Network Resources Research Articles

Cloud-edge hybrid deep learning framework for scalable IoT resource optimization

In the dynamic environment of the Internet of Things (IoT), edge and cloud computing play critical roles in analysing and storing data from numerous connected devices to produce valuable insights. Efficient resource allocation and workload distribution are vital to ensuring continuous and reliable service in growing IoT ecosystems with increasing data volumes and changing application demands. This study proposes a novel optimisation approach utilising deep learning to tackle these challenges. The integration of Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) offers a practical approach to addressing the dynamic characteristics of IoT applications. The hybrid algorithm's primary characteristic is its capacity to simultaneously fulfil multiple objectives, including reducing response times, enhancing resource efficiency, and decreasing operational costs. DQN facilitates the formulation of optimal resource allocation strategies in intricate and unpredictable environments. PPO enhances policies in continuous action spaces to guarantee reliable performance in real-time, dynamic IoT settings. This method achieves an optimal equilibrium between policy learning and optimisation, rendering it suitable for contemporary IoT systems. This method improves numerous IoT applications, including smart cities, industrial automation, and healthcare. The hybrid DQN-PPO-GNN-RL model addresses bottlenecks by dynamically managing computing and network resources, allowing for efficient operations in low-latency, high-demand environments such as autonomous systems, sensor networks, and real-time monitoring. The use of Graph Neural Networks (GNNs) improves the accuracy of resource representation, while reinforcement learning-based scheduling allows for seamless adaptation to changing workloads. Simulations using real-world IoT data on the iFogSim platform showed significant improvements: task scheduling time was reduced by 21%, operational costs by 17%, and energy consumption by 22%. The method reliably provided equitable resource distribution, with values between 0.93 and 0.99, guaranteeing efficient allocation throughout the network. This hybrid methodology establishes a novel benchmark for scalable, real-time resource management in extensive, data-centric IoT ecosystems, consequently enhancing system performance and operational efficiency.

Innovative Application of 6G Network Slicing Driven by Artificial Intelligence in the Internet of Vehicles

ABSTRACTThe rapid growth of vehicle networks in the Internet of Vehicles (IoV) needs novel approaches to optimizing network resource allocation and enhancing traffic management. Sixth‐generation (6G) network slicing, when paired with artificial intelligence (AI), has enormous potential in this field. The purpose of this research is to investigate the use of AI‐driven 6G network slicing (NS) for efficient usage of resources and accurate traffic prediction in IoV systems. A unique network design is suggested, combining data‐driven approaches and dynamic network slicing. Data are acquired from vehicular sensors and traffic monitoring systems, and log transformation is used to handle exponential growth patterns like vehicle counts and congestion levels. The Fourier transform (FT) is used to extract frequency‐domain information from traffic data, which allows for the detection of periodic patterns, trends, and anomalies such as vehicle velocity and traffic density. The Dipper Throated Optimized Efficient Elman Neural Network (DTO‐EENN) is used to forecast traffic and optimize resources. This technology allows the system to predict traffic patterns and dynamically alter network slices to ensure optimal resource allocation while reducing latency. The results show that the suggested AI‐driven NS technique increases forecast accuracy and network performance while dramatically reducing congestion levels. The research indicates that AI‐driven 6G based NS offers a solid framework for optimizing IoV performance.

Retraction Note: Encrypted Network Traffic Classification and Resource Allocation with Deep Learning in Software Defined Network

Retraction Note: Encrypted Network Traffic Classification and Resource Allocation with Deep Learning in Software Defined Network

