Network Resource Optimization Research Articles

Optimization model for mass casualty management system using qos-aware routing protocol and casualty triage prediction

Abstract Purpose Unexpected events, whether man-made or natural, cause significant distress and disorder. The number and magnitude of disasters and catastrophes have been rising globally in recent decades, according to historical data. Continued real-time monitoring of mass casualty along with the arrangement of needed medical resources to handle the mass casualty is required to reduce the mortality and morbidity rates. Methods Electronic tag-based casualty monitoring and machine learning-based Decision Support Systems are emerging as a more effective and proactive solution that provides continuous remote monitoring of patients. A novel framework based on Body-to-Body Network, Prediction model, and Genetic Algorithm-based medical resource optimization is proposed for the continuous monitoring of the mass casualty and medical resource allocation at the incident. The aim of this work is to give priority to the handling of critical casualties. Firstly, a Quality of Service and load-sensitive routing protocol for transmitting mass casualties' physiological parameters across a wireless network is proposed, with the critical casualty being emphasized. Secondly, the clinical seriousness degree of the mass casualty is predicted using Backpropagation Artificial Neural Network. Finally, an optimization model using a Genetic algorithm and queuing theory is proposed to find the required optimal number of medical resources to handle critical and non-critical casualties separately. Also, the proposed optimization model considers the predicted clinical level transition rates of the mass casualty. Results The performance and accuracy of the proposed framework are evaluated using the MIMIC-II dataset. The outcome demonstrates that the framework emphasizes critical casualty management. Furthermore, the framework allocates an adequate number of servers by incorporating the proposed routing protocol in comparison to the AODV protocol. Conclusion The inclusion of a prediction model in the framework aids in allocating an adequate number of servers by considering the predicted clinical deteriorating transition rates of casualties at a mass casualty incident. In terms of the estimated length of the casualty at the incident, the results suggest that incorporating a medical resource optimization model outperforms the non-optimal option.

Revolutionizing MANET Route Discovery with INTSM: An Innovative Load Balancing Approach

Communication challenges in ad hoc networks arise due to the mobility of nodes, causing frequent changes in connections and locations. Maintaining network equilibrium to prevent node overload and underutilization is crucial. However, imposing static behaviors on nodes to improve performance can lead to delays, especially in core nodes. Addressing these issues, this research proposes the Intermediate Node Traffic Sharing Model (INTSM) for ad hoc networks. INTSM prioritizes congestion control and load balancing during route discovery, aiming to optimize network resource utilization and traffic distribution, thereby reducing packet delays. The model employs dynamic traffic sharing algorithms that consider real-time network conditions, enabling nodes to adjust their behaviour adaptively. This approach minimizes congestion by distributing traffic loads more evenly across the network, preventing bottlenecks at central nodes. Additionally, INTSM incorporates predictive analysis to foresee potential congestion points and reroute traffic proactively, enhancing overall network stability and performance. Extensive simulations demonstrate that INTSM significantly reduces average packet delay and improves throughput compared to traditional routing protocols. The results highlight the model's efficacy in diverse scenarios, including high mobility and varying traffic loads, proving its robustness and scalability. The primary objective of this study is to enhance navigation and equilibrium mechanisms to improve the performance of ad hoc networks, contributing to more reliable and efficient wireless communication systems. The findings of this research have significant implications for the design of future ad hoc networks, particularly in applications requiring high reliability and quick adaptation to changing network conditions, such as disaster recovery, military operations, and mobile sensor networks. By addressing the critical challenges of congestion control and load balancing, INTSM offers a promising solution to enhance the resilience and efficiency of ad hoc networks.

