Network Solution Research Articles

Scaling solutions for current-carrying cosmic string networks

Cosmic string networks are the best motivated relics of cosmological phase transitions, being unavoidable in many physically plausible extensions of the Standard Model. Most studies, including those providing constraints from and forecasts of their observational signals, rely on assumptions of featureless networks, neglecting the additional degrees of freedom on the string worldsheet, e.g., charges and currents, which are all but unavoidable in physically realistic models. An extension of the canonical velocity-dependent one-scale model, accounting for all such possible degrees of freedom, has been recently developed. Here we improve its physical interpretation by studying and classifying its possible asymptotic scaling solutions, and in particular how they are affected by the expansion of the Universe and the available energy loss or transfer mechanisms. We find three classes of solutions. For sufficiently fast expansion rates the charges and currents decay and one asymptotes to the Nambu-Goto case, while for slower expansion rates they can dominate the network dynamics. In between the two there is a third regime in which the network, including its charge and current, reaches full scaling. Under specific but plausible assumptions, this intermediate regime corresponds to the matter-dominated era. Our results agree with, and significantly extend, those of previous studies. Published by the American Physical Society 2024

Energy efficiency and interoperability through O-RAN Rapid Transition Protocol (ORTP)

Mobile network traffic is increasing accompanied by increased energy consumption. Traditional Radio Access Networks (RANs) have been used for decades as the foundation of mobile communications, providing mobile customers with reliable voice and data services. However, they encounter obstacles in terms of scalability, adaptability, and cost-effectiveness. Next-generation wireless networks need efficient Radio Access Network (RAN) solutions to achieve low latency and high throughput. The Open RAN (O-RAN) architecture is a potential solution for 5G and beyond networks due to its open interfaces, disaggregated network entities and services, network hardware and software virtualization, and intelligent control. Traditionally standard RAN accounts for the bulk of mobile network energy consumption, leading towards higher Operational expenditure (OPEX) costs. In the context of cellular networks. Handover strategies need careful development to cater for efficient resource usage as well as overall system energy efficiency. O-RAN Rapid Transition Protocol (ORTP) presented in this paper employs minimum value of handover margin (HOM) and is aimed at increased energy efficiency. 5G based System level simulations have been performed to investigate the efficacy of ORTP for key performance indicators including handover probability, Radio Link Failure, connection density, ping pong effect and dynamic power consumption. It is found that the usage of lower HOM values provide noticeable achievement in terms of power consumption, thereby rendering ORTP around 20 % more efficient compared to 5G networks, while keeping it interoperable.

Enhancing routing and quality of service using AI-driven technique for internet of vehicles contexts

This paper introduces an Enhancing Routing and Quality of Service (QoS) Using AI-driven Technique for Internet of Vehicles (IoV) Contexts. The proposed approach aims to enhance QoS and overall network performance in connected vehicle networks. Vehicles utilize Road-Side Units and the Internet to aggregate data from neighboring vehicles, optimizing routing and data management. The proposed approach utilizes a graph traversal algorithm, a widely adopted technique in Artificial Intelligence for graph search, which facilitates traversal in routing. The proposed approach integrates a mobility score based on metrics such as velocity, acceleration, and neighboring information, ensuring optimal routing for dynamic vehicular networks. Objectives include improving QoS and network performance by reducing overheads, optimizing load balancing, and extending network lifetime. Implemented in NS-3 and MOVE simulators, results demonstrate significant performance enhancements compared to existing approaches. This approach addresses the challenges of extensive vehicle mobility and frequent topology changes in connected environments. Overall, the paper presents a comprehensive solution for IoV networks, combining AI-driven routing with mobility-aware strategies to advance network efficiency and QoS.

Optimizing Network Costs for NFV Solutions in Urban and Rural Indian Cellular Networks

Optimizing Network Costs for NFV Solutions in Urban and Rural Indian Cellular Networks

A Multi-Agent Reinforcement Learning-Based Task-Offloading Strategy in a Blockchain-Enabled Edge Computing Network