Hierarchical Resource Management for Mega-LEO Satellite Constellation

The mega-low Earth orbit (LEO) satellite constellation is pivotal for the future of satellite Internet and 6G networks. In the mega-LEO satellite constellation system (MLSCS), which is the spatial distribution of satellites, global users, and their services, along with the utilization of global spectrum resources, significantly impacts resource allocation and scheduling. This paper addresses the challenge of effectively allocating system resources based on service and resource distribution, particularly in hotspot areas where user demand is concentrated, to enhance resource utilization efficiency. We propose a novel three-layer management architecture designed to implement scheduling strategies and alleviate the processing burden on the terrestrial Network Control Center (NCC), while providing real-time scheduling capabilities to adapt to rapid changes in network topology, resource distribution, and service requirements. The three layers of the resource management architecture—NCC, space base station (SBS), and user terminal (UT)—are discussed in detail, along with the functions and responsibilities of each layer. Additionally, we explore various resource scheduling strategies, approaches, and algorithms, including spectrum cognition, interference coordination, beam scheduling, multi-satellite collaboration, and random access. Simulations demonstrate the effectiveness of the proposed approaches and algorithms, indicating significant improvements in resource management in the MLSCS.

Minimizing unavailability in Elastic Optical Networks: Pre-provisioning and provisioning protection strategy using DLP and DPP

The rapid growth of internet traffic, driven by advancements in 5G and cloud services, has intensified the need for reliable optical networks. Infrastructure failures in these networks can lead to significant disruptions and financial losses, highlighting the critical importance of robust protection mechanisms. While existing research has explored various protection strategies based on on-demand provisioning of resources, a gap remains in understanding the effectiveness of pre-provisioning mechanisms specifically tailored for Elastic Optical Networks (EONs). Pre-provisioning is a strategy that involves reserving network resources, such as bandwidth or lightpaths, in advance of anticipated traffic demands. This proactive approach ensures that resources are readily available when needed, reducing setup times and enhancing network resilience. In this paper we propose a novel algorithm, BPreProvEON, designed to optimize resource allocation and enhance the survivability of EONs through a hybrid combination of pre-provisioning and on-demand provisioning of protection schemes. Extensive comparisons with traditional non-pre-provisioning and Shared Path Protection (SPP)-based pre-provisioning demonstrate BPreProvEON’s superior performance in bandwidth blocking rate and connection availability, highlighting its potential for enhancing the resilience and performance of optical networks.

Software-Defined Networking Challenges and Research Opportunities for Future Interest

Software Defined Network (SDN) has become one of the most preferred solutions for the management of large-scale complex networks. The network policies in the large-scale network are difficult to embed on entire network devices simultaneously, whereas in SDN these policies can be embedded on the top of the network. The SDN network is divided into two parts which are vertically integrated to form the entire network. Currently many aspects of the classical architecture of the Internet are etched in stone – a so-called ossification of the Internet – which has led to major obstacles in IPv6 deployment and difficulty in using IP multicast services. Yet, there exist many reasons to extend the Internet, e.g., for improving intra-domain and inter- domain routing for high availability of the network, providing end-to-end connectivity for users, and allowing dynamic management of network resources for new applications, such as data center, cloud computing, and network virtualization. To address these requirements, the next- generation architecture for the Future Internet has introduced the concept of Software-Defined Networking (SDN). At the core of this emerging paradigm is the separation and centralization of the control plane from the forwarding elements in the network as opposed to the distributed control plane of existing networks. With the advent of cloud computing, many ecosystem and business paradigms are encountering potential changes and may be able to eliminate their IT infrastructure maintenance processes. Real-time performance and high availability requirements have induced telecom networks to adopt the new concepts of the cloud model: software-defined networking (SDN) and network function virtualization (NFV). NFV introduces and deploys new network functions in an open and standardized IT environment, while SDN aims to transform the way network’s function. SDN and NFV are complementary technologies; they do not depend on each other. However, both concepts can be merged and have the potential to mitigate the challenges of legacy networks. In this paper, our aim is to describe the benefits of using SDN in a multitude of environments such as in data centers, data center networks, and Network as service offerings.

