Changing Network Conditions Research Articles

Artificial Intelligence Driven Power Optimization in IOT-Enabled Wireless Sensor Networks

The widespread use of Wireless Sensor Networks (WSN) in Internet of Things (IoT) causes energy efficiency issues. This paper proposes an AI-based solution to this problem. The propose an AI-Driven Power Optimization framework for IoT-enabled WSN using Deep Q-Network (DQN) and Dynamic Voltage and Frequency Scaling (DVFS). These techniques can adapt to changing network conditions and reduce power consumption when used together. Sensor nodes provide environmental parameters, battery status, and network behavior data to the AI-driven framework DQN is implemented after data preprocessing to learn and make power management decisions using reinforcement learning. Neural network-driven agent operates in a state and action space. It optimizes energy use with rewards. Real-time hardware power adjustment is done using DVFS. DVFS precise control and AI-driven decision-making create a comprehensive power optimization strategy. AI adapts to new challenges and optimizes network lifespan by improving its power management policies. Experimental implementations of the proposed framework show significant energy savings, network lifespan extension, and QoS improvements. AI-Driven Power Optimization in IoT-enabled WSN is proven effective and flexible. This study shows the potential of AI, specifically DQN and DVFS, in IoT-enabled WSN. This AI-Driven Power Optimization framework addresses energy efficiency and improves IoT sensor networks. AI improves power optimization in IoT-enabled WSN making IoT deployments more sustainable and resilient.

A hybrid MGO-JAYA based clustered routing for FANETs

In recent years, Flying Ad-hoc Networks (FANETs) have gained significant attention among researchers due to the widespread applications and increasing popularity of Unmanned Aerial Vehicles (UAVs). As technology advances and more research is undertaken, FANETs are expected to become a vital aspect of modern times, allowing for more effective and creative applications in different domains. However, FANETs also face several challenges, including high mobility, dynamic topology, energy constraints, and communication reliability. Addressing these challenges is essential to unlock the full potential of FANETs and to ensure reliable and timely delivery of data. In this paper, we propose HMGOC, a novel clustered routing model for FANETs, utilizing a hybrid approach that combines the Mountain Gazelle Optimizer (MGO) and Jaya Algorithms. The dynamic flying behavior of UAVs demands an adaptive and efficient clustering strategy to maintain network stability and ensure robust and reliable communication among UAVs. In this context, MGO, one of the most recent swarm-based optimization methods, is enhanced and employed for FANET clustering process. Also, we design a routing mechanism based on conditional Bayes' theorem which adapts to changing network conditions, reduces packet losses, and ensures timely data delivery. HMGOC offers several advantages over other competitive techniques, including improved load balancing, minimized energy consumption and latency, and enhanced network throughput and lifespan. The simulation results demonstrate that the HMGOC technique beats the existing methods in terms of enhanced cluster stability and lifetime, increased packet deliverability, energy efficiency, reduced latency, and minimized overhead.

Dynamic Resource Allocation of Reinforcement Learning Based on Neural Networks in Software Defined Networks

With the rapid development of Software Defined Networking (SDN) technology, how to efficiently and flexibly manage and allocate network resources has become a key challenge. This article proposes the DDPG (Deep Deterministic Policy Gradient) algorithm method, aiming to dynamically optimize resource allocation in SDN. The DDPG algorithm can respond in real-time to changes in network status, automatically adjust resource allocation strategies, and thereby improve network performance and service quality. This study comprehensively evaluated the dynamic resource allocation ability of neural network-based DDPG reinforcement learning algorithm in SDN through four experiments. In the baseline comparison experiment, the network throughput of DDPG reached 95 Mbps. Under different network loads, DDPG still maintains a throughput of 95 Mbps under high load conditions. In the fault recovery capability testing experiment, the recovery time of DDPG is 30 seconds. In the final real-time adjustment capability evaluation, DDPG demonstrated a fast response time of 1.2 seconds, as well as a throughput of up to 80 Mbps and a resource utilization rate of 95% after adjustment. From the experimental data conclusions, it can be seen that the DDPG algorithm provides superior performance and flexible resource management capabilities in SDN environments.

SFC active reconfiguration based on user mobility and resource demand prediction in dynamic IoT-MEC networks.