In recent years, many mobile edge computing network solutions have enhanced data privacy and security and built a trusted network mechanism by introducing blockchain technology. However, this also complicates the task-offloading problem of blockchain-enabled mobile edge computing, and traditional evolutionary learning and single-agent reinforcement learning algorithms are difficult to solve effectively. In this paper, we propose a blockchain-enabled mobile edge computing task-offloading strategy based on multi-agent reinforcement learning. First, we innovatively propose a blockchain-enabled mobile edge computing task-offloading model by comprehensively considering optimization objectives such as task execution energy consumption, processing delay, user privacy metrics, and blockchain incentive rewards. Then, we propose a deep reinforcement learning algorithm based on multiple agents sharing a global memory pool using the actor–critic architecture, which enables each agent to acquire the experience of another agent during the training process to enhance the collaborative capability among agents and overall performance. In addition, we adopt attenuatable Gaussian noise into the action space selection process in the actor network to avoid falling into the local optimum. Finally, experiments show that this scheme’s comprehensive cost calculation performance is enhanced by more than 10% compared with other multi-agent reinforcement learning algorithms. In addition, Gaussian random noise-based action space selection and a global memory pool improve the performance by 38.36% and 43.59%, respectively.

Improving Air Quality Prediction via Self-Supervision Masked Air Modeling

Presently, the harm to human health created by air pollution has greatly drawn public attention, in particular, vehicle emissions including nitrogen oxides as well as particulate matter. How to predict air quality, e.g., pollutant concentration, efficiently and accurately is a core problem in environmental research. Developing a robust air quality predictive model has become an increasingly important task, holding practical significance in the formulation of effective control policies. Recently, deep learning has progressed significantly in air quality prediction. In this paper, we go one step further and present a neat scheme of masked autoencoders, termed as masked air modeling (MAM), for sequence data self-supervised learning, which addresses the challenges posed by missing data. Specifically, the front end of our pipeline integrates a WRF-CAMx numerical model, which can simulate the process of emission, diffusion, transformation, and removal of pollutants based on atmospheric physics and chemical reactions. Then, the predicted results of WRF-CAMx are concatenated into a time series, and fed into an asymmetric Transformer-based encoder–decoder architecture for pre-training via random masking. Finally, we fine-tune an additional regression network, based on the pre-trained encoder, to predict ozone (O 3) concentration. Coupling these two designs enables us to consider the atmospheric physics and chemical reactions of pollutants while inheriting the long-range dependency modeling capabilities of the Transformer. The experimental results indicated that our approach effectively enhances the WRF-CAMx model’s predictive capabilities and outperforms pure supervised network solutions. Overall, using advanced self-supervision approaches, our work provides a novel perspective for further improving air quality forecasting, which allows us to increase the smartness and resilience of the air prediction systems. This is due to the fact that accurate prediction of air pollutant concentrations is essential for detecting pollution events and implementing effective response strategies, thereby promoting environmentally sustainable development.

Multi-Layer PVA-PANI Conductive Hydrogel for Symmetrical Supercapacitors: Preparation and Characterization.

This work describes a simple, inexpensive, and robust method to prepare a flexible "all in one" integrated hydrogel supercapacitors (HySCs). Preparing smart hydrogels with high electrical conductivity, ability to stretch significantly, and excellent mechanical properties is the last challenge for tailored wearable devices. In this paper, we employed a physical crosslinking process that involves consecutive freezing and thawing cycles to prepare a polyvinyl alcohol (PVA)-based hydrogel. Exploiting the self-healing properties of these materials, the assembly of the different layers of the HySCs has been performed. The ionic conductivity within the electrolyte layer arises from the inclusion of an H2SO4 solution in the hydrogel network. Instead, the electronic conductivity is facilitated by the addition of the conductive polymer PANI-PAMPSA into the hydrogel layers. Electrochemical measures have highlighted newsworthy properties related to our HySCs, opening their use in wearable electronic applications.

Transport SDN architecture for multi-layer transport slicing

The proposed architecture advances the concept of network slicing, crucial for beyond 5G services, by enabling dynamic resource allocation and customized partitioning in managed network infrastructures. This architecture addresses the challenges of provisioning end-to-end (E2E) slices across diverse network domains, which is complicated by technological heterogeneity and the variety of vendor solutions. By introducing a standardized transport network solution, we ensure seamless integration, equitable treatment of service requests, and the ability to meet diverse demands. The architecture is centered around a multi-layer transport network slicing architecture, which allows for the division of transport networks into virtual autonomous segments, each tailored for specific services or applications. This segmentation is essential for providing differentiated and personalized 5G services, optimizing network performance, and maximizing resource application. A key component of this architecture is the transport slice controller (TSC), which controls the provision and life-cycle management of transport slices, ensuring a standardized approach in the industry for the definition and realization of slices.