AI-Driven Network Supervision in 6G Enhancing Self-Association, Optimization, and Self-governing Maintenance

The advent of 6G networks is expected to transfigure communication by providing ultra-high speeds, low latency, and omnipresent connectivity. One of the central challenges in 6G is well-organized network management, specifically as the scale and complexity of the network grow. This paper explores the role of Artificial Intelligence (AI) and Machine Learning (ML) in 6G network design, concentrating on their application in network self-organization, optimization, and analytical maintenance. By utilizing AI-driven algorithms, 6G networks can separately manage resources, optimize presentation, and predictively address faults before they interrupt services. The paper discusses the implementation of AI technologies in network resource allocation, real-time traffic management, fault detection, and automated retrieval mechanisms, prominence both the opportunities and challenges associated with these innovations. The proposed approach aims to establish a more resilient, adaptive, and effectual network capable of meeting the demands of upcoming generations.

Unveiling factors affecting college students’ online self-directed learning: logit-ISM model analysis

ABSTRACT The ubiquity of online education in college settings underscores the importance of investigating the influencing factors of online self-directed learning effectiveness. Such exploration not only contributes to theoretical advancements but also offers practical insights for enhancing college students’ academic quality, fostering the growth of online education, and reforming teaching methodologies in higher education institutions. This paper undertakes an analysis of the influencing factors and their hierarchical logical structure concerning college students’ efficacy in self-directed learning within the online environment. Leveraging survey data obtained from a random sample of 966 Chinese college students, the study employs the Logit-ISM (Interpretative Structural Modeling) model. Findings reveal that various factors including college students’ grade, parental educational background, employment difficulty, network resource application, self-planning, self-drive, self-assessment, goal-orientation, self-management, and self-discipline abilities positively impact the online self-directed learning effectiveness. Moreover, results from the ISM demonstrate that self-drive, goal-orientation and self-management abilities directly influence online self-directed learning effectiveness, while self-planning, self-assessment and network resource application abilities indirectly contribute to this effectiveness. Furthermore, factors such as employment difficulty, grade, parental educational background and self-discipline ability are identified as fundamental determinants underlying online self-directed learning effectiveness.

Resource and Trajectory Optimization in RIS-Assisted Cognitive UAV Networks with Multiple Users Under Malicious Eavesdropping

Unmanned aerial vehicles (UAVs) have shown significant advantages in disaster relief, emergency communication, and Integrated Sensing and Communication (ISAC). However, the escalating demand for UAV spectrum is severely restricted by the scarcity of available spectrum, which in turn significantly limits communication performance. Additionally, the openness of the wireless channel poses a serious threat, such as wiretapping and jamming. Therefore, it is necessary to improve the security performance of the system. Recently, Reconfigurable Intelligent Surfaces (RIS), as a highly promising technology, has been integrated into Cognitive UAV Network. This integration enhances the legitimate signal while suppressing the eavesdropping signal. This paper investigates a RIS-assisted Cognitive UAV Network with multiple corresponding receiving users as cognitive users (CUs) in the presence of malicious eavesdroppers (Eav), in which the Cognitive UAV functions as the mobile aerial Base Station (BS) to transmit confidential messages for the users on the ground. Our primary aim is to attain the maximum secrecy bits by means of jointly optimizing the transmit power, access scheme of the CUs, the RIS phase shift matrix, and the trajectory. In light of the fact that the access scheme is an integer, the original problem proves to be a mixed integer non-convex one, which falls into the NP-hard category. To solve this problem, we propose block coordinate descent and successive convex approximation (BCD-SCA) algorithms. Firstly, we introduce the BCD algorithm to decouple the coupled variables and convert the original problem into four sub-problems for the non-convex subproblems to solve by the SCA algorithm. The results of our simulations indicate that the joint optimization scheme we have put forward not only achieves robust convergence but also outperforms conventional benchmark approaches.

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.