To achieve secure, reliable, and scalable traffic delivery, request streams in mobile Internet of Things (IoT) networks supporting Multi-access Edge Computing (MEC) typically need to pass through a service function chain (SFC) consisting of an ordered series of Virtual Network Functions (VNFs), and then arrive at the target application in the MEC for processing. The high mobility of users and the real-time variability of network traffic in IoT-MEC networks lead to constant changes in the network state, which results in a mismatch between the performance requirements of the currently deployed SFCs and the allocated resources. Meanwhile, there are usually multiple instances of the same VNF in the network, and proactively reconfiguring the deployed SFCs based on the network state changes to ensure high quality of service in the network is a great challenge. In this paper, we study the SFC Reconfiguration Strategy (SFC-RS) based on user mobility and resource demand prediction in IoT MEC networks, aiming to minimize the end-to-end delay and reconfiguration cost of SFCs. First, we model SFC-RS as Integer Linear Programming (ILP). Then, a user trajectory prediction model based on codec movement with attention mechanism and a VNF resource demand prediction model based on the Long Short-Term Memory (LSTM) network are designed to accurately predict user trajectories and node computational and storage resources, respectively. Based on the prediction results, a Prediction-based SFV Active Reconfiguration (PSAR) algorithm is proposed to achieve seamless SFC migration and routing update before the user experience quality degrades, ensuring network consistency and high quality service. Simulation results show that PSAR provides 51.28%, 28.60%, 21.75%, and 16.80% performance improvement over the existing TSRFCM, DDQ, OSA, and DPSM algorithms in terms of end-to-end delay reduction, and 33.32%, 18.94%, 67.42%, and 60.61% performance optimization in terms of reconfiguration cost reduction.

Integrating Smart Computility for Subflow Orchestration in Remote Virtual Services.

The burgeoning domain of the metaverse has sparked significant interest from a diverse array of industries, including healthcare services. However, the metaverse and its associated applications present various challenges. This could strain the comprehensive capacity of existing networks. In this paper, we have investigated vital network demands of healthcare services within the metaverse. First, to meet the increasing demands of the metaverse, there is a need for enhanced bandwidth, reduced latency, and improved packet loss control. Furthermore, the transmission mechanism should exhibit flexibility to automatically adapt to the diverse hybrid needs of different healthcare services. Considering the aforementioned challenges, a transmission paradigm tailored for the metaverse-based healthcare services is developed. Multipath transmission has the potential to effectively enhance network performance in multiple aspects. Significantly, we devise an orchestration framework to reconcile edge-side subflow management with diverse healthcare applications. Using machine learning techniques, the framework can produce near-optimal subflow adjustment strategies for client nodes and miscellaneous services. Comprehensive experiments are performed on applications with diverse requirements to validate the adaptability of the framework to the application needs. The experimental results demonstrate that the proposed method enables the network to autonomously adapt to changing network conditions and service requirements. This includes applications' preferences for high throughput, low delay, and high stability. Moreover, the test results show that the proposed approach can notably decrease the occurrences of network quality falling below the minimum requirement. Given its adaptability and impact on network quality, this work paves the way for future metaverse-based healthcare services.

ОГЛЯД МЕХАНІЗМІВ ROOT CAUSE АНАЛІЗУ ТЕЛЕКОМУНІКАЦІЙНИХ МЕРЕЖ

Telecommunication networks are an integral part of modern information society, providing data transmission and communication among millions of users worldwide. The complexity and scale of these networks are constantly growing, which increases the demands on their reliability and stability. One of the key tasks in ensuring the efficient operation of telecommunication networks is the identification and elimination of the root causes of failures and anomalies that can significantly affect the quality of service for end users. Root cause analysis (RCA) is a powerful tool for identifying the primary causes of problems, preventing their recurrence, and enhancing the overall reliability of networks. The article provides a detailed review of contemporary methods and tools of RCA used in telecommunication networks. In particular, methods such as fishbone diagram analysis, the «five whys» method, fault tree analysis, and the application of machine learning for big data analysis are considered. Each of these approaches has its advantages and disadvantages, and their comprehensive use allows for higher accuracy and efficiency in identifying the root causes of problems. Practical examples demonstrate the effectiveness of applying various RCA methods to solve specific problems in telecommunication networks. For instance, the use of fishbone diagram analysis and the «five whys» method allows for the identification of the primary causes of network delays and equipment issues at base stations. The implementation of failure prediction systems based on machine learning significantly enhances network reliability by allowing for the early detection of potential problems and the necessary technical measures. The prospects for the development of RCA in telecommunication networks are outlined. The primary focus is on the development of adaptive and self-learning systems capable of dynamically responding to changing network conditions. The use of more complex artificial intelligence algorithms and tools for predicting potential problems will significantly improve the effectiveness of RCA and ensure high-quality telecommunication services.

