autonomous vehicles. According to the Report on Development Forecast and Strategic Investment Planning Analysis of China's autonomous vehicle industry, at present, the development scale of China's intelligent autonomous vehicles has exceeded market expectations. Considering limited research on utilizing autonomous vehicles to meet the needs of urban transportation (transporting passengers), this study investigates how autonomous vehicles affect traffic demand in specific areas, using traffic modeling. It examines how different penetration rates of autonomous vehicles in various scenarios impact the efficiency of road networks with constant traffic demand. In addition, this study also predicts future changes in commuter traffic demand in selected regions using a constructed NL model. The results aim to simulate the delivery of autonomous vehicles to meet the transportation needs of the region.

Trust Evaluation Techniques for 6G Networks: A Comprehensive Survey with Fuzzy Algorithm Approach

Sixth-generation (6G) networks are poised to support an array of advanced technologies and promising high-quality and secure services. However, ensuring robust security, privacy protection, operational efficiency, and superior service delivery poses significant challenges. In this context, trust emerges as a foundational element that is critical for addressing the multifaceted challenges inherent in 6G networks. This review article comprehensively examines trust concepts, methodologies, and techniques that are vital for establishing and maintaining a secure and reliable 6G ecosystem. Beginning with an overview of the trust problem in 6G networks, this study underscores their pivotal role in navigating the network’s complexities. It proceeds to explore the conceptual frameworks underpinning trust and discuss various trust models tailored to the unique demands of 6G networks. Moreover, this article surveys a range of scholarly works presenting diverse techniques for evaluating trust by using the fuzzy logic algorithm, which is essential for ensuring the integrity and resilience of 6G networks. Through a meticulous analysis of these techniques, this study elucidates their technical nuances, advantages, and limitations. By offering a comprehensive assessment of trust evaluation methodologies, this review facilitates informed decision making in the design and implementation of secure and trustworthy 6G networks.

Optimal Transport with Constraints: From Mirror Descent to Classical Mechanics.

Finding optimal trajectories for multiple traffic demands in a congested network is a challenging task. Optimal transport theory is a principled approach that has been used successfully to study various transportation problems. Its usage is limited by the lack of principled and flexible ways to incorporate realistic constraints. We propose a principled physics-based approach to impose constraints flexibly in optimal transport problems. Constraints are included in mirror descent dynamics using the D'Alembert-Lagrange principle from classical mechanics. This results in a sparse, local and linear approximation of the feasible set leading in many cases to closed-form updates.

Perfect vortex Laguerre-Gauss beams as a carrier in the MMF/FSO communication system

This paper presents a novel, to the best of our knowledge, high-speed transmission system that integrates a new structured light beam, specifically the perfect vortex Laguerre-Gaussian (PVLG) beam, with an optical code division multiple access (OCDMA) system utilizing a premutation vector (PV) code. The PVLG beams are distinguished by their unique shape, which remains nearly invariant during propagation regardless of the azimuthal order of the orbital angular momentum (OAM), facilitating the multiplexing of multiple OAM beams within the same spatial area. Additionally, the system employs hybrid multimode fiber (MMF) and free space optics (FSO) channels, with consideration of foggy weather conditions in the FSO channel. A comparative analysis between the performance of PVLG beams and standard LG beams is conducted. Performance evaluation metrics include the Q-factor, bit error rate (BER), and eye diagrams, providing comprehensive insights into received signal quality. The results demonstrate that the system utilizing PVLG beams outperforms the one using standard LG beams. Specifically, the system achieves a maximum MMF length of 0.35 km with a BER of approximately 10−4 and a Q-factor of around three when the MMF cable channel is used only. For the FSO channel, the achievable ranges are 1.1 km, 0.7 km, and 0.35 km under low fog (LF), medium fog (MF), and high fog (HF) conditions, respectively, maintaining the same BER and Q-factor values. Moreover, the hybrid MMF/FSO channel extends the transmission range to 1.2 km under LF conditions and to 0.45 km under HF conditions, with consistent BER and Q-factor values. Each of the four PVLG beams carries 40 Gbps, resulting in a total transmission capacity of 160 Gbps. Thus, the proposed system is well positioned to meet the high-speed data transmission demands of next-generation 6G networks.

A Dual-Branch Fusion of a Graph Convolutional Network and a Convolutional Neural Network for Hyperspectral Image Classification.

