Network Environment Research Articles

A Q-Learning Based Target Coverage Algorithm for Wireless Sensor Networks

To address the problems of unclear node activation strategy and redundant feasible solutions in solving the target coverage of wireless sensor networks, a target coverage algorithm based on deep Q-learning is proposed to learn the scheduling strategy of nodes for wireless sensor networks. First, the algorithm abstracts the construction of feasible solutions into a Markov decision process, and the smart body selects the activated sensor nodes as discrete actions according to the network environment. Second, the reward function evaluates the merit of the smart body’s choice of actions in terms of the coverage capacity of the activated nodes and their residual energy. The simulation results show that the proposed algorithm intelligences are able to stabilize their gains after 2500 rounds of learning and training under the specific designed states, actions and reward mechanisms, corresponding to the convergence of the proposed algorithm. It can also be seen that the proposed algorithm is effective under different network sizes, and its network lifetime outperforms the three greedy algorithms, the maximum lifetime coverage algorithm and the self-adaptive learning automata algorithm. Moreover, this advantage becomes more and more obvious with the increase in network size, node sensing radius and carrying initial energy.

Ubunye: An MEC Orchestration Service Based on QoE, QoS, and Service Classification Using Machine Learning

The increasing adoption of Internet of Things devices has led to a significant demand for cloud services, where latency and bandwidth play a crucial role in shaping users’ perception of network service quality. However, the use of cloud services with the desired quality is not always available to all users. Furthermore, uneven network coverage in urban and rural areas has created “digital deserts”, which are characterized by a lack of connectivity resources, complicating access to cloud services. In this scenario, edge computing emerges as a promising alternative for service provision. Edge computing leverages data processing at or near the source where it is generated rather than sending it to the cloud for processing. It can lead to several advantages, such as reduced latency and lower bandwidth usage. This paper addresses the need to ensure consistent quality of experience (QoE) and quality of service (QoS) in dynamic network environments, particularly in remote regions with limited infrastructure. We propose an orchestration service called Ubunye, which operates at the network edge and selects the most appropriate edge node to fulfill a given application request while satisfying its quality requirements. Ubunye considers factors such as latency and available bandwidth when selecting a node to execute the requested service. It implements a service classification system based on machine learning (ML) techniques. The ideal edge node is chosen through a multi-faceted evaluation, which includes current CPU load, memory availability, and other relevant parameters. Experiment results show that Ubunye effectively orchestrates resources at the network edge, enhancing QoE and QoS for services that demand low latency and high bandwidth. Additionally, it showcases the ability to classify services and allocate resources under challenging network conditions.

Collaborative Design of Observer and Controller for Singular Systems With Transmission Delay Based on Dynamic Event‐Triggered

ABSTRACTThis article investigates the dynamic event‐triggered with communication delay fault estimation and fault‐tolerant control problem of singular systems in network environments under system faults and external disturbances. By considering both the current time and transmission delay output error, a fault diagnosis observer is constructed to estimate the system state and faults simultaneously based on dynamic event‐triggered sampling. On the basis of estimating the system state and fault signals, a fault‐tolerant controller is proposed to compensate for the impact of faults on the system and maintain its performance. Through the collaborative design of observers, controllers, and event‐triggering schemes, the required performance of the faulty system is ensured while reducing the consumption of communication resources. In addition by constructing a new enhanced Lyapunov functional and utilizing linear matrix inequalities and Lyapunov stability theory, the conditions for asymptotic stability of the system are less conservative and easily obtained. Finally, the effectiveness of the proposed method was verified through simulation results of two examples.

