Dynamic Network Conditions Research Articles

Integrating Neutrosophic Theory for Improved Decision-Making in Wireless Body Area Networks: Enhancing Accuracy and Efficiency in Health Monitoring

Wireless Body Area Networks (WBANs) play a pivotal role in modern healthcare by enabling continuous monitoring of physiological data through sensors placed on or around the human body. Despite their significant benefits, WBANs face challenges such as data uncertainty, complex decision-making processes, and dynamic network conditions. These challenges can lead to inaccuracies and inefficiencies in health monitoring and diagnostics. The paper's main aim is to incorporate neutrosophic theory into Wireless Body Area Networks to provide enhancements in decision-making. In modern healthcare, the use of WBANs for monitoring physiological data by sensors, which are attached to or around the human body, can be continuous. Despite huge advantages, the main challenges that WBANs face are the uncertainties in data, complex decision-making processes, and dynamic network conditions, making health monitoring and diagnostics inaccurate and inefficient. In this paper, authors propose a robust framework to map sensor data into the neutrosophic domain and apply neutrosophic logic for enhanced accuracy and reliability of decision-making. In this paper, a Neutrosophic Decision-Making Algorithm is proposed, and its performance is compared with other decision-making techniques in terms of accuracy, response time, energy efficiency, and reliability. Experimental results show major improvements of around 95.3% in accuracy and a reduction of up to 25% in response time and energy consumption. Results underline the potential of neutrosophic theory for revolutionizing decision-making processes within WBANs to ensure more reliable and efficient health monitoring. This approach enables not only operational life but also improves patient outcome, avoiding a wrong diagnosis, during long-term health monitoring applications using WBAN devices.

Optimizing Wireless Sensor Networks: A Deep Reinforcement Learning-Assisted Butterfly Optimization Algorithm in MOD-LEACH Routing for Enhanced Energy Efficiency

Wireless Sensor Networks (WSNs) play a crucial role in diverse applications, necessitating the development of energy-efficient routing protocols to extend network lifetime. This study proposes a novel Deep Reinforcement Learning-Assisted Butterfly Optimization Algorithm (DRL-BOA) integrated with the MOD-LEACH protocol to optimize routing in WSNs. The proposed hybrid approach leverages the exploration and exploitation capabilities of BOA and the adaptive decision-making power of DRL to dynamically select cluster heads and optimal routes based on network conditions. The DRL-BOA model was evaluated on various WSN scenarios with node densities ranging from 50 to 500, considering parameters such as energy consumption, packet delivery ratio (PDR), throughput, and network lifetime. Simulation results demonstrated that the proposed method achieved a 22% reduction in energy consumption compared to traditional MOD-LEACH, a 15% improvement in PDR, a 27% increase in throughput, and an 18% enhancement in network lifetime over the Hybrid PSO-GWO approach. These significant improvements highlight the effectiveness of the DRL-BOA model in overcoming the limitations of existing algorithms. The proposed framework demonstrates superior adaptability to dynamic network conditions, making it a promising solution for energy-efficient and reliable WSN operations. Future work will explore integrating this model with emerging technologies, such as edge computing and the Internet of Things (IoT), for further enhancements

Dynamic spectrum resource allocations in wireless senor networks for improving packet transmission

Spectrum allocation has gained a lot of attention in cognitive wireless networks and research as one of the key problems for enhancing spectrum quality in the communication processes in the contemporary communication-dependent wireless environment. By effectively managing the restricted spectrum resources, adjusting to dynamic network conditions, lowering interference, and increasing energy efficiency, the study on dynamic spectrum allocation in wireless sensor networks seeks to improve packet transmission. In a variety of applications, this improves network performance, dependability, and quality of service. This is the reason we apply the dynamic source allocation to the sensor nodes in the network visualization for the packet transmission performance study in the 5G spectrum region. The primary goal of the study conducted for this article is to improve packet delivery ratios and data packet throughputs from the source to the destinations. Compared to previous research, this work has achieved 100% of its aims. In this context, certain DRL concerns are also addressed. The spectrum is allotted such that, in the event of phishing or malicious nodal assaults on the cluster groups of the wireless sensor nodal points in the WSN, efficient packet transmission will occur, beginning at the source and terminating at the sink. The simulation results demonstrate the efficacy of the approach described in the research paper and its application to data transmission.