Semi-supervised graph convolutional networks (SSGCNs) have been proven to be effective in hyperspectral image classification (HSIC). However, limited training data and spectral uncertainty restrict the classification performance, and the computational demands of a graph convolution network (GCN) present challenges for real-time applications. To overcome these issues, a dual-branch fusion of a GCN and convolutional neural network (DFGCN) is proposed for HSIC tasks. The GCN branch uses an adaptive multi-scale superpixel segmentation method to build fusion adjacency matrices at various scales, which improves the graph convolution efficiency and node representations. Additionally, a spectral feature enhancement module (SFEM) enhances the transmission of crucial channel information between the two graph convolutions. Meanwhile, the CNN branch uses a convolutional network with an attention mechanism to focus on detailed features of local areas. By combining the multi-scale superpixel features from the GCN branch and the local pixel features from the CNN branch, this method leverages complementary features to fully learn rich spatial-spectral information. Our experimental results demonstrate that the proposed method outperforms existing advanced approaches in terms of classification efficiency and accuracy across three benchmark data sets.

Short-Term Energy Forecasting to Improve the Estimation of Demand Response Baselines in Residential Neighborhoods: Deep Learning vs. Machine Learning

Promoting flexible energy demand through response programs in residential neighborhoods would play a vital role in addressing the issues associated with increasing the share of distributed solar systems and balancing supply and demand in energy networks. However, accurately identifying baseline-related energy measurements when activating energy demand response events remains challenging. In response, this study presents a deep learning-based, data-driven framework to improve short-term estimates of demand response baselines during the activation of response events. This framework includes bidirectional long-term memory (BiLSTM), long-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN). Their performance is evaluated by considering different aggregation levels of the demand response baseline profile for 337 dwellings in the city of La Rochelle, France, over different time horizons, not exceeding 24 h. It is also compared with fifteen traditional statistical and machine learning methods in terms of forecasting accuracy. The results demonstrated that deep learning-based models, compared to others, significantly succeeded in minimizing the gap between the actual and forecasted values of demand response baselines at all different aggregation levels of dwelling units over the considered time-horizons. BiLSTM models, followed by GRU and LSTM, consistently demonstrated the lowest mean absolute percentage error (MAPE) in most comparison experiments, with values up to 9.08%, 8.71%, and 9.42%, respectively. Compared to traditional statistical and machine learning models, extreme gradient boosting (XGBoost) was among the best, with a value up to 11.56% of MAPE, but could not achieve the same level of forecasting accuracy in all comparison experiments. Such high performance reveals the potential of the proposed deep learning approach and highlights its importance for improving short-term estimates of future baselines when implementing demand response programs in residential neighborhood contexts.

Supporting Immersive Video Streaming via V2X Communication

With the rapid advancement of autonomous driving and network technologies, future vehicles will function as network nodes, facilitating information transmission. Concurrently, in-vehicle entertainment systems will undergo substantial enhancements. Beyond traditional broadcasting and video playback, future systems will integrate immersive applications featuring 360-degree views and six degrees of freedom (6DoF) capabilities. As autonomous driving technology matures, vehicle passengers will be able to engage in a broader range of entertainment activities while on the move. However, this evolution in video applications will significantly increase bandwidth demand for vehicular networks, potentially leading to bandwidth shortages in congested traffic areas. This paper presents a method for bandwidth allocation for vehicle video applications within the landscape of vehicle-to-everything (V2X) networks. By utilizing a millimeter-wave (mmWave), terahertz (THz) frequency band, and cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) wireless communication technologies, we provide vehicle passengers with the necessary bandwidth resources. Additionally, we address bandwidth contention issues in congested road segments by incorporating communication methods tailored to the characteristics of vehicular environments. By classifying users and adjusting according to the unique requirements of various multimedia applications, we ensure that real-time applications receive adequate bandwidth. Simulation experiments validate the proposed method’s effectiveness in managing bandwidth allocation for in-vehicle video applications within V2X networks. It increases the available bandwidth during peak hours by 32%, demonstrating its ability to reduce network congestion and ensure smooth playback of various video application types.

A GSO‐based multi‐objective technique for performance optimization of blockchain‐based industrial Internet of things

SummaryThe latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain‐based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain‐based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain‐based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision‐making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain‐based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations.

Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems

Utilizing unmanned aerial vehicles (UAVs) as mobile access points or base stations has emerged as a promising solution to address the excessive traffic demands in wireless networks. This paper investigates improving the detector performance at the unmanned aerial vehicle base stations (UAV-BSs) in an uplink grant-free non-orthogonal multiple access (GF-NOMA) system by considering the activity state (AS) temporal correlation of the different user equipments (UEs) in the time domain. The Bernoulli Gaussian-Markov chain (BG-MC) probability model is used for exploiting both the sparsity and slow change characteristic of the AS of the UE. The GAMP Bernoulli Gaussian-Markov chain (GAMP-BG-MC) algorithm is proposed to improve the detector performance, which can utilize the bidirectional message passing between the neighboring time slots to fully exploit the temporally correlated AS of the UE. Furthermore, the parameters of the BG-MC model can be updated adaptively during the estimation procedure with unknown system statistics. Simulation results show that the proposed algorithm can improve the detection accuracy compared to existing methods while keeping the same order complexity.