A Review of Deep Learning Applications in Intrusion Detection Systems: Overcoming Challenges in Spatiotemporal Feature Extraction and Data Imbalance

In the rapid development of the Internet of Things (IoT) and large-scale distributed networks, Intrusion Detection Systems (IDS) face significant challenges in handling complex spatiotemporal features and addressing data imbalance issues. This article systematically reviews recent advancements in applying deep learning techniques in IDS, focusing on the core challenges of spatiotemporal feature extraction and data imbalance. First, this article analyzes the spatiotemporal dependencies of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in network traffic feature extraction and examines the main methods these models use to solve this problem. Next, the impact of data imbalance on IDS performance is explored, and the effectiveness of various data augmentation and handling techniques, including Generative Adversarial Networks (GANs) and resampling methods, in improving the detection of minority class attacks is assessed. Finally, the paper highlights the current research gaps and proposes future research directions to optimize deep learning models further to enhance the detection capabilities and robustness of IDS in complex network environments. This review provides researchers with a comprehensive perspective, helping them identify the challenges in the current field and laying a foundation for future research efforts.

Hybrid event-based anti-bump finite-time H∞ control for cyber-physical switched systems under denial-of-service attacks

This paper aims to investigate the hybrid event-based anti-bump finite-time H∞ control method for cyber-physical switched systems under Denial of Service (DoS) attacks. By gaining insights into the characteristics of DoS attacks, a novel hybrid event-triggered scheme (HETS) is proposed. This scheme effectively reduces network transmission load and accurately captures the network service denial behavior caused by attack interference. In the context of complex network environments with multiple switching characteristics, this study addresses the challenges posed by HETS, transmission delays, and DoS attacks on the input signals of controllers. To tackle these challenges, we utilize an asynchronous switching strategy, along with bumpless transfer constraints and a zero-order hold mechanism. The objective is to design sustainable input asynchronous control signals while ensuring that the control scheme exhibits bumpless transfer (BT) characteristics and achieves H∞ performance. By employing the asynchronous switching strategy and the admissible edge-dependent average dwell time method (AED-ADT), a dual-mode Lyapunov function is devised to establish finite-time stability criteria and provide a design approach for constructing anti-bump H∞ controllers. Finally, two examples illustrate the applicability and effectiveness of the method.

An Innovative Priority Queueing Strategy for Mitigating Traffic Congestion in Complex Networks

Optimizing transportation in both natural and engineered systems, particularly within complex network environments, has become a pivotal area of research. Traditional methods for mitigating congestion primarily focus on routing strategies that utilize first-in-first-out (FIFO) queueing disciplines to determine the processing order of packets in buffer queues. However, these approaches often fail to explore the benefits of incorporating priority mechanisms directly within the routing decision-making processes, leaving significant room for improvement in congestion management. This study introduces an innovative generalized priority queueing (GPQ) strategy, specifically designed as an enhancement to existing FIFO-based routing methods. It is important to note that GPQ is not a new queue scheduling algorithm (e.g., deficit round robin (DRR) or weighted fair queuing (WFQ)), which typically manage multiple queues in broader queue management scenarios. Instead, GPQ integrates a dynamic priority-based mechanism into the routing layer, allowing the routing function to adaptively prioritize packets within a single buffer queue based on network conditions and packet attributes. By focusing on the routing strategy itself, GPQ improves the process of selecting packets for forwarding, thereby optimizing congestion management across the network. The effectiveness of the GPQ strategy is evaluated through extensive simulations on single-layer, two-layer, and dynamic networks. The results demonstrate significant improvements in key performance metrics, such as network throughput and average packet delay, when compared to traditional FIFO-based routing methods. These findings underscore the versatility and robustness of the GPQ strategy, emphasizing its capability to enhance network efficiency across diverse topologies and configurations. By addressing the inherent limitations of FIFO-based routing strategies and proposing a generalized yet scalable enhancement, this study makes a notable contribution to network optimization. The GPQ strategy provides a practical and adaptable solution for improving transportation efficiency in complex networks, bridging the gap between conventional routing techniques and emerging demands for dynamic congestion management.