PopFL: A scalable Federated Learning model in serverless edge computing integrating with dynamic pop-up network

With the rapid increase in the number of Internet-of-Things (IoT) devices, the massive volume of data creates significant challenges for traditional cloud-based solutions. These solutions often lead to high latency, increased operational costs, and limited scalability, making them unsuitable for real-time applications and resource-constrained environments. As a result, edge, and fog computing have emerged as viable alternatives, reducing latency and costs by processing data closer to its source. However, managing the flow of such vast and distributed data streams requires well-structured data pipelines to control the complete lifecycle—from data acquisition at the source to processing at the edge and fog layers, and finally storage and analytics in the cloud. To dynamically handle data analytics at varying distances from the source, often on heterogeneous hardware devices with collaborative learning techniques such as Federated Learning (FL). FL enables decentralized model training by leveraging the local data on Edge Devices (EDs), thereby preserving data privacy and reducing communication overhead with the cloud. However, FL faces critical challenges, including data heterogeneity, where the non-independent and identically distributed (non-IID) nature of data degrades model performance, and resource limitations on EDs, which lead to inefficiencies in training and biases in the aggregated models.To address these issues, we propose a novel FL solution, called Pop-Up Federated Learning (PopFL) in edge networks. This solution introduces hierarchical aggregation to reduce network congestion by distributing the aggregation tasks across multiple Fog Servers (FSs), rather than relying solely on centralized cloud aggregation. To further enhance participation and resource utilization at the edge, we incorporate the Stackelberg game model, which incentivizes EDs based on their contribution and resource availability. Additionally, PopFL employs a pop-up ad-hoc network for scalable and efficient communication between EDs and FSs, ensuring robust data transmission in dynamic network conditions. Extensive experiments conducted on three diverse datasets highlight the superior performance of PopFL compared to state-of-the-art FL techniques. The results show significant improvements in model accuracy, robustness, and fairness across various scenarios, effectively addressing the challenges of data heterogeneity and resource limitations. Through these innovations, PopFL paves the way for more reliable and efficient distributed learning systems, unlocking the full potential of FL in real-world applications where low latency and scalable solutions are critical.

AI-driven Performance Testing Framework for Mobile Applications

The rapid proliferation of mobile applications across diverse platforms has introduced unprecedented challenges in ensuring optimal performance under varying conditions. Traditional performance testing techniques often struggle to address the complexity of mobile environments, characterized by diverse devices, dynamic network conditions, and resource constraints. This paper presents an AI-Driven Performance Testing Framework for Mobile Applications, designed to revolutionize the way performance bottlenecks are identified and addressed. The proposed framework leverages artificial intelligence to automate the testing process, dynamically adapt to real-world scenarios, and provide actionable insights for developers. Key innovations include AIpowered workload generation that mimics realistic user behaviors, anomaly detection to uncover hidden performance issues, and predictive analytics to anticipate future bottlenecks. The framework integrates seamlessly with CI/CD pipelines, ensuring continuous and scalable performance assurance. To validate its effectiveness, we conducted extensive evaluations across multiple mobile applications, demonstrating significant improvements in test accuracy, efficiency, and resource utilization. By addressing critical challenges such as device diversity, latency variability, and resource optimization, this research establishes a foundation for the next generation of performance testing tools tailored to the unique demands of mobile applications.