Machine Learning Driven Threat Identification to Enhance FANET Security using Genetic Algorithm

Emerging as a highly promising technology, Flying Ad-hoc Networks represent self-organizing networks of Unmanned Aerial Vehicles (UAVs), garnering attention for their diverse applications spanning environmental monitoring, disaster management, precision agriculture, surveillance, and military operations. However, these networks face challenges to various security threats, including malicious node detection due to their deployment in dynamic environments. To address this issue, we present an improved novel security solution, Machine Learning-based Threat Identification for FANET using a Genetic Algorithm (ML-TIFGA) in this paper. The research includes the detection of abnormal behavior nodes using a basic genetic algorithm and dynamically adapting the changing network conditions by utilizing a reputation system. To enhance our security solution ML-TIFGA, we evaluated two key factors: cooperation and trustworthiness, which act as genetic elements within the chromosome of the flying node in our genetic population. Further, a mechanism is incorporated to reconfigure the trust, addressing the challenge of dynamically extracting threats through the updated weighted reputation system while considering past behavior monitoring. Significant improvements were found in the experimental results using actual sample values from the NSL-KDD dataset, which produced a remarkable 99.829% classification accuracy. Additionally, threat identification rates reached 98.36% for training and 98.86% for testing samples, with a remarkable improvement of 99.3% in network reliability through ML-TIFGA. When benchmarked against state-of-the-art approaches, performance metrics such as delay, throughput, and data delivery rate exhibited notable enhancements of 24.65%, 29.16%, and 31.73%, respectively.

Survey of the Influence of Routing Protocols to Network Performance Enhancement

Routing protocols play a crucial role in the operation of computer networks by determining the optimal paths for data transmission. The selection of an appropriate routing protocol can significantly impact network performance, including factors such as latency, throughput, reliability, and scalability. This survey aims to provide a comprehensive analysis of the influence of various routing protocols on network performance enhancement. The survey begins by presenting an overview of common routing protocols with their key characteristics. Subsequently, it explores the impact of these protocols on network performance metrics, focusing on their ability to adapt to changing network conditions, mitigate congestion, and ensure efficient resource utilization. Through a systematic review of literature, empirical studies, and real-world implementations, this survey aims to provide network administrators, researchers, and practitioners with valuable insights into selecting and optimizing routing protocols for specific network environments. Additionally, it identifies areas for further research and development to continue advancing the field of routing protocols and network performance enhancement. Keywords: Optimizing Routing Protocols, Performance Metrics, Reliability and Scalability, Resource Utilization, Wireless Local Area Networks, Accurate Routing Tables Juliana I. Consul & Bunakiye R. Japheth (2023): Survey of the Influence of Routing Protocols to Network Performance Enhancement. Journal of Advances in Mathematical & Computational Science. Vol. 11, No. 4. Pp 13-28 Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V11N4P2

Deep reinforcement learning‐based resource allocation in multi‐access edge computing

SummaryNetwork architects and engineers face challenges in meeting the increasing complexity and low‐latency requirements of various services. To tackle these challenges, multi‐access edge computing (MEC) has emerged as a solution, bringing computation and storage resources closer to the network's edge. This proximity enables low‐latency data access, reduces network congestion, and improves quality of service. Effective resource allocation is crucial for leveraging MEC capabilities and overcoming limitations. However, traditional approaches lack intelligence and adaptability. This study explores the use of deep reinforcement learning (DRL) as a technique to enhance resource allocation in MEC. DRL has gained significant attention due to its ability to adapt to changing network conditions and handle complex and dynamic environments more effectively than traditional methods. The study presents the results of applying DRL for efficient and dynamic resource allocation in MEC Computing, optimizing allocation decisions based on real‐time environment and user demands. By providing an overview of the current research on resource allocation in MEC using DRL, including components, algorithms, and the performance metrics of various DRL‐based schemes, this review article demonstrates the superiority of DRL‐based resource allocation schemes over traditional methods in diverse MEC conditions. The findings highlight the potential of DRL‐based approaches in addressing challenges associated with resource allocation in MEC.