Distributed Ledger Technology in Healthcare: Enhancing Governance and Performance in a Decentralized Ecosystem

This paper evaluates the technical feasibility of Distributed Ledger Technology (DLT) within the healthcare ecosystem, with a focus on the use of Corda DLT to enhance governance and performance in a decentralized ecosystem, ensuring data integrity, security, and trustworthiness. Key attributes examined include the guarantee of data integrity, ensuring that transmitted data remain unaltered; authenticity through the implementation of digital signatures and certificates; confidentiality achieved via secure peer-to-peer communication accessible only to authorized parties; and traceability and auditing mechanisms that enable tracking of information changes and accountability. To validate these features, a Corda Distributed Application (CorDapp) was developed to manage the core logic of the healthcare ecosystem. The CorDapp was deployed across nodes and executed within the Corda network. Its performance was assessed using metrics such as throughput, latency, CPU usage, and memory consumption in both local and cloud network environments. Results demonstrate the feasibility of using Corda DLT technology in healthcare, effectively addressing critical requirements such as integrity, authenticity, confidentiality, traceability, and auditing while maintaining satisfactory performance across diverse deployment scenarios.

Comprehensive Approaches for Detecting and Mitigating Distributed Denial of Service (DDoS) Attacks in Modern Network Environments

Distributed Denial of Service (DDoS) attacks remain one of the most significant threats to the security and availability of online services. These attacks exploit multiple systems, typically compromised devices, to flood a target server with excessive traffic, causing service disruption and resource depletion. Over the years, DDoS attacks have evolved in both scale and complexity, posing new challenges to cybersecurity professionals. This paper provides an in-depth analysis of DDoS attacks, categorizing various types, exploring their impact on both businesses and infrastructure, and reviewing the latest detection and mitigation techniques. We focus on the intersection of machine learning, network traffic analysis, and cloud-based solutions as advanced strategies to counteract these persistent threats. Additionally, we explore case studies highlighting the real-world applications of these methods. The paper concludes by proposing future research directions and the role of emerging technologies such as AI and blockchain in strengthening DDoS defenses.

A Robust Framework for Detecting Brute-Force Attacks through Deep Learning Techniques

A considerable concern arises with the precise identification of brute-force threats within a networked environment. It emphasizes the need for new methods, as existing ones often lead to many false alarms, as well as delays in real-time threat detection. To tackle these issues, this study proposes a novel intrusion detection framework that utilizes deep learning models for more accurate and efficient detection of brute-force attacks. The framework’s structure includes data collection and preprocessing components performed at the outset of the study using the CSE-CICIDS2018 dataset. The design architecture includes data collection and preprocessing steps. Feature extraction and selection techniques are employed to optimize data for model training. Further, after building the model, various attributes are extracted from the data from feature selection to be used in the training. Then, the construction of multiple architectures of deep learning algorithms, which include Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) models. Evaluation results show CNN and LSTM achieved the highest accuracy of 99.995 Parsant and 99.99 Parsant respectively. It showcases its ability to detect complex attack patterns in network traffic. It indicates that the CNN network got the best optimum results with a test time of 9.94 seconds. This establishes CNN as an effective method, achieving high accuracy quickly. In comparison, we have surpassed the accuracy of current methods while addressing their weaknesses. The findings are consistent with the effectiveness of CNN in brute-force attack detection frameworks as a more accurate and faster alternative, increasing the capability of detecting intrusions on a network in real-time.

An approach for multipath optimal selection of network service combinations based on golden eagle optimizer with double learning strategies

The traditional optimal-path algorithm can address a single constraint in small and straightforward networks. However, in complex multipath distributed cloud services, the network nodes no longer exhibit singular or deterministic path characteristics. It requires the optimal paths that not only determines the shortest routes, but also combine the safety, speed, and enhanced service quality across multiple service nodes in the network topology. The Golden Eagle Optimization Algorithm (GEO) is specialized for optimizing these network service combinations. On this basis, the Golden Eagle Optimizer with Double Learning Strategies (GEO-DLS) resolved the multipath optimal service selection issues within intricate network environments. The algorithm modeled the hunting tactics of wild golden eagles, efficiently targeting the best prey in minimal time by dynamically adjusting two critical components, such as the attack and cruising strategies. In GEO-DLS, the enhanced GEO significantly broadened the search scope for food sources by using personalized learning and mirror reflection techniques. These advancements notably enhanced the GEO search capabilities and improved the solution accuracy. Key contribution include GEO-DLS can converge to the optimal solution faster by optimizing the search strategy and parameter settings. This means that in the problem of network service composition, algorithms can quickly find the optimal path that meets the quality of service (QoS) requirements. To validate the effectiveness of GEO, a set of ten standard benchmark functions was utilized to evaluate its performance. The results from these evaluations consistently presented its superior performance in tackling optimization challenges compared to other five metaheuristic algorithms and five enhanced algorithms.