Security Control for a Fuzzy System under Dynamic Protocols and Cyber-Attacks with Engineering Applications

The objective of this study is to design a security control for ensuring the stability of systems, maintaining their state within bounded limits and securing operations. Thus, we enhance the reliability and resilience in control systems for critical infrastructure such as manufacturing, network bandwidth constraints, power grids, and transportation amid increasing cyber-threats. These systems operate as singularly perturbed structures with variables changing at different time scales, leading to complexities such as stiffness and parasitic parameters. To manage these complexities, we integrate type-2 fuzzy logic with Markov jumps in dynamic event-triggered protocols. These protocols handle communications, optimizing network resources and improving security by adjusting triggering thresholds in real-time based on system operational states. Incorporating fractional calculus into control algorithms enhances the modeling of memory properties in physical systems. Numerical studies validate the effectiveness of our proposal, demonstrating a 20% reduction in network load and enhanced stochastic stability under varying conditions and cyber-threats. This innovative proposal enables real-time adaptation to changing conditions and robust handling of uncertainties, setting it apart from traditional control strategies by offering a higher level of reliability and resilience. Our methodology shows potential for broader application in improving critical infrastructure systems.

Enhancing 5G Core Network Performance through Optimal Network Fragmentation and Resource Allocation

Enhancing 5G Core Network Performance through Optimal Network Fragmentation and Resource Allocation

AdaBoost‐powered cloud of things framework for low‐latency, energy‐efficient chronic kidney disease prediction

AbstractThe United Nations' sustainable development agenda has set an ambitious goal of reducing premature mortality from non‐communicable diseases by 33% by 2030. Among these diseases, chronic kidney disease (CKD) is a significant contributor to both morbidity and mortality. Integrating the Internet of Things (IoT) and cloud computing in healthcare has gained momentum, particularly in remote patient monitoring. However, it is essential to acknowledge that cloud computing has limitations, particularly in handling vast volumes of Big Data, mainly due to scalability and latency concerns. This article proposes a novel framework, AdaBoostCoTCKD, to mitigate latency issues, minimize response times, reduce power consumption, and optimize network resources for predicting CKD. The framework leverages the synergy between the AdaBoost machine learning technique and fog computing paradigms to enhance the precision and efficiency of CKD prediction methods. In addition, it introduces an auxiliary cloud‐based database, enriching the pool of future insights and facilitating prospective database infrastructure expansions. This augmentation is expected to impact predictive accuracy positively. We conducted comprehensive experiments to demonstrate the effectiveness of our approach. Our model achieved an impressive training accuracy of 99.928% and testing accuracy of 99.975%, while the fog environment reduced latency by 31% and energy consumption by 75% compared to traditional cloud‐based solutions. Our proposed system enables early CKD detection and offers advantages over cloud‐only solutions, providing a robust and efficient platform for healthcare IoT applications with significant clinical value. These promising results underscore the potential of combining fog computing and the AdaBoost machine learning technique to advance healthcare by addressing latency, response time, power consumption, and network resource optimization challenges in CKD prediction.

Towards the automatic network resource management of OPC UA in 5G private networks

In the realm of Network Function Virtualization (NFV) and Software Defined Networking (SDN), the shift of network functions to the edge presents both challenges and opportunities across various industries. This paper investigates the utilization of Network Resource Management (NRM) within the Third Generation Partnership Project (3GPP) Service Enabler Architecture Layer (SEAL) framework for network operations in different sectors. Leveraging the Open Platform Communications (OPC) Unified Architecture (UA) Publish Subscribe (PubSub) pattern, we enhance edge networking by automating NRM within SEAL, introducing custom Message Queuing Telemetry Transport (MQTT) brokers and a mapping node. To validate our approach, we establish a SEAL NRM test server and implement a translator mechanism for interoperability. We address the integration of non-adaptive applications into the NRM domain and demonstrate our proposal with a practical Robot Operating System (ROS) Digital Twin (DT) application, showcasing dynamic NRM’s potential for optimizing network resources in scenarios like 3D printing.