Dynamic adaptation of contention window boundaries using deep Q networks in UAV swarms

In flying ad hoc networks (FANET), medium access layer (MAC) protocols play an essential role in ensuring better network performance. The effective utilization of network resources and providing access fairness are key research issues in this area, and the contention window size directly influences these factors. This paper mainly focuses on addressing the upper and lower boundaries of the contention window in Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) with a binary exponential back-off algorithm. Existing solutions include static and dynamic window adjustment techniques only to update the upper boundary of the contention window size. However, the static window adjustment technique is unsuitable for changing network conditions, while the dynamic window adjustment technique may need to be more efficient in dense network environments. The proposed solution uses a Deep Q-learning algorithm(DQN) framework to adjust the contention window adaptively based on the reward function, resulting in efficient and dynamic adaptation to changing network conditions among unmanned aerial vehicles (UAV). The proposed methodology is evaluated through simulations, and performance is measured using channel utilization, efficiency, delay, and collision rate. The simulation findings show that, in comparison to generic MAC protocols, the proposed algorithm is 5% more efficient in throughput and incurs a four-fold reduction in delay.

Efficient Internet-of-Things Cyberattack Depletion Using Blockchain-Enabled Software-Defined Networking and 6G Network Technology.

Low-speed internet can negatively impact incident response by causing delayed detection, ineffective response, poor collaboration, inaccurate analysis, and increased risk. Slow internet speeds can delay the receipt and analysis of data, making it difficult for security teams to access the relevant information and take action, leading to a fragmented and inadequate response. All of these factors can increase the risk of data breaches and other security incidents and their impact on IoT-enabled communication. This study combines virtual network function (VNF) technology with software -defined networking (SDN) called virtual network function software-defined networking (VNFSDN). The adoption of the VNFSDN approach has the potential to enhance network security and efficiency while reducing the risk of cyberattacks. This approach supports IoT devices that can analyze large volumes of data in real time. The proposed VNFSDN can dynamically adapt to changing security requirements and network conditions for IoT devices. VNFSDN uses threat filtration and threat-capturing and decision-driven algorithms to minimize cyber risks for IoT devices and enhance network performance. Additionally, the integrity of IoT devices is safeguarded by addressing the three risk categories of data manipulation, insertion, and deletion. Furthermore, the prioritized delegated proof of stake (PDPoS) consensus variant is integrated with VNFSDN to combat attacks. This variant addresses the scalability issue of blockchain technology by providing a safe and adaptable environment for IoT devices that can quickly be scaled up and down to pull together the changing demands of the organization, allowing IoT devices to efficiently utilize resources. The PDPoS variant provides flexibility to IoT devices to proactively respond to potential security threats, preventing or mitigating the impact of cyberattacks. The proposed VNFSDN dynamically adapts to the changing security requirements and network conditions, improving network resiliency and enabling proactive threat detection. Finally, we compare the proposed VNFSDN to existing state-of-the-art approaches. According to the results, the proposed VNFSDN has a 0.08 ms minimum response time, a 2% packet loss rate, 99.5% network availability, a 99.36% threat detection rate, and a 99.77% detection accuracy with 1% malicious nodes.

Resource allocation for fog computing based on software-defined networks

<p><span lang="EN-US">With the emergence of cloud computing as a processing backbone for internet of thing (IoT), fog computing has been proposed as a solution for delay-sensitive applications. According to fog computing, this is done by placing computing servers near IoT. IoT networks are inherently very dynamic, and their topology and resources may be changed drastically in a short period. So, using the traditional networking paradigm to build their communication backbone, may lower network performance and higher network configuration convergence latency. So, it seems to be more beneficial to employ a software-defined network paradigm to implement their communication network. In software-defined networking (SDN), separating the network’s control and data forwarding plane makes it possible to manage the network in a centralized way. Managing a network using a centralized controller can make it more flexible and agile in response to any possible network topology and state changes. This paper presents a software-defined fog platform to host real-time applications in IoT. The effectiveness of the mechanism has been evaluated by conducting a series of simulations. The results of the simulations show that the proposed mechanism is able to find near to optimal solutions in a very lower execution time compared to the brute force method.</span></p>

Influencer identification of dynamical networks based on an information entropy dimension reduction method

Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control. Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure. However, these algorithms do not consider network state changes. We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity. By using mean field theory and information entropy to calculate node activity, we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance. We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C. elegans neural network. We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.