Towards contention resolution model to enhance rate of interference during dynamic spectrum utilization in cognitive radio network environment

In cognitive radio networks (CRNs), contention resolution is a fundamental challenge for optimizing dynamic spectrum access among secondary users (SUs) without causing interference to primary users (PUs). This study introduces a contention resolution model within cognitive radio networks. The queuing theory model of M/G/1/K with a First Come First Serve (FCFS) algorithm was employed. This paper proposes a novel contention resolution model using the First-Come-First-Served (FCFS) strategy, framed within a Markov Decision Process (MDP). The model dynamically allocates spectrum to SUs based on their arrival time. It leverages on M/G/1/K queuing model, providing a theoretical foundation for optimizing contention resolution. Through the FCFS algorithm, the model ensures equitable access to the spectrum, prioritizing the first arriving users. As a proof of concept and in order to validate the effectiveness of the model, simulations were performed using MATLAB and Minitab software. MATLAB was employed to simulate the dynamic behavior of the network under varying conditions, providing insights into throughput, delay, and collision metrics while Minitab was used for statistical analysis and validation of the simulation data, ensuring accuracy and reliability of the results. The results obtained from the simulations indicates that the MDP-based FCFS model significantly improves spectrum efficiency, reduces access delays, and minimizes collision rates compared to traditional contention resolution techniques. Another main contribution of this model formulation and evaluation is to help Nigerian Communication Commission (NCC) and other Dynamic Spectrum Usage functionaries in analyzing and optimizing the contention resolution predominant challenge, for the effective utilization of available spectrum resources in dynamic cognitive radio environment.

QR Code attendance deputation system

Traditional attendance systems can be time- consuming, prone to errors, and susceptible to proxy Atten- dances, where individuals falsely mark their presence in place of others. The” QR Code Attendance System” is a fast, efficient, and user-friendly solution for tracking attendance through QR codes, specifically designed to address these challenges. By leveraging web-based technology, it automates and streamlines the attendance process, significantly reducing the likelihood of attendance manipulation. Built with HTML, CSS, and Django, the system offers a seamless, real-time interface for marking presence. It operates within a local network environment. Key features include Automatic IP Fetching, which retrieves the student’s IPv4 address to generate the QR code, and a Faculty Panel that enables educators to identify and eliminate proxy attendances. This ensures that only legitimate attendance is recorded, enhancing the accuracy of the system. Additionally, it provides a straightforward interface for ease of use, real- time tracking through QR code scanning, and quick access for managing attendance records.

HMLPA*: a hierarchical multi-target LPA* pathfinding algorithm designed for dynamic indoor path network

The Lifelong Planning A* (LPA*) algorithm demonstrates unique advantages in dynamic pathfinding by employing incremental update techniques that reuse previously computed search results, enabling rapid path replanning in dynamic road network environments. However, when changes occur near the starting point or specific positions, such as key intersections, bottlenecks, or locations with high connectivity within the path network, even minor changes can trigger significant adjustments, which significantly increase the re-routing search space and computational costs of LPA*. To address this limitation, we propose a Hierarchical Multi-target LPA* (HMLPA*) algorithm that partitions the indoor path network into multiple subgraphs using the METIS graph partitioning algorithm and constructs an abstract trunk graph based on the key nodes of these subgraphs, thereby forming a hierarchical structure for the indoor path network. By leveraging this hierarchical structure, The HMLPA*’s subalgorithm, Multi-target LPA* (MLPA*), initiates pathfinding and confines re-routing to affected subgraphs and the abstract trunk graph. This localized re-routing approach effectively limits the search scope and significantly reduces computational overhead. Experimental results demonstrate that HMLPA* substantially outperforms LPA* in rerouting efficiency, effectively mitigating the high computational costs associated with the dynamic computational environment of the indoor path network.

Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks

As 6G development progresses, joint sensing and communication (JSC) is emerging as a transformative technology, promising enhanced spectrum and energy efficiency alongside innovative services. This paper delves into underexplored facets of JSC, particularly its role in vehicular technology and transportation systems. It discusses data fusion techniques that enable cooperative sensing in networked environments and underscores the critical role of resource management in balancing sensing and communication. It suggests modeling extended targets, such as vehicles, within a computationally feasible framework. Moreover, it proposes a novel integration of AI-based target recognition, allowing target-specific tracking parameters and target-based sensing resource allocation. Importantly, a case study is presented to underscore the real-world applicability of these concepts in vehicular scenarios, demonstrating how networked devices can achieve high sensing and communication performance.

Leveraging Network Target Theory for Efficient Prediction of Drug-Disease Interactions: A Transfer Learning Approach.

Efficient virtual screening methods can expedite drug discovery and facilitate the development of innovative therapeutics. This study presents a novel transfer learning model based on network target theory, integrating deep learning techniques with diverse biological molecular networks to predict drug-disease interactions. By incorporating network techniques that leverage vast existing knowledge, the approach enables the extraction of more precise and informative drug features, resulting in the identification of 88,161 drug-disease interactions involving 7,940 drugs and 2,986 diseases. Furthermore, this model effectively addresses the challenge of balancing large-scale positive and negative samples, leading to improved performance across various evaluation metrics such as an Area under curve (AUC) of 0.9298 and an F1 score of 0.6316. Moreover, the algorithm accurately predicts drug combinations and achieves an F1 score of 0.7746 after fine-tuning. Additionally, it identifies two previously unexplored synergistic drug combinations for distinct cancer types in disease-specific biological network environments. These findings are further validated through in vitro cytotoxicity assays, demonstrating the potential of the model to enhance drug development and identify effective treatment regimens for specific diseases.

Accelerating Consensus Reaching Through Top Persuaders: A Social Persuasion Model in Social Network Group Decision Making

In traditional group decision-making models, it is commonly assumed that all decision makers exert equal influence on one another. However, in real-world social networks, such as Twitter and Facebook, certain individuals—known as top persuaders—hold a disproportionately large influence over others. This study formulates the consensus-reaching problem in social network group decision making by introducing a novel framework for predicting top persuaders. Building on social network theories, we develop a social persuasion model that integrates social influence and social status to quantify individuals’ persuasive power more comprehensively. Subsequently, we propose a new CRP that leverages the influence of top persuaders. Our simulations and comparative analyses demonstrate that: (1) increasing the number of top persuaders substantially reduces the iterations required to achieve consensus; (2) establishing trust relationships between top persuaders and other individuals accelerates the consensus process; and (3) top persuaders retain a high and stable level of influence throughout the entire CRP rounds. Our research provides practical insights into identifying and strategically guiding top persuaders to enhance the efficiency in consensus reaching and reduce social management costs within social networked environments.