Optimizing 6G Network Slicing with the EvoNetSlice Model for Dynamic Resource Allocation and Real-Time QoS Management

This research paper focuses on thoroughly examining the challenges in 6G network slicing. To develop, evaluate performance characteristics for on-demand reallocation and instantaneously changeable QoS EvoNetSlice model. The study employs integrated evolutionary algorithms with artificial intelligence-enabled data analytics and multi-objective optimization to optimize network resources usage under minimum end-to-end delay, high transmission rates and optimal background data management. Firstly, the network resource allocation individuals should be based on the network traffic data, QoD (quality of demand) value for some applications and users’ behaviors. The performance degradation detection and quality of service (QoS) adaptation mechanism combined with a multi-layer objective fitness function for achieving good balance in conflict between conflicting objectives. Results indicate that EvoNetSlice improves the general efficiency of a particular network, adapts according to ever shifting requirements for QoS at any time and provides crucial statistics-focused data on network management. The importance of this work lies in developing the future 6G network’s technology. W the key issues, including resource optimization and real-time adaptation required to support modern 6G services, are considered by EvoNetSlice. Such an exploration is an essential element in developing flexible 6G systems that will define next-generation wireless communication.

Adaptive Speech Coding Method Based on Singular Value Decomposition and Grey Wolf Optimization for Arabic Language

Speech coding plays a crucial role in maintaining speech quality while optimizing network resources and expediting transmission, as well as facilitating the storage of speech data. In this paper, an adaptive method for speech coding using singular value decomposition (SVD), grey wolf optimization (GWO), and run-length encoding (RLE) was proposed. The proposed method streamlines the speech matrix through preprocessing, converting it into short intervals. Subsequently, each interval undergoes decomposition using SVD, followed by optimization of compression quality using GWO. Finally, RLE is employed as the last step to increase space-saving. The developed method was conducted on two datasets: Quran and LibriSpeech using PSNR, PSEQ, and MOS tests. The results demonstrate promising outcomes, achieving space-saving up to 89.80, 84.04, 74.76, 67.24, and 59.52, respectively, for different values of quality (10, 20, 30, 40, and 50). GWO was used to optimize the quality factor which varies in each block, further increasing the space-saving up to 85.77. The average value of PSNR was equal to 21.3, MOS = 4.71, and PSEQ was equal to 3.95. Lastly, the RLE method effectively reduced the size of speech matrices, yielding a highly satisfactory space saving of up to 90.77, while maintaining excellent speech quality.

Co-design of distributed dynamic event-triggered scheme and extended dissipative consensus control for singular Markov jumping multi-agent systems under periodic Denial-of-Service jamming attacks

This paper introduces a distributed dynamic event-triggered (DDET) controller meticulously crafted to achieve resilient consensus control in singular Markov jumping multi-agent systems (SMJMAs) even in the face of periodic Denial-of-Service (DoS) jamming attacks. With a dual focus on optimizing network resource utilization and fortifying against cyber threats, we propose a distributed dynamic event-triggered scheme (DDETS) grounded in the principles of sampled data consensus. In the presence of cyber attacks, we demonstrate the transformation of the original consensus control challenge within the singular multi-agent system into a robust stability control problem, centering around a singular switching error system governed by the DoS attack signal. Acknowledging the disturbances that typify real-world scenarios, this paper establishes criteria for attaining exponential stability and stochastic admissibility within the singular Markov jumping switching error system under the extended dissipative performance index. Furthermore, we present a hybrid design for the consensus controller and DDETS, delineated through a set of solvable linear matrix inequalities (LMIs). Finally, a comprehensive array of numerical examples meticulously illustrates the compelling effectiveness and unequivocal superiority of our proposed methodology.

Decentralized Machine Learning for Dynamic Resource Optimization in Wireless Networks using Reinforcement Learning

Efficient allocation of resources is crucial for optimizing wireless networks that face constraints in bandwidth, power, and spectrum. This paper proposes a decentralized reinforcement learning (RL) model that departs from traditional centralized paradigms to revolutionize resource optimization. The proposed model empowers individual wireless devices with autonomous decision-making capabilities, enhancing adaptability and scalability by leveraging Deep Q-Network (DQN) and Proximal Policy Optimization (PPO). The innovative integration of memory mechanisms facilitates learning from past experiences, addressing the dynamic nature of wireless environments. This decentralized RL model offers practical implications for improved efficiency, adaptability, and reliability in wireless network resource optimization. By transforming individual devices into collaborative decision-makers, our proposed model contributes to a resilient and responsive wireless communication infrastructure. The specific contributions of this paper include the pioneering use of DQN and PPO algorithms within a multi-agent system, offering a groundbreaking solution for dynamic resource optimization in wireless networks.