Resource Control in Active IRS-Aided 6G IoT Networks with Use Case in Smart Indoor Communication

The IoT ecosystem involves connected smart devices that support massive amount of data for processing and further analysis. The proliferating massive IoT network demands network connectivity, robust communication links and computational resources which puts huge load on the network. To overcome the communication overhead in the IoT landscape while offering optimal fault tolerance is the main challenge. This paper presents an intelligent solution for improving the communication efficiency of IoT network using active IRS technology. An active IRS aided communication framework is proposed which offers enhanced network connectivity by enabling controlled reflection amplitude. An algorithm is proposed which associates optimal active IRS to each AP-node link such that signal-to-interference-plus-noise ratio (SINR) is maximized. It is observed that the proposed association scheme in active IRS system offers an improvement of 19.04% in achievable rate over random association at AP transmit power [Formula: see text] of 20[Formula: see text]dBm and IRS amplification power [Formula: see text] of 4[Formula: see text]dBm. Furthermore, the system outage probability is carried out with change in [Formula: see text] and amplification gain [Formula: see text]. The mean channel power is also evaluated for different IRS densities and AP densities under different IRS reflecting elements [Formula: see text], [Formula: see text] and [Formula: see text]. In the end, the performance comparison with passive IRS system and system with no IRS assistance is carried out. A use case scenario of active IRS-aided system in smart indoor communication is also discussed.

A Placement Method of the 5G Edge Nodes Based on the Hotspot Distribution of Mobile Users

Due to the emergence of various new applications, such as short videos and online games, higher requirements of their computing and storage capacity are demanded of mobile networks. The traditional cloud computing paradigm has the shortcomings of large latency and high bandwidth demand of the core network. Therefore, how to mine the hotspot distribution of these applications and reasonably configure 5G edge nodes to reduce latency and core network bandwidth are facing great challenges. To address these issues, we designed a placement method for the 5G edge nodes based on mobile hotspots. In this method, we first cluster all locations from the user trajectories to obtain the cluster areas. Further, we extract the features, such as the number of users and duration time in all cluster areas, and extract the hotspots from all cluster areas based on the features of each cluster. Then, we introduce the base station’s high load utilization rate and the core network’s bandwidth reduction rate as the optimization parameters to construct the mathematical model of multi-objective optimization. Finally, we formalize the model into a 0–1 integer programming problem and design a greedy algorithm to solve this model. We also complete a series of experiments to evaluate our proposed methods using the GeoLife dataset. The experimental results show that the high load utilization rate can be increased up to 7.69%, and the bandwidth reduction rate of the core network can be improved up to 6.34%.

ReLeC-based clustering and multi-objective optimization for efficient energy optimization in IoT networks

In response to the escalating demand for energy-efficient wireless sensor networks (WSNs) within the expanding Internet of Things (IoT) landscape, we introduce ReLeC-MO, a novel protocol that integrates the ReLeC clustering algorithm with multi-objective optimization. Leveraging reinforcement learning-based clustering, ReLeC optimizes network topology to enhance energy efficiency. Multi-objective optimization further refines this process by identifying non-dominated solutions on the Pareto front, facilitating a balanced trade-off between network lifetime, energy consumption, and data transmission quality. Our comprehensive simulations reveal the remarkable performance improvements achieved by ReLeC-MO over existing techniques. Specifically, ReLeC-MO demonstrates a 39% reduction in delay, a 50% decrease in energy consumption, and a 25% increase in throughput, showcasing its efficacy in enhancing both energy efficiency and network performance. It also increases network lifetime by 20%, surpassing the latest existing model. Furthermore, its implementation in MATLAB ensures ease of replication and adaptation across diverse IoT applications.

Transport capacity optimization for high-speed rail network considering flexible train composition and additional capacity pool

To balance the limited capacity and constantly changing demand in the high-speed rail network, a transport capacity optimization problem is investigated by adjusting train composition through uncoupling/coupling operations at the origin station, scheduling additional trains from the capacity pool, and optimizing the seat allocation among ODs. To make joint decisions, a two-stage stochastic programming model is formulated to minimize both operating cost and penalty cost for unsatisfied passengers, while incorporating various constraints such as flow conservation, loading capacity, flexible composition, etc. The two-stage model is linearized into a mixed-integer linear programming model, which can be effectively solved for small networks but is still intractable to solve for realistic networks. Therefore, a Lagrangian relaxation algorithm is proposed integrating parallel computing and sub-gradient method. Finally, two sets of case tests, including a toy network and a real-world case of a high-speed rail network in China, are implemented to show the performance of the proposed approach. The results illustrate that the parallel heuristic algorithm could generate a high-quality solution with a slight discrepancy from the optimal solution, while achieving a commendable computation time. Furthermore, the findings emphasize the advantages of applying flexible train composition and additional capacity pool in the high-speed railway system for cost-saving and supply–demand matching.