A Review of Adaptive Control Methods for Grid-Connected PV Inverters in Complex Distribution Systems

With the growth of energy demand and the aggravation of environmental problems, solar photovoltaic (PV) power generation has become a research hotspot. As the key interface between new energy generation and power grids, a PV grid-connected inverter ensures that the power generated by new energy can be injected into the power grid in a stable and safe way, and its power grid adaptability has also received more and more close attention in the field of new energy research. This research focuses on the discussion of PV grid-connected inverters under the complex distribution network environment, introduces in detail the domestic and international standards and requirements on grid-connected inverter grid adaptability, and then analyzes in depth the impacts of the access point voltage changes, access point frequency changes, and access point harmonic changes on the inverters. In order to enhance the adaptability of grid-connected inverters under these abnormal conditions, this research systematically summarizes and concludes a series of inverter adaptive control strategies, which provide literature guidance to effectively reduce the probability of power system faults and improve the reliability of the power system. Finally, the future development direction of PV inverter technology is outlooked, pointing out that, with the increase in the proportion of PV power generation in the power system, PV inverters need to evolve gradually from adapting to the grid to supporting the grid and promote the transformation of PV power generation from the auxiliary power source to the main power source through the integration of PV and energy storage.

Secure Platooning Control of Connected Vehicles Against Data Injection Attacks

This paper presents an attack-defense formation control framework for connected vehicle platoons. This framework effectively addresses the challenges posed by malicious attacks on multi-intelligent vehicle systems in open network environments. First, from the attacker’s perspective, an optimal data injection attack strategy is designed, taking into account energy constraints and the complexities of the communication environment. This strategy aims to disrupt the multi-intelligent vehicle system with minimal energy expenditure. Next, from the defender’s perspective, a formation compensation control strategy is proposed to enhance the system’s resilience against such attacks. This defense control strategy includes real-time monitoring and adjustments of the communication data between vehicles, ensuring that the vehicle formation remains stable and coordinated even in the case of data injection attacks. Finally, simulation experiments are conducted to validate the effectiveness and robustness of the proposed methods.

Performance analysis of artificial neural networks for predicting propagation losses in suburban environments for 4G LTE and 5G networks

<p>This study analyzes two distinct approaches for predicting path loss at frequencies of 800 MHz, 1800 MHz, and 2600 MHz in suburban areas. These frequencies are commonly utilized in broadcasting, 4G LTE, and 5G networks. Two models of Artificial Neural Networks (ANN) were implemented: an Error-Based Neural Network (EBNN), which incorporates error correction by combining empirical propagation models with an ANN, and a Terrain Parameters Based Neural Network (TBNN), which uses input parameters commonly applied in related studies, such as the distance from the transmitter to the receiver, receiver altitude, average terrain level, and azimuth angle between the transmitter and receiver. The performance of these models was evaluated using root mean square error (RMSE) and the Wilcoxon rank-sum test, comparing them with empirical propagation models such as SUI, ECC-33, Ericsson, and TR 25.942. The results were then compared with data obtained from a measurement campaign conducted along three routes in the city of Natal, Brazil. The findings from both simulations and actual measurements showed good metric alignment, particularly highlighting the performance of the error-based model. The primary contribution of this study is demonstrating that these techniques enable more accurate prediction of signal information, thereby reducing errors in the planning and implementation of wireless networks.</p>

An Intelligent Fuzzy-Based Routing Protocol for Vehicular Opportunistic Networks

Opportunistic networks are characterized by intermittent connectivity and dynamic topologies, which pose significant challenges for efficient message delivery, resource management, and routing decision-making. This paper introduces the Fuzzy Control Routing Protocol, a novel approach designed to address these challenges by leveraging fuzzy logic to enhance routing decisions and improve overall network performance. The protocol considers buffer occupancy, angle to destination, and the number of unique connections of the target nodes to make context-aware routing decisions. It was implemented and evaluated using the FuzzyC framework for simulations and the opportunistic network environment simulator for realistic network scenarios. Simulation results demonstrate that the Fuzzy Control Routing Protocol achieves competitive delivery probability, efficient resource utilization, and low overhead compared to the Epidemic and MaxProp protocols. Notably, it consistently outperformed the Epidemic protocol across all metrics and exhibited comparable delivery probability to MaxProp while maintaining significantly lower overhead, particularly in low-density scenarios. The results demonstrate the protocol’s ability to adapt to varying network conditions, effectively balance forwarding and resource management, and maintain robust performance in dynamic vehicular environments.