An expert system for hybrid edge to cloud computational offloading in heterogeneous MEC–MCC environments

Mobile Cloud Computing (MCC) integrates cloud computing into the mobile environment, effectively tackling performance, environmental, and security constraints. However, the proliferation of clouds and applications introduces complexities in offloading decisions. Mobile Edge Computing (MEC) has emerged as a solution to bolster MCC performance, capitalizing on the proximity to edge devices. However, optimizing computation offloading remains paramount for heterogeneous smart (mobile phones, wearables, and IoT) devices, necessitating efficient utilization of network resources and computational offloading. To address these challenges, this paper introduces NSANNOM (Network and Device Resources Utilization through Smart ANN-based Offloading Mechanism), an expert system designed for optimal computational offloading decision-making and efficient network resource allocation mechanisms. NSANNOM employs Artificial Neural Networks (ANN) for precise decision-making by validating real-world datasets, underscoring ANN’s superiority over existing algorithms, and showcasing enhanced energy savings, cost efficiency, and latency response. Experimental evaluations demonstrate that the proposed ANN model for the offloading decision-making algorithm achieves a training accuracy of 97% and a validation accuracy of 99%. The system consumes minimal energy (10 MJ) for task scheduling and exhibits remarkable accuracy in resource utilization across multiple tasks (10–50) ranging in size (from 1 to 16 GB). Additionally, it minimizes time delays (in milliseconds) during the offloading process.

Security synchronization problem for stochastic complex networks via event-triggered impulsive control with actuation delays

This study focused on the security synchronization problem for stochastic complex networks (SCNs) via event-triggered impulsive control (ETIC) with actuation delays. Firstly, incorporating the network topology and the Lyapunov function theory, a novel event-triggered mechanism (ETM) is devised, which accounts for actuation delays; Secondly, an ETM-based quantizer is introduced to optimize network resources, meantime, building upon this foundation, the Lipschitz–Razumikhin method is integrated with graph theory to establish a positive lower bound on the minimum event interval time, thereby preventing Zeno behavior; Additionally, some sufficient conditions are obtained to achieve global asymptotic synchronization of SCNs under quantization and deception attacks; Finally, a Chua’s circuit numerical simulation is employed to demonstrate the effectiveness of our theoretical findings.

Advancing 6G-IoT networks: Willow catkin packet transmission scheduling with AI and bayesian game-theoretic approach-based resource allocation.

The rapid expansion of mobile broadband networks and the proliferation of Internet of Things (IoT) applications have substantially increased data transmission and processing demands. However, the application domains of IoT-enabled models often face resource limitations, requiring rapid responses, low latency, and large bandwidth, surpassing their inherent capabilities. To address these challenges, we propose a fishnet approach-based packet scheduling and resource allocation system, termed Fishnet-6G, to optimize network resource allocation in the proposed 6G networks. Initially, we constructed a Sierpinski Triangle-based network in a 6G-IoT environment, enhancing device connectivity. We utilize the Quantum Density Peak Clustering (QDPC) algorithm to perform clustering for IoT devices, establishing Cluster Head (CH) and Substitute CH (SUB CH) based on actual metrics. Furthermore, traffic prediction is achieved through two processes, grouping, and fair queue status, using the Improved Deep Deterministic Policy Gradient (IMPDDPG) algorithm with a variable sampling rate, resulting in well-organized packet scheduling. Subsequently, we perform optimal packet scheduling by employing the Willow Catkin Optimization (WCO) algorithm, and the scheduled packets are managed within a Fishing Net Topology to reduce energy consumption and system complexity. Finally, we allocate the scheduled packets to the desired resource blocks using the Bayesian Game-Theoretic Approach (BGTA). The proposed approach is implemented using Network Simulator-3.26, and the performance of the Fishnet-6G model is evaluated based on time, transmission rate, energy efficiency, average throughput, latency, and Packet loss rate. Numerical analysis demonstrates that Fishnet-6G outperforms existing approaches across these metrics, showcasing its effectiveness in addressing the challenges of 6G-IoT networks.