Robust Routing Using Electrical Flows

Generating alternative routes in road networks is an application of significant interest for online navigation systems. A high quality set of diverse alternate routes offers two functionalities - (a) support multiple (unknown) preferences that the user may have; and (b) robust changes in network conditions. We formulate a new quantification of the latter in this paper, and propose a novel method to produce alternative routes based on concepts from electrical flows and their decompositions. Our method is fundamentally different from the main techniques that produce alternative routes in road networks, which are the penalty and the plateau methods, with the former providing high quality results but being too slow for practical use and the latter being fast but suffering in terms of quality. We evaluate our method against the penalty and plateau methods, showing that it is as fast as the plateau method while also recovering much of the headroom towards the quality of the penalty method. The metrics we use to evaluate performance include the stretch (the average cost of the routes), the diversity , and the robustness (the connectivity between the origin and destination) of the induced set of routes.

Bayesian Regularization (BR) Optimization Based DDoS Detection for SDN and Next Generation Communication Network

The rapid evolution of communication networks, particularly Software-Defined Networking (SDN) and next-generation communication infrastructures, has introduced new challenges in securing these dynamic and complex environments. Among the most persistent threats are Distributed Denial of Service (DDoS) attacks, which can disrupt critical services and inflict severe economic and operational damages. To combat these threats, novel and adaptive DDoS detection mechanisms are crucial. This paper proposes a Bayesian Regularization (BR) optimization-based approach for DDoS detection in SDN and next-generation communication networks. Bayesian Regularization is a statistical technique that combines the strength of Bayesian analysis with optimization methodologies, enabling the model to adapt to changing network conditions and attack strategies. This approach leverages the inherent advantages of SDN, such as centralized control and real-time network monitoring, to enhance the accuracy and timeliness of DDoS detection.

On the feasibility of using an autonomic control loop and automated planning for tenant-oriented reconfiguration of network slices

In 5G, 6G and beyond networks, tenants need capabilities to reconfigure network slices since they must react quickly to changing network conditions and end-users’ demands to meet Service Level Agreements. Offering reconfiguration operations to tenants is challenging since those functions must be autonomous and governed by high-level network management policies. Existing approaches face the network slice reconfiguration problem from the provider perspective, disregarding the importance of providing slice reconfiguration capabilities to tenants. This paper presents an approach called ATRAP to demonstrate the feasibility of using an autonomic control loop and automated planning for reconfiguring network slices from the tenant perspective. ATRAP introduces a tenant-oriented architecture based on the monitor-analyze-plan-execute-knowledge method and automated planning for computing reconfiguration plans autonomously. An ATRAP’s plan aims to turn a network from a source configuration where one or more intents representing high-level network management policies are unmet into a target configuration in which the network meets the intents again. As ATRAP represents a network slice as a Service Function Chain comprising Virtual Network Functions (VNFs) and virtual links connecting them, a reconfiguration plan involves tasks to migrate VNFs and virtual links into the substrate network in a particular order. Results revealed as ATRAP computes plans with few migrations of VNFs and their adjacent links, leading to low reconfiguration costs related to short service disruptions. Also, results showed as ATRAP’s planning time depends on the size of the substrate network and the tenant’s slices. The results evidence that ATRAP is an attractive and feasible solution for the in-tenant reconfiguration of network slices.