Accelerator: an intent-based intelligent resource-slicing scheme for SFC-based 6G application execution over SDN- and NFV-empowered zero-touch network

Zero-touch networks (ZTNs) can provide autonomous network solutions by integrating software-based solutions for various emerging 5G and 6G applications. The current literature does not provide any suitable end-to-end network management and resource-slicing solutions for service function chaining (SFC) and user intent–based (time and cost preference) 6G/non-6G application execution over ZTNs enabled by mobile edge computing, network function virtualization, and software-defined networking. To tackle these challenges, this work initiates an end-to-end network management and user intent–aware intelligent network resource–slicing scheme for SFC-based 6G/non-6G application execution over ZTNs, taking into account various virtual and physical resources, task workloads, service requirements, and task numbers. The results depicted that at least 25.27% average task implementation delay gain, 6.15% energy gain, and 11.52% service monetary gain are realized in the proposed scheme over the compared schemes.

Generalized-Type Multistability of Almost Periodic Solutions for Memristive Cohen-Grossberg Neural Networks.

This article investigates a generalized type of multistability about almost periodic solutions for memristive Cohen-Grossberg neural networks (MCGNNs). As the inevitable disturbances in biological neurons, almost periodic solutions are more common in nature than equilibrium points (EPs). They are also generalizations of EPs in mathematics. According to the concepts of almost periodic solutions and Ψ -type stability, this article presents a generalized-type multistability definition of almost periodic solutions. The results show that (K+1)n generalized stable almost periodic solutions can coexist in a MCGNN with n neurons, where K is a parameter of the activation functions. The enlarged attraction basins are also estimated based on the original state space partition method. Some comparisons and convincing simulations are given to verify the theoretical results at the end of this article.

The Toxic Cost of Cheap Usernames: Re-Applying Friedman and Resnick's Framework in Competitive Video Game Networks

Toxicity in video games, acting in a rude, abusive, bullying, or deliberately losing manner, ruins competitive team-based video game experiences for everyone involved. Companies are experimenting with the detection, regulation, and bans of toxic players. Regulation attempts are foiled by the ease with which players can switch accounts by creating new profiles to evade consequences. This article applies the framework of Friedman and Resnick (2001), “The Social Cost of Cheap Pseudonyms”, to address the reputational and repetition cost of online pseudonyms in the niche environment of video game toxicity. Four potential solutions are discussed in the context of modern video game regulation: resource-intensive account set-up, paid dues, real-world identification, and a once-in-a-lifetime identification system that creates a permanent, traceable record of toxicity. The article covers the strengths, weaknesses, feasibility, implementation challenges, and player impact of the potential implementation of these solutions in video game networks.

A Comprehensive Survey of Load Balancing Techniques in Multipath Energy-Consuming Routing Protocols for Wireless Ad hoc Networks in MANET

As technology progresses, networking undergoes continuous evolution to align with contemporary trends. Mobile Ad hoc Networks (MANETs) have emerged as a pivotal technology facilitating communication among mobile nodes sans a central administering node. MANETs find diverse applications spanning communication networks to battlefield scenarios. Nonetheless, MANETs encounter various challenges, including load balancing, energy efficiency, packet loss, and connection failures. Routing, among these challenges, assumes a pivotal role in defining network performance and connectivity capacity within MANETs. Moreover, the energy consumption of mobile nodes, often reliant on batteries with limited rechargeability, presents a significant concern in MANETs. This paper delves into diverse techniques devised to tackle the issues of energy efficiency and load balancing in MANETs. Additionally, it offers a comparative analysis to consolidate the survey findings. The outcomes of this study enrich our comprehension of energy-efficient and load-balanced routing protocols in MANETs, thereby facilitating the design and deployment of efficient networking solutions.