Enhancing Stability and Efficiency in Mobile Ad Hoc Networks (MANETs): A Multicriteria Algorithm for Optimal Multipoint Relay Selection

Mobile ad hoc networks (MANETs) are autonomous systems composed of multiple mobile nodes that communicate wirelessly without relying on any pre-established infrastructure. These networks operate in highly dynamic environments, which can compromise their ability to guarantee consistent link lifetimes, security, reliability, and overall stability. Factors such as mobility, energy availability, and security critically influence network performance. Consequently, the selection of paths and relay nodes that ensure stability, security, and extended network lifetimes is fundamental in designing routing protocols for MANETs. This selection is pivotal in maintaining robust network operations and optimizing communication efficiency. This paper introduces a sophisticated algorithm for selecting multipoint relays (MPRs) in MANETs, addressing the challenges posed by node mobility, energy constraints, and security vulnerabilities. By employing a multicriteria-weighted technique that assesses the mobility, energy levels, and trustworthiness of mobile nodes, the proposed approach enhances network stability, reachability, and longevity. The enhanced algorithm is integrated into the Optimized Link State Routing Protocol (OLSR) and validated through NS3 simulations, using the Random Waypoint and ManhattanGrid mobility models. The results indicate superior performance of the enhanced algorithm over traditional OLSR, particularly in terms of packet delivery, delay reduction, and throughput in dynamic network conditions. This study not only advances the design of routing protocols for MANETs but also significantly contributes to the development of robust communication frameworks within the realm of smart mobile communications.

Hybrid RBFN-GWO Approach for Energy-Efficient and Secure Routing in Intelligent IoT Networks for Precision Farming

This paper introduces a hybrid approach combining Radial Basis Function Networks (RBFN) and Grey Wolf Optimization (GWO) to address the challenges of energy efficiency and security in Intelligent IoT networks for precision farming. Our proposed method utilizes RBFN for feature extraction and classification of network states, while GWO optimizes the routing parameters to achieve an optimal balance between energy conservation and security. The hybrid RBFN-GWO algorithm adapts to dynamic network conditions and evolving security threats in real-time. Extensive simulations using real-world precision farming data demonstrate that our approach outperforms existing protocols, achieving a 20% increase in network lifetime and a 15% improvement in intrusion detection accuracy. This research contributes to the development of more efficient and secure IoT infrastructures for precision agriculture applications.

Relay Assisted Device-to-Device Communication for Future Generation Wireless Networks: A Review from Architectural and Challenges Perspective

The rapid evolution of wireless communication technologies, along with the increasing demand for efficient and reliable data transfer, has driven the rethinking of traditional cellular network architectures in the context of next-generation networks. In conventional cellular systems, communication between users typically occurs through base stations. However, to improve efficiency, scalability, and spectral utilization, device-to-device (D2D) communication has been introduced, enabling direct data transmission between users without base station involvement. In this paper, we present a comprehensive review of D2D communication from architectural and challenges perspective for future generation networks. Additionally, the study focuses on relay-assisted D2D (RAD2D) communication, examining the role of machine learning (ML) and artificial intelligence (AI) in optimizing relay selection processes. In light of the existing literature, challenges for implementing RAD2D are discussed from different perspectives such as relay selection, energy efficiency, secure communication, resource allocation, and the management of dynamic network conditions

A Weighted AIMD Congestion Control Algorithm for Device-to-Device Communication in 5G Network