Towards an Optimal Cloud-Based Resource Management Framework for Next-Generation Internet with Multi-Slice Capabilities

With the advent of 5G networks, the demand for improved mobile broadband, massive machine-type communication, and ultra-reliable, low-latency communication has surged, enabling a wide array of new applications. A key enabling technology in 5G networks is network slicing, which allows the creation of multiple virtual networks to support various use cases on a unified physical network. However, the limited availability of radio resources in the 5G cloud-Radio Access Network (C-RAN) and the ever-increasing data traffic volume necessitate efficient resource allocation algorithms to ensure quality of service (QoS) for each network slice. This paper proposes an Adaptive Slice Allocation (ASA) mechanism for the 5G C-RAN, designed to dynamically allocate resources and adapt to changing network conditions and traffic delay tolerances. The ASA system incorporates slice admission control and dynamic resource allocation to maximize network resource efficiency while meeting the QoS requirements of each slice. Through extensive simulations, we evaluate the ASA system’s performance in terms of resource consumption, average waiting time, and total blocking probability. Comparative analysis with a popular static slice allocation (SSA) approach demonstrates the superiority of the ASA system in achieving a balanced utilization of system resources, maintaining slice isolation, and provisioning QoS. The results highlight the effectiveness of the proposed ASA mechanism in optimizing future internet connectivity within the context of 5G C-RAN, paving the way for enhanced network performance and improved user experiences.

Coordination of distributed resources for frequency support provision in microgrids

To address the reduction of system inertia, transmission system operators are now requesting converter-interfaced generators (CIGs) to participate in frequency support services. Microgrids (MGs) and virtual power plants have been used as a solution for aggregation of small CIGs. It has been recently considered they may use their combined actions to provide frequency services. However, these proposals are not general enough, focusing on specific CIGs controllers and, in many cases, stability and operational limits are not considered. This paper presents an standardised methodology to design MG controllers that are in compliance with the MG transient response specifications (frequency nadir and rate-of-change-of-frequency, ROCOF). The methodology has an iterative nature and can be applied when the network conditions change. In each iteration, the control parameters are adjusted based on the sensitivity of the system eigenvalues against the frequency metrics of interest while stability and operational constraints are respected. Compared to the literature, the use of quantitative parametric sensitivity is performed for the reallocation of several eigenvalues using two sensitivity components, not only one. Moreover, it is suitable for any combination of grid-forming and grid-following devices. The proposed methodology is applied to a benchmark MG and results show that both nadir and ROCOF can be effectively modified and set as required. The methodology was validated by using a real MG comprising four 15 kW converters and one 75 kW converter.

Calibration of Effective Structural Number and Tensile Strain Models Using Traffic Speed Deflectometer (TSD) Data for Enhanced Project-Level Assessment on Flexible and Composite Pavements

Pavement deterioration models provide the basis for predicting future changes in network conditions, estimating future funding needs, and determining the effectiveness and timing of maintenance and rehabilitation activities. Determining the accurate structural condition of pavements helps identify effective maintenance strategies which enhance the sustainability and service life of pavements. This study aimed to use the Traffic Speed Deflectometer (TSD) and Fast Falling Weight Deflectometer (FFWD) for project-level evaluation of pavements and use pavement properties to calibrate current models that help to predict the structural condition of pavements. Model parameters were calibrated to determine the effective structural number and tensile strains at the bottom of asphalt concrete for asphalt and composite pavements from TSD deflections. Tensile strains from the KENLAYER highlighted varied behaviors for composite pavements. A significant improvement in the calibration was observed for asphalt concrete pavements. While the TSD has higher daily operational costs than FWD, its per-mile cost is significantly lower, making it a viable choice for extensive coverage, even though the quantitative results might differ between the two devices.

A novel machine learning-based framework for channel bandwidth allocation and optimization in distributed computing environments

Efficient utilization of network resources, particularly channel bandwidth allocation, is critical for optimizing the overall system performance and ensuring fair resource allocation among multiple distributed computing nodes. Traditional methods for channel bandwidth allocation, based on fixed allocation schemes or static heuristics, often need more adaptability to dynamic changes in the network and may not fully exploit the system’s potential. To address these limitations, we employ reinforcement learning algorithms to learn optimal channel allocation policies by intermingling with the environment and getting feedback on the outcomes of their actions. This allows devices to adapt to changing network conditions and optimize resource usage. Our proposed framework is experimentally evaluated through simulation experiments. The results demonstrate that the framework consistently achieves higher system throughput than conventional static allocation methods and state-of-the-art bandwidth allocation techniques. It also exhibits lower latency values, indicating faster data transmission and reduced communication delays. Additionally, the hybrid approach shows improved resource utilization efficiency, efficiently leveraging the strengths of both Q-learning and reinforcement learning for optimized resource allocation and management.