Survey: State-of-the-Art Energy-Consumption Optimization Solutions for Mobile Ad-Hoc Networks

Survey: State-of-the-Art Energy-Consumption Optimization Solutions for Mobile Ad-Hoc Networks

НЕЙРОСЕТЕВОЕ ПРОГНОЗИРОВАНИЕ ПАРАМЕТРОВ ТРАНСПОРТНЫХ ПОТОКОВ В ИНТЕЛЛЕКТУАЛЬНЫХ СИСТЕМАХ СВЕТОФОРНОГО РЕГУЛИРОВАНИЯ

A method is proposed for neural network prediction of congestion on road sections outside the control zones of transport detectors in the systems of “flexible” transport flow control at intersections with the traffic light regulation. The paper describes the operating principle of the original control system based on fuzzy logic. To develop a universal neural network solution that can be used to predict traffic on most road sections with common characteristics without the need for training for each case separately, it is proposed to identify 9 main types of sections and, accordingly, 9 neural networks. Each typical neural network differs in the amount of the input data and parameters. All neural networks are trained on samples obtained from analyzing traffic flow on road sections of each type. Typical characteristics of road sections, architecture, and parameters of neural networks are determined. Neural networks are trained and tested on the model data. Using computer simulation methods, the author assesses the control quality of the original intelligent traffic light control system, which has complete information about the transport flow (however, it is difficult to implement in practice), as well as a control system that uses a set of data obtained using transport detectors and supplemented with a neural network forecast. According to the experiment results, a system with a neural network forecast of congestion on road sections is inferior in quality of control by no more than 1.92% compared to an idealized system, which gives grounds to consider this method applicable in intelligent transport flow control systems at intersections with traffic light regulation.

An adaptive MLP-based joint optimization of resource allocation and relay selection in device-to-device communication using hybrid meta-heuristic algorithm

Enhancement in both energy efficiency and spectral efficiency in cellular networks is made possible by means of an advanced technique called device-to device (D2D) communication. The enhancement of these efficiencies is done by utilizing the cellular user (CU) resources once again to make communication with nearby cellular devices in an effective manner by spectral means. As the D2D communication technology is capable of providing a direct communication link with nearby devices effectively with enhanced spectral efficacy, this approach is considered as an ideal solution for the futuristic cellular communication network. A flexible and reliable relay-assisted communication by means of D2D technology is required that acts as an intermediate relay when the attenuation between the channels across the D2D devices becomes high. The throughput of the system is increased by utilizing D2D communication technology as it uses direct data transmission within cellular devices. When the cellular user is far apart from one another, the data loss in the D2D communication system is minimized with the utilization of the relay. When the channels are good, then the relay nodes (RNs) serve the cellular user. However, it is noted that the D2D systems are affected by issues such as higher consumption of energy and spectral sharing. Also, the sum rate gets degraded as a result of mutual interference between resource-sharing cellular devices in the relay-assisted D2D communication system. The transmission of the data in a traditional relay-assisted D2D communication system is carried out between the D2D receiver (DR) and D2D transmitter (DT) only with the utilization of its own energy by the RN. The issues in relay selection and resource allocation in the conventional joint resource allocation schemes are tackled by executing a scheme for performing the task of optimal relay selection and joint resource allocation. The enhancement of the overall sum rate in the D2D communication is the main motive behind the implemented scheme. This goal is attained along with the minimization of the link rates in the cellular and D2D networks. The ideal selection of the relay and the execution of the joint resource allocation are done with the utilization of a new optimization scheme called the hybrid flow direction with the chameleon swarm algorithm (HFDCSA), in which the flow direction algorithm (FDA) is fused along with chameleon swarm algorithm. This optimal selection of the relays is assisted by considering constraints like the network’s sum rate and energy efficiency in the network to achieve high performance. The data obtained from distinct sources are given to the adaptive multi-layer perceptron (AMLP) in which optimal resource allocation and the relay selection are performed with the help of the suggested HFDCSA. The parameters in the MLP are tuned by the same HFDCSA. Finally, the performance validation is conducted in the stage to verify the working of the suggested approach.

Reliable lifelong planning A*: Technique for re-optimizing reliable shortest paths when travel time distribution updating

Re-optimization technique is an efficient approach for solving the shortest path problem in dynamic deterministic networks, where link travel times are updated in real-time. However, existing re-optimization techniques, built on the assumption that link travel times are deterministic, cannot be used to solve reliable shortest path problems in real road networks with noticeable levels of travel time uncertainties. This study proposes a novel re-optimization technique, named reliable lifelong planning A* (RLPA*), for re-optimizing reliable shortest path finding results in dynamic stochastic networks, where link travel time distributions are updated in real-time. The proposed RLPA* technique can efficiently determine the optimal solution in dynamic stochastic networks by reusing path search results produced in the previous time instance. The proposed RLPA* technique is further utilized to solve the K reliable shortest paths problem, which is regarded as a series of reliable shortest path searches in a dynamic stochastic network. To validate the proposed algorithms, a comprehensive case study using real traffic data is conducted. The case study results demonstrated that the proposed algorithms significantly outperform the corresponding state-of-the-art algorithms on all testing networks.