ABSTRACT Device-to-Device (D2D) communication offers promising benefits in 5G networks in terms of improved throughput and reduced latency. However, efficient congestion control remains a critical challenge to ensure optimal performance and resource utilization. Existing congestion control mechanisms often struggle with adapting to dynamic network conditions, leading to inefficiencies, such as underutilization of network resources or excessive packet loss. This paper addresses these challenges by proposing a novel Weighted AIMD congestion control algorithm tailored specifically for D2D communication in 5G networks (WACC-D2DC-5GN). Here, the Weighted AIMD Congestion Control Algorithm is proposed for estimating available bandwidth and Hunger Games Search Optimization to optimize the weight parameter of WACC. The proposed algorithm integrates weighted factors that dynamically adjust congestion window sizes based on real-time network feedback. Simulations are run on the NS3-mmWave module, NS3 LTE-A module. The simulation outcomes show that the proposed WACC-D2DC-5GN method attains 27.26%, 20.41%, and 23.26% higher SNR and 16.20%, 26.97%, and 30.34% higher Throughput when analysed with existing techniques like NexGen D-TCP: next generation dynamic TCP congestion control approach (NGD-TCP-CCA), user-centric channel allocation system for 5G higher-speed users by using ML to decrease handover rate (UCC-HSU-MLA), and efficient with reliable hybrid deep learning-allowed method for congestion control in 5G/6G networks (HDL-EMC-CN) respectively.

Interference management and power scheduling in femtocell networks with the optimized power scheduling BiLSTM

With the proliferation of femtocell networks, seamless hand-off, and efficient power management are crucial for ensuring undisturbed connectivity and the optimal utilization of resources. Certain limitations of the existing methods include limited adaptability to dynamic network conditions, suboptimal hand-off decision-making, and inefficient power allocation. To tackle the addressed drawbacks of the existing interference management, the Vocal Hunt Optimization-based Power Scheduling BiLSTM (VHO-based PS-BiLSTM) is introduced in the research. This model aims at achieving a seamless transition during the hand-off events while optimizing the power consumption, ultimately enhancing the energy efficiency of the network and maximizing the end-user quality of service. The optimized Bidirectional Long Short-term Memory (BiLSTM) achieves superior performance by acquiring the sequential dependencies in energy consumption data, effectively capturing underlying patterns, and enabling the storage and utilization of data from both past and future time steps. The BiLSTM classifier is optimized with the Vocal Hunt Optimization (VHO) to improve the efficiency of the detection process and in turn, the power scheduling is carried out by the VHO individually based on the detected hand-off. The efficiency of the research is measured in terms of accuracy, sensitivity, specificity, delay, energy, Quality of service (QoS), and throughput which achieve 96.8 %, 95.76 %, 96.25 %, 109.38 ms, 0.999 J, 0.513, and 0.487 respectively.

Extensive Review of Security and Privacy Issues in Heterogeneous Networks

This paper studies advanced network security, privacy, and performance issues for Heterogeneous Networks (HetNets), which combine multiple types of cells to enhance wireless communication coverage and capacity. The coexistence of heterogeneous network elements inherently leads to considerable issues in terms of security, privacy, and performance. Previous research has primarily addressed security issues using common cryptographic techniques including data encryption with AES, secure key exchange with RSA, and handoff security with mutual authentication. Data anonymization methods and regulatory compliance have been used to address privacy concerns. Allocating resources and managing interference are some of the tactics that have been used in performance optimization. Although the current solutions offer a solid base, they frequently have issues with computational overhead, scalability, and continuous maintenance needs. Performance optimization solutions might not sufficiently handle dynamic network conditions, and privacy protections might not be sufficient to mitigate sophisticated data harvesting operations. This study evaluates existing HetNets solutions using a thorough evaluation and analytical technique. It assesses how well they handle issues with security, privacy, and performance, points out any shortcomings, and suggests areas for further investigation. Our analysis emphasizes the need for improved security mechanisms, including quantum-resistant cryptography, AI-driven threat detection, and technologies that improve privacy, such as differential privacy and homomorphic encryption. Innovative resource management and optimization strategies catered to HetNets' dynamic nature are needed to address performance issues. The necessity of developing new security, privacy, and performance solutions to guarantee the stability and dependability of HetNets is emphasized by this study. Stakeholders can promote the broad adoption and smooth integration of HetNet technology by tackling these obstacles. To sum up, this study offers a thorough analysis of the HetNets' architectural, security, privacy, and performance issues. It lists existing remedies, analyses their drawbacks, and suggests new lines of inquiry for future development to advance the area and improve HetNets' operational capabilities.