Mobile association scheme based on auction algorithm in heterogeneous wireless networks

With the rapid development of mobile communications, the dense deployment of small base stations (SBSs) in heterogeneous wireless networks (HW-Nets) has met the explosive growth in demand from terminals. However, due to the irregular deployment of SBSs, users frequently handover base stations in pursuit of high-quality services, which inevitably leads to load imbalance among SBSs. To alleviate load imbalance in HW-Nets and improve the efficiency of seamless handover, this paper proposes a distributed auction algorithm scheme based on dual active protocol stack (DAPS), which transforms the wireless access selection of terminals into the terminal-base station association process during movement to globally optimize network resource utilization. At the same time, this paper constructs a terminal-base station association graph and uses auction algorithms to solve the maximum weighted matching of the graph. The DAPS-based scheme can effectively avoid the problem of excessively long outage time caused by handovering outages. Simulation results show that compared with general distributed algorithms, the distributed auction algorithm based on DAPS can effectively balance the load of base stations and reduce the mobile outage latency by 59% .

Event‐based privacy‐preserving security consensus of multi‐agent systems with encryption–decryption mechanism

AbstractThe article concentrates on exploring the issue of privacy‐preserving sliding mode consensus of multi‐agent systems (MASs) with disturbance. An encryption and decryption algorithm has been proposed to address data security and privacy issues during data transmission. To optimize network resource allocation, a dynamic event‐triggering mechanism has been introduced, which reduces the number of encrypted data while saving the computation cost. The consensus performance based on the sliding mode control strategy is achieved when the reachability of the slide‐mode surface is guaranteed, and then the slide‐mode controller is developed. Finally, an empirical demonstration through a numerical example validates the efficacy of the proposed strategy.

Efficient network management and security in 5G enabled internet of things using deep learning algorithms

The rise of fifth generation (5G) networks and the proliferation of internet-of-things (IoT) devices have created new opportunities for innovation and increased connectivity. However, this growth has also brought forth several challenges related to network management and security. Based on the review of literature it has been identified that majority of existing research work are limited to either addressing the network management issue or security concerns. In this paper, the proposed work has presented an integrated framework to address both network management and security concerns in 5G internet-of-things (IoT) network using a deep learning algorithm. Firstly, a joint approach of attention mechanism and long short-term memory (LSTM) model is proposed to forecast network traffic and optimization of network resources in a, service-based and user-oriented manner. The second contribution is development of reliable network attack detection system using autoencoder mechanism. Finally, a contextual model of 5G-IoT is discussed to demonstrate the scope of the proposed models quantifying the network behavior to drive predictive decision making in network resources and attack detection with performance guarantees. The experiments are conducted with respect to various statistical error analysis and other performance indicators to assess prediction capability of both traffic forecasting and attack detection model.

Low-latency partial resource offloading in cloud-edge elastic optical networks

In the context of the rapid deployment of IoT, 5G, and cloud computing, numerous emerging applications demand efficient networked computing capacity for task offloading from mobile and IoT users. This paper focuses on the optimization of network resource allocation and reduction of end-to-end (E2E) latency through the strategic decision of whether and where to offload user requests in a cloud-edge elastic optical network (CE-EON). To address this problem, we first formulate the problem into an integer linear programming (ILP) model as an initial solution. Additionally, we introduce several heuristic approaches that leverage the concept of partial resource offloading, specifically based on proportional segmentation (PRO_PS), partial resource offloading based on average segmentation (PRO_AS), all resource offloading (ARO), and all local processing (ALP). Furthermore, we implement a collaborative cloud-edge (CCE) offloading approach as a baseline for comparison. Our results demonstrate that the PRO_PS approach closely approximates the optimal solutions obtained from the ILP model in static scenarios. Moreover, the PRO_PS approach achieves the lowest E2E latency, blocking probability, and optimized network resource allocation in dynamic scenarios. This highlights the effectiveness of the proposed approach in improving system performance and addressing the challenges of CE-EONs.