Dynamic Event-Triggered-Based Finite-Time Distributed Tracking Control of Networked Multi-UAV Systems

The distributed tracking of multiple unmanned aerial vehicles (UAVs) is a hotspot due to its broad applications in various fields, while continuous communication among UAVs is often impractical, especially in time-sensitive tasks or environments with limited bandwidth. With this in mind, this paper presents a finite-time leader-following distributed tracking control scheme for general multi-agent systems, with a particular emphasis on its application for networked UAVs. Theoretically, a dynamic event-triggered mechanism is proposed, which features a novel finite-time stable dynamic variable within its triggering rule, ensuring that neither controller updates nor trigger detection requires continuous communication. This event-triggered finite-time controller facilitates efficient network resource management and timely mission response in UAV cooperation, enhancing adaptability to onboard wireless communication networks and time-sensitive tasks. The method allows for the customization of parameters in the internal dynamic variables to adjust the convergence rate and event-triggering frequency of the system, while also preventing Zeno behavior. Moreover, a Lyapunov-based analysis is conducted to theoretically verify the finite-time stability of the closed-loop system and its applicability in directed communication networks. Finally, some numerical simulations are performed to validate the effectiveness of the proposed distributed control scheme for networked multi-UAV systems.

Secured DDoS Attack Detection in SDN Using TS‐RBDM With MDPP‐Streebog Based User Authentication

ABSTRACTIn a Distributed Denial of Service (DDoS) attack, the attacker aims to render a network resource unavailable to its intended users. A novel Software Defined Networking (SDN)‐centered secured DDoS attack detection system is presented in this paper by utilizing TanhSoftmax‐Restricted Boltzmann Dense Machines (TS‐RBDM) with a Mean Difference of Public key and Private key based Streebog (MDPP‐Streebog) user authentication algorithm. Primarily, in the registration phase, the users have registered their device details. The two‐stage login process is performed after successful registration. Then, in the network layer, the nodes are initialized, and via the Gate/Router, the sensed data is transmitted to the SDN controller to enhance network energy efficiency. Later, by using the CIC DDoS 2019 dataset, the DDoS detection system is trained. This dataset undergoes preprocessing, and features are extracted from it. By employing the Adaptive Synthetic (ADASYN) technique, data balancing is achieved. Lastly, by using the TS‐RBDM technique, the data is trained. The sensed data is categorized as either attacked or non‐attacked data within this trained DDoS detection system. By employing the Entropy Binomial probability‐based Shanon‐Fano‐Elias (EB‐SFE) technique, the non‐attacked data will be encoded and transmitted to the receiving terminal. Lastly, the experiential assessment illustrated that the proposed DDoS detection system attained 98% accuracy with 37 485 ms minimal training time, thus outperforming all state‐of‐the‐art methods.

Assessing the impact of network embeddedness on SMEs innovation performance through resource bricolage: a mediated-moderated model

Abstract The main objective of this study is to investigate the impact of network embeddedness, resource bricolage and organizational readiness on innovation performance based on social network theory and resource-based view. The study employed a convenience sampling technique for research survey. Data was collected from owners and CEOs of 379 small and medium-sized enterprises, operating in Pakistan. The study carried out partial least square structural equation modelling (PLS-SEM) for data analysis. The results showed that network embeddedness significantly affects innovation performance through partial mediation of resource bricolage, whereas organizational readiness positively moderates the relationship between resource bricolage and innovation performance. These findings provide a novel theoretical framework to bridge critical gaps in the existing literature. Current research extends the existing literature by integrating social network theory and resource-based view, particularly in the context of innovation performance of small and medium-sized enterprises. These results indicate that enterprises can manage resource-constraint challenges through business networks and bricolage practices and foster innovation performance. The study findings also provide practical insights for entrepreneurs, policymakers and institutions and recommends that business collaborations with efficient resource management and strategic preparedness, can achieve high level of innovation performance. The theoretical and practical implications improvise the strategic competitiveness, industrial collaborations and long-term sustainability for small and medium-sized enterprises.