Towards Distributed Flow Scheduling in IEEE 802.1Qbv Time-Sensitive Networks

Flow scheduling plays a pivotal role in enabling Time-Sensitive Networking (TSN) applications. Current flow scheduling mainly adopts a centralized scheme, posing challenges in adapting to dynamic network conditions and scaling up for larger networks. To address these challenges, we first thoroughly analyze the flow scheduling problem and find the inherent locality nature of time scheduling tasks. Leveraging this insight, we introduce the first distributed framework for IEEE 802.1Qbv TSN flow scheduling. In this framework, we further propose a multi-agent flow scheduling method by designing Deep Reinforcement Learning (DRL)-based route and time agents for route and time planning tasks. The time agents are deployed on field devices to schedule flows in a distributed way. Evaluations in dynamic scenarios validate the effectiveness and scalability of our proposed method. It enhances the scheduling success rate by 20.31% compared to state-of-the-art methods and achieves substantial cost savings, reducing transmission costs by 410× in large-scale networks. Additionally, we validate our approach on edge devices and a TSN testbed, highlighting its lightweight nature and ease of deployment.

Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework.

The growing importance of edge and fog computing in the modern IT infrastructure is driven by the rise of decentralized applications. However, resource allocation within these frameworks is challenging due to varying device capabilities and dynamic network conditions. Conventional approaches often result in poor resource use and slowed advancements. This study presents a novel strategy for enhancing resource allocation in edge and fog computing by integrating machine learning with the blockchain for reliable trust management. Our proposed framework, called CyberGuard, leverages the blockchain's inherent immutability and decentralization to establish a trustworthy and transparent network for monitoring and verifying edge and fog computing transactions. CyberGuard combines the Trust2Vec model with conventional machine-learning models like SVM, KNN, and random forests, creating a robust mechanism for assessing trust and security risks. Through detailed optimization and case studies, CyberGuard demonstrates significant improvements in resource allocation efficiency and overall system performance in real-world scenarios. Our results highlight CyberGuard's effectiveness, evidenced by a remarkable accuracy, precision, recall, and F1-score of 98.18%, showcasing the transformative potential of our comprehensive approach in edge and fog computing environments.

Energy-Efficient Virtual Network Embedding: A Deep Reinforcement Learning Approach Based on Graph Convolutional Networks

Network virtualization (NV) technology is the cornerstone of modern network architectures, offering significant advantages in resource utilization, flexibility, security, and streamlined management. By enabling the deployment of multiple virtual network requests (VNRs) within a single base network through virtual network embedding (VNE), NV technology can substantially reduce the operational costs and energy consumption. However, the existing algorithms for energy-efficient VNE have limitations, including manual tuning for heuristic routing policies, inefficient feature extraction in traditional intelligent algorithms, and a lack of consideration of periodic traffic fluctuations. To address these limitations, this paper introduces a novel approach that leverages deep reinforcement learning (DRL) to enhance the efficiency of traditional methods. We employ graph convolutional networks (GCNs) for feature extraction, capturing the nuances of network graph structures, and integrate periodic traffic fluctuations as a key constraint in our model. This allows for the predictive embedding of VNRs that is both energy-efficient and responsive to dynamic network conditions. Our research aims to develop an energy-efficient VNE algorithm that dynamically adapts to network traffic patterns, thereby optimizing resource allocation and reducing energy consumption. Extensive simulation experiments demonstrate that our proposed algorithm achieves an average reduction of 22.4% in energy consumption and 41.0% in active substrate nodes, along with a 23.4% improvement in the acceptance rate compared to other algorithms.

Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer.