How do inventors overcome the relational inertia after inter‐firm mobility?

This study aims to explore strategies for overcoming relational inertia and enhancing their creativity after inter‐firm mobility of inventors. The research sample comprises 591 mobile inventors identified using a patent data‐based model from Chinese communication technology companies. Employing the framework of dynamic network analysis, this study examines the impact of network closure at different time on overcoming relational inertia and investigates the influence of knowledge structure. The findings suggest that a lower level of past and current network closure is more conducive to knowledge sharing and technological innovation after inter‐firm mobility of inventors. Furthermore, mobile inventors with broad and in‐depth knowledge structures demonstrate significantly improved knowledge sharing and technological innovation, effectively mitigating relational inertia. These conclusions offer a systematic research perspective and comprehensive theoretical guidance, emphasizing the importance of optimizing network resources and knowledge structures to help inventors overcome relational inertia after inter‐firm mobility.

Intelligent data routing strategy based on federated deep reinforcement learning for IOT-enabled wireless sensor networks

The high-speed data routing in the Internet of Things (IoT)-enabled Wireless Sensor Networks (WSNs) is vital in improving network performance. Existing routing protocols face difficulties dealing with frequent node relocations, energy efficiency optimization, scalability, and dynamic network environments. The inadequacy of conventional routing algorithms in handling the issues provided by frequent node relocations, energy efficiency optimization, scalability, and dynamic network settings in IoT-enabled WSNs is identified as the problem. Traditional protocols are inflexible in the face of node relocations, whereas machine learning-based techniques frequently miss energy efficiency factors. Reinforcement learning-based techniques presume static topologies and the potential of federated learning in WSN routing is largely untapped. The goal is to provide adaptive and energy-efficient routing while considering message overhead, temporal complexity, data sum rate, communication delay, and scalability. The research adopts federated deep reinforcement learning (FDRL) to address these limitations for routing the high-speed data packets in IoT-enabled WSNs. The proposed research technique uses FDRL, which enables distributed decision-making and adaptive routing in dynamic network situations. The fundamental instant data load is separated into cluster pairs in the proposed technique, each with one strong and one weak sensor node. This split enables optimal network load balancing and resource use. The FDRL architecture is used to disperse the learning process across numerous nodes or access points, allowing for localized decision-making while taking advantage of global coordination. The simulations are conducted to validate the effectiveness of the FDRL Routing in terms of packet loss, latency, energy efficiency, and scalability. The results show that the proposed federated DRL-based intelligent data routing approach outperforms traditional protocols and other current routing strategies.

Dynamic dissipative control for fuzzy distributed parameter cyber physical system under input quantization and DoS attack.

This article explores the dissipative control for a class of nonlinear DP-CPS (distributed parameter cyber physical system) within a finite-time interval. By utilizing a Takagi-Sugeno (T-S) fuzzy model to represent the system's nonlinear aspects, the studied system is formulated as a class of fuzzy parabolic partial differential equation (PDE). In order to optimize network resources, both the system state and input signal are subjected to quantization using dynamic quantizers. Subsequently, a dynamic state control strategy is proposed, taking into account potential DoS attack. The finite-time boundedness of the fuzzy parabolic PDE is analyzed, with respect to the influence of quantization, through the construction of an appropriate Lyapunov functional. The article then presents the conditions for finite-time dissipative control design, alongside the adjustment parameters for the dynamic quantizers within the fuzzy closed-loop system. Furthermore, the decoupling of interlinked nonlinear terms in the control design conditions is achieved by using an arbitrary matrix. Finally, an example is provided and the simulation results indicate the effectiveness of the dissipative control method proposed.