Network Resource Mobilization in Community-Based Tourism Social Entrepreneurship

Network Resource Mobilization in Community-Based Tourism Social Entrepreneurship

Crayfish optimization based pixel selection using block scrambling based encryption for secure cloud computing environment

Cloud Computing (CC) is a fast emerging field that enables consumers to access network resources on-demand. However, ensuring a high level of security in CC environments remains a significant challenge. Traditional encryption algorithms are often inadequate in protecting confidential data, especially digital images, from complex cyberattacks. The increasing reliance on cloud storage and transmission of digital images has made it essential to develop strong security measures to stop unauthorized access and guarantee the integrity of sensitive information. This paper presents a novel Crayfish Optimization based Pixel Selection using Block Scrambling Based Encryption Approach (CFOPS-BSBEA) technique that offers a unique solution to improve security in cloud environments. By integrating steganography and encryption, the CFOPS-BSBEA technique provides a robust approach to secure digital images. Our key contribution lies in the development of a three-stage process that optimally selects pixels for steganography, encodes secret images using Block Scrambling Based Encryption, and embeds them in cover images. The CFOPS-BSBEA technique leverages the strengths of both steganography and encryption to provide a secure and effective approach to digital image protection. The Crayfish Optimization algorithm is used to select the most suitable pixels for steganography, ensuring that the secret image is embedded in a way that minimizes detection. The Block Scrambling Based Encryption algorithm is then used to encode the secret image, providing an additional layer of security. Experimental results show that the CFOPS-BSBEA technique outperforms existing models in terms of security performance. The proposed approach has significant implications for the secure storage and transmission of digital images in cloud environments, and its originality and novelty make it an attractive contribution to the field. Furthermore, the CFOPS-BSBEA technique has the potential to inspire further research in secure cloud computing environments, making the way for the development of more robust and efficient security measures.

Output‐Based Dual Dynamic Periodic Event‐Triggered Control for Nonlinear Interconnected Cyber‐Physical Systems Under Deception Attacks

ABSTRACTThis article considers the problem of output‐based dual dynamic periodic decentralized event‐triggered control (DPDETC) for nonlinear interconnected cyber‐physical systems (CPSs) subject to deception attacks. Taking into account the conflict between the limited network resources and the growing bandwidth demand, a dual dynamic periodic event‐triggered scheme (DPETS) is designed to reasonably decrease unnecessary data transmission, which includes sensor‐to‐controller (S‐C) and controller‐to‐actuator (C‐A) communication channels. For resisting the impact of attacks, a deception attacks model following Bernoulli distribution is introduced for nonlinear interconnected CPSs. Then, a new decentralized sampled‐data observer is proposed based on output‐dependent periodic event‐triggered conditions, such that the system output under deception attacks can be estimated. By means of decentralized observer and observer‐dependent DPETS, a new decentralized output feedback controller is established. The designed DPDETC strategy can guarantee that the nonlinear interconnected CPSs are mean square stability. Finally, simulation results are performed to validate the superiority of the designed DPDETC scheme.

Mitigation and Detection of Faulty Nodes in Multinode Hadoop Cluster using Hybrid Machine Learning Techniques

In today's modern significant computational systems, jobs are broken down into several smaller processes that run simultaneously to increase the rate at which jobs are completed and lower the amount of energy that is consumed. However, dealing with straggler processes, which are sluggish running processes that raise the total response time, is a typical performance challenge in these kinds of systems. These kinds of jobs have the potential to have a substantial effect on the Quality of Service (QoS) provided by the system. It is necessary to have automatic straggler identification and mitigation systems that can complete jobs in a shorter amount of time in order to address this problem. Previous work often constructs reactive frameworks, the central emphasis of which is, in order, the identification, followed by the mitigation, of straggler tasks, that ultimately results in delays. Other research make use of prediction-based proactive systems, however they disregard the peculiarities of heterogeneous hosts or dynamic tasks. In this article, Hybrid Machine Learning (HML) is offered as a method that may determine which jobs are likely to be behind schedule and dynamically adjust scheduling in order to obtain faster response times. The method that has been suggested examines all tasks as well as hosts on the basis of the use of compute and network resources, and it is also able to predict and mitigate the effects of expected straggler activities. This speeds up the execution without lowering the quality of service. The proposed HML is evaluated in terms of quality of service factors such as energy usage, processing time, resource contention, and CPU utilisation in comparison to other machine learning methods that already exist, including Support Vector Machine (SVM), ADABOOST, Artificial Neural Network (ANN), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF). According to the results of several evaluations, the proposed HML cuts down on processing time, resource contention, and energy usage by 13.5%, 11.25%, and 16.75%, correspondingly, when compared to standard machine learning methodologies. The proposed HML has a performance accuracy of 98.1%, making it superior to those other conventional ML methods.