The IEEE 802.11ah standard is introduced to address the growing scale of internet of things (IoT) applications. To reduce contention and enhance energy efficiency in the system, the restricted access window (RAW) mechanism is introduced in the medium access control (MAC) layer to manage the significant number of stations accessing the network. However, to achieve optimized network performance, it is necessary to appropriately determine the RAW parameters, including the number of RAW groups, the number of slots in each RAW, and the duration of each slot. In this paper, we optimize the configuration of RAW parameters in the uplink IEEE 802.11ah-based IoT network. To improve network throughput, we analyze and establish a RAW parameters optimization problem. To effectively cope with the complex and dynamic network conditions, we propose a deep reinforcement learning (DRL) approach to determine the preferable RAW parameters to optimize network throughput. To enhance learning efficiency and stability, we employ the proximal policy optimization (PPO) algorithm. We construct network environments with periodic and random traffic in an NS-3 simulator to validate the performance of the proposed PPO-based RAW parameters optimization algorithm. The simulation results reveal that using the PPO-based DRL algorithm, optimized RAW parameters can be obtained under different network conditions, and network throughput can be improved significantly.

Intellectual Property Law's Confiscatory Losses: Its Application in Dynamic Network Environments

This paper discusses confiscatory losses in the dynamic network enforcement of intellectual property (IP). IP law protects copyrights, patents, and trademarks. Instead, IP enforcement may consequence in "confiscatory damages." This occurs when the defendant's property is apprehended pursuant to a court order. For both individuals and companies, the internet has made sharing and disseminating innovative creations simpler. IP infringement is also increased by this. In these circumstances, IP law enforcement has become more crucial. Penalisations and sanctions for IP infringement in these conditions may be severe and upshot in confiscatory losses. So as to apply confiscatory losses in dynamic network conditions, this paper suggests reviewing IP law enforcement strategies. Hence, IP infringement penalisations and compensation should be proportionate to the harm caused and avoid confiscatory losses. Moreover, IP infringement can also be minimized by education, information, and alternative dispute resolution. IP law enforcement in dynamic network systems must strike a compromise between protecting IP rights and sidestepping confiscatory damages.

Adjustable random linear network coding (ARLNC): a solution for data transmission in dynamic IoT computational environments

In mobile computing environments, most IoT devices connected to networks experience variable error rates and possess limited bandwidth. The conventional method of retransmitting lost information during transmission, commonly used in data transmission protocols, increases transmission delay and consumes excessive bandwidth. To overcome this issue, forward error correction techniques, e.g., Random Linear Network Coding (RLNC) can be used in data transmission. The primary challenge in RLNC-based methodologies is sustaining a consistent coding ratio during data transmission, leading to notable bandwidth usage and transmission delay in dynamic network conditions. Therefore, this study proposes a new block-based RLNC strategy known as Adjustable RLNC (ARLNC), which dynamically adjusts the coding ratio and transmission window during runtime based on the estimated network error rate calculated via receiver feedback. The calculations in this approach are performed using a Galois field with the order of 256. Furthermore, we assessed ARLNC's performance by subjecting it to various error models such as Gilbert Elliott, exponential, and constant rates and compared it with the standard RLNC. The results show that dynamically adjusting the coding ratio and transmission window size based on network conditions significantly enhances network throughput and reduces total transmission delay in most scenarios. In contrast to the conventional RLNC method employing a fixed coding ratio, the presented approach has demonstrated significant enhancements, resulting in a 73% decrease in transmission delay and a 4 times augmentation in throughput. However, in dynamic computational environments, ARLNC generally incurs higher computational costs than the standard RLNC but excels in high-performance networks.