Adaptive convergence enhancement strategies for Newton–Raphson power flow solutions in distribution networks

AbstractThis study introduces a series of strategies to enhance convergence in the Newton–Raphson power flow method for unbalanced distribution networks. The proposed approach incorporates graph theory, Kron reduction, and current injection techniques. The network components, including transmission conductors, power transformers, power conductors, shunt capacitors/reactors, and demand loads, were merged into the bus impedance matrix. Using two identification processes (based on mismatches for bus voltages and bus power injections) and optimal multipliers (μ), the proposed approach estimates the initial bus voltages by approximating the converged solutions. To verify the effectiveness of the proposed approach, the authors performed a comparative analysis of five IEEE test feeders in various scenarios. The results confirmed the effectiveness of the proposed approach, particularly for unbalanced distribution systems with diverse transformer connections.

Advanced optical transceiver and switching solutions for next-generation optical networks

Innovative transceiver and switching approaches should be explored with special focus on flexibility, energy efficiency, sustainability, and interoperability to be adopted on next-generation 6G optical networks driven by the diverse landscape of emerging applications and services and increasing traffic demand. In this regard, multiband (MB) and spatial division multiplexing (SDM) technologies arise as promising technologies for providing suitable network capacity scaling while fulfilling the stringent requirements of the incoming 6G era. In this paper, innovative MB over SDM (MBoSDM) switching node and sliceable bandwidth/bit rate variable transceiver (S-BVT) architectures with enhanced capabilities and features are proposed and experimentally validated. Different network scenarios have been identified and assessed, enabling up to 180.9 Gb/s S+C+L transmission in back-to-back (B2B) configuration. A MBoSDM scenario including both transceiver and switching solutions is demonstrated, including a 19-core multi-core fiber (MCF) of 25.4 km. Thanks to the transceiver modular and scalable approach, higher capacities can be envisioned by enabling multiple slices working in the different bands beyond the C-band. A power efficiency analysis of the proposed transceiver is also presented, including a pathway towards the integration with a software defined networking (SDN) control plane assisted by energy-aware artificial intelligence (AI)/machine learning (ML) trained models.

Automated hearing loss type classification based on pure tone audiometry data

Hearing problems are commonly diagnosed with the use of tonal audiometry, which measures a patient’s hearing threshold in both air and bone conduction at various frequencies. Results of audiometry tests, usually represented graphically in the form of an audiogram, need to be interpreted by a professional audiologist in order to determine the exact type of hearing loss and administer proper treatment. However, the small number of professionals in the field can severely delay proper diagnosis. The presented work proposes a neural network solution for classification of tonal audiometry data. The solution, based on the Bidirectional Long Short-Term Memory architecture, has been devised and evaluated for classifying audiometry results into four classes, representing normal hearing, conductive hearing loss, mixed hearing loss, and sensorineural hearing loss. The network was trained using 15,046 test results analysed and categorised by professional audiologists. The proposed model achieves 99.33% classification accuracy on datasets outside of training. In clinical application, the model allows general practitioners to independently classify tonal audiometry results for patient referral. In addition, the proposed solution provides audiologists and otolaryngologists with access to an AI decision support system that has the potential to reduce their burden, improve diagnostic accuracy, and minimise human error.

Is order-2 proportionality good enough for approximating the most likely path flow in user equilibrium traffic assignment?

The proportionality condition is a standard approach to dealing with the non-uniqueness issue in the user equilibrium (UE) traffic assignment problems (TAP). Although the proportionality condition can reduce the degree of arbitrariness, it remains unclear how much arbitrariness remains and whether it can meaningfully affect model outcomes and relevant decisions that depend on them. The answers to these questions are impeded by the lack of an efficient algorithm that can find the exact maximum entropy UE path flow solution for networks of practical size. In this paper, we fill this gap by developing a high-performance augmented Lagrangian algorithm that effectively exploits the special problem structure. Our numerical results reveal that there are a considerable number of links with non-negligible arbitrariness in the solution generated by the proportionality condition, and that this problem becomes worse if the level of congestion increases in the network. Since about a decade ago, many practitioners have relied on state-of-the-art traffic assignment tools based on the proportionality condition to perform select link analysis, among other applications. The results reported herein are a reminder that their toolbox may need reevaluation and perhaps an upgrade.