Chief executive officer political connection and firm performance: Evidence from Nigerian listed deposit money banks

Purpose: Extant studies have investigated the determinants of firm performance; however, limited studies have focused on CEOs' political connections, especially in Nigeria, despite the postulation that the invisible hand of politics controls the economic sector. Our study fills the gap in the literature by examining the effect of the CEO's political connection on the financial performance of the Nigerian Deposit Money Banks. Design/ Approach/Methodology: The scope of this study is limited to the listed Nigerian Deposit Money Banks (DMBs) within the period 2011-2023. We employed a longitudinal research design by collecting 156 firm-year observations resulting from 12 listed DMBs and estimated using the ordinary least square method. Findings: Our study shows that CEO political connection is positive and significantly related to firm performance, supporting the assertion that CEO political connection improves the firm's financial performance, which aligns with the resource dependence theory. Conclusion: the study concluded banks CEO with political connection have a higher performance. Research Limitations/Implications: The study is limited to the Deposit Money Banks (DMBs); hence, we recommend that future studies focus on other sectors of the Nigerian economy. Our study contributed to limited empirical evidence on political connection and firm performance and shows that the CEO's political connection revolves around resource dependence and networking theories.

Enhanced IPv6 Addressing Scheme: Irreversible Elegant Pairing for Reconnaissance Attack Prevention

ABSTRACTIn scenarios where network resources are restricted, IPv6 addresses are frequently allocated to devices via SLAAC‐based EUI‐64 addressing to ensure their uniqueness. The consistent interface identifier (IID) resulting from this approach across networks poses a vulnerability to diverse reconnaissance attacks, encompassing activities like tracking locations, correlating network actions, scanning addresses, and targeting specific devices and so on. In response to this concern, our study introduces an innovative addressing strategy by utilizing the elegant pairing function. This technique ensures the creation of IPv6 addresses that are both nonpredictable and unique and effectively counteracts various reconnaissance attacks. Through empirical assessment, our proposed method achieves a 100% address success rate (ASR), effectively mitigating reconnaissance attacks without introducing additional communication overhead or heightened energy consumption.

Optimized Framework for Composite Cloud Service Selection: A Computational Intelligence‐Driven Approach

ABSTRACTOver the past decade, as demand for cloud services has surged, the strategic selection of these services has become increasingly crucial. The growing complexity within the cloud industry underscores the urgent need for a robust model for choosing cloud services effectively. Users often struggle to make informed decisions due to the dynamic nature and varying quality of available cloud services. In response, this paper introduces a novel decision‐making approach aimed at optimizing the selection process by identifying the most suitable combination of cloud services. The focus is on integrating these services into a cohesive ensemble to better fulfill user requirements. In contrast to existing methodologies, our approach evaluates cloud services on a continuous scale, taking into account critical tasks such as workload balancing, storage management, and network resource handling. We propose a model for selecting optimal composite cloud services, which includes real‐time optimization and addresses the consideration of null values for Quality of Service (QoS)‐based attributes (e.g., response time, cost, availability, and reliability) in the dataset—a factor overlooked by current literature. The proposed algorithm is inspired by computational intelligence and driven by an evolutionary algorithm‐based approach that undergoes evaluation across multiple datasets. The results illustrate its superiority, showcasing its ability to outperform existing optimization‐based methods in terms of execution time.