Dynamic hierarchical intrusion detection task offloading in IoT edge networks

AbstractThe Internet of Things (IoT) has gained widespread importance in recent time. However, the related issues of security and privacy persist in such IoT networks. Owing to device limitations in terms of computational power and storage, standard protection approaches cannot be deployed. In this article, we propose a lightweight distributed intrusion detection system (IDS) framework, called FCAFE‐BNET (Fog based Context Aware Feature Extraction using BranchyNET). The proposed FCAFE‐BNET approach considers versatile network conditions, such as varying bandwidths and data loads, while allocating inference tasks to cloud/edge resources. FCAFE‐BNET is able to adjust to dynamic network conditions. This can be advantageous for applications with particular quality of service requirements, such as video streaming or real‐time communication, ensuring a steady and reliable performance. Early exit deep neural networks (DNNs) have been employed for faster inference generation at the edge. Often, the weights that the model learns in the initial layer may be sufficiently qualified to perform the required classification tasks. Instead of using subsequent layers of DNNs for generating the inference, we have employed the early‐exit mechanism in the DNNs. Such DNNs help to predict a wide range of testing samples through these early‐exit branches, upon crossing a threshold. This method maintains the confidence values corresponding to the inference. Employing this approach, we achieved a faster inference, with significantly high accuracy. Comparative studies exploit manual feature extraction techniques, that can potentially overlook certain valuable patterns, thus degrading classification performance. The proposed framework converts textual/tabular data into 2‐D images, allowing the DNN model to autonomously learns its own features. This conversion scheme facilitated the identification of various intrusion types, ranging from 5 to 14 different categories. FCAFE‐BNET works for both network‐based and host‐based IDS: NIDS and HIDS. Our experiments demonstrate that, in comparison with recent approaches, FCAFE‐BNET achieves a 39.12%–50.23% reduction in the total inference time on benchmark real‐world datasets, such as: NSL‐KDD, UNSW‐NB 15, ToN_IoT, and ADFA_LD.

Analysis of Wireless Multimedia Big Data Transmission Based on Adaptive Scheduling Algorithm

The increasing demand for wireless multimedia applications has prompted the need for efficient data transmission strategies that can adapt to dynamic network conditions and diverse Quality of Service (QoS) requirements. This paper analyzes wireless multimedia big data transmission using an innovative Adaptive QoS-Aware Scheduling Algorithm (AQOS-ASA). The proposed algorithm aims to optimize the transmission of multimedia data by dynamically adjusting the scheduling based on QoS considerations and real-time network conditions. Through adaptive scheduling, the algorithm ensures that high-priority multimedia data receives optimal transmission treatment, balancing the quality preferences set by users. The algorithm dynamically allocates time slots based on the changing network conditions, allowing for efficient utilization of the available bandwidth. The paper analyses the algorithm's performance in different wireless network scenarios through simulations and evaluations, highlighting its effectiveness in adapting to varying QoS requirements and network conditions. The results demonstrate that the proposed algorithm significantly enhances the quality and efficiency of wireless multimedia big data transmission, making it well-suited for applications with diverse multimedia content and dynamic network environments. The findings contribute valuable insights to wireless multimedia communication and provide a foundation for further advancements in adaptive scheduling algorithms for improved Quality of Service.

To Design a Routing Module for IoT Routing Protocols to Address Modification and Manipulation Attacks

Internet of Things devices have become increasingly prevalent in various domains, including smart homes, healthcare, transportation, and industrial automation. As the number of IoT devices continues to grow, so does the potential for security challenges. Routing protocols play a critical role in IoT networks by determining the path for packet transmission. However, their susceptibility to modification and manipulation attacks can have far-reaching consequences. These attacks can lead to unauthorized access to sensitive data, disruption of critical services, and even physical harm in certain applications such as healthcare and industrial automation. This paper presents the design and implementation of a security-enhanced routing module aimed at protecting Internet of Things networks from modification and manipulation attacks, particularly focusing on the challenges within the IPv6 Routing Protocol for Low-Power and Lossy Networks. Acknowledging the increasing pervasiveness of IoT devices and their susceptibility to routing disruptions, this work contributes a novel approach to augment RPL's Lamport’s Keyed Hash Chain scheme security posture without impinging on the protocol's inherent resource-efficiency. Through a comprehensive simulation-based system verification process, the module demonstrates its effectiveness in mitigating common routing attacks, its adaptability in dynamic network conditions, and its fidelity to established RPL performance metrics