Video-based Cognitive radio networks (CRNs) are a sub-type in which some video users send video traffic. In video CRN context, cognitive users must find an optimal rate/power assignment strategy by solving an optimization problem to maximize their perceived quality of experience (QoE) under interference and energy constraints. Due to large dimension of parameter space in dense CRNs, solving this problem using traditional methods such as game theory or other analytical gradient descent-based approaches can lead to large computational burden. Basically, in such scenarios, each cognitive video user actually faces with mean interference effect from its surrounding nodes and must adopt its behavior (rate/power optimization) strategy based on this mean-field interference effect. Because of inherent nature of mean-field game (MFG) theory in distributed solving of high-dimensional optimization problems, it seems to be a good solution candidate in this context. So, in the current paper, we have used MFG for green (energy-efficient) quality control of cognitive users in dense CRNs. We design two different solution approaches based on a finite-difference method (named GMFQ) and machine learning (named D2GMFQ). The first approach is a standard MFG solution but lacks good scalability in very dense CRN scenarios. So, we introduce the second methodology which is fast enough to tackle such cases. Numerical results show that the proposed methods, outperforms similar ones in maximizing sum perceived cognitive user QoEs under energy-efficiency constraints. Specifically, it is determined that about 11 dB and 15 dB gains can be achieved by GMFQ in average comparing with the traditional TCP and UDP streaming scenarios respectively.

Wireless Body Sensor Networks (WBSNs) face key challenges in balancing energy efficiency, routing reliability, and data privacy under dynamic body movements and strict resource constraints. Clustering and routing protocols usually operate separately, which reduces their performance in adaptive situations and causes a lack of integrated privacy protections for sensitive healthcare data. This paper introduces an improved routing framework that combines Deep Embedded Clustering (DEC) with an Adaptive Contextual Routing Protocol (ACRP) to enable energy-efficient, context-aware, and privacy-protected communication in WBSNs. The approach features a multi-stage system: (1) autoencoder-based learning of spatial-temporal node features, (2) attention-driven Graph Neural Network (GAT) for mapping local topology, (3) DEC for flexible cluster creation with balanced cluster head selection, and (4) ACRP using a Deep Q-Network routing agent with multi-objective path evaluation. The protection provided by this protocol is achieved through differential privacy (ε-differential privacy), where Laplace noise is added, along with random and quantum-mechanical processes, to ensure the probabilistic direction of transmitted data. Simulation results applied to real physiological datasets (PhysioNet and MHEALTH multi-sensor systems) showed that the DEC + ACRP framework achieved significant performance improvements. This led to a 25–30

Modern society increasingly relies on online applications, which demand high-quality networking services. Network Intrusion Detection Systems (NIDS) are vital for detecting and mitigating cyberattacks that aim to disrupt such services. While the research community continues to develop innovative NIDS, several limitations persist in state-of-the-art systems, including a heavy reliance on supervised configuration, burdensome hyperparameter tuning, and a failure to account for traffic contamination in training data. In this work, we address these challenges by proposing an unsupervised hybrid NIDS that integrates Generative Adversarial Network traffic modeling with Dynamic Dictionaries of legitimate and suspicious IP Addresses (GAN-DDA). Specifically, a fast Anomaly GAN (f-AnoGAN) is designed to reconstruct historical IP flow traffic and attribute higher residuals to anomalous samples. An adaptive threshold is integrated into the model for autonomous traffic classification, demonstrating robustness against low levels of traffic contamination in the training sets. A post-processing stage further refines inferences based on the state of dynamic dictionaries. The proposed system achieves F1-scores of 0.9215, 0.9649, and 0.9041 on the UNSW-NB15, CIC-IDS2017, and CSE-CIC-IDS2018 benchmarks, respectively. An ablation study validates the necessity of each internal component for achieving this performance. Comparisons with state-of-the-art alternatives indicate competitive performance, though differences in experimental setups across studies highlight the need for standardized future evaluations. Finally, future research directions are identified to improve the statistical generalization, long-term performance, and applicability of reconstruction-based NIDS to real networks.

Routing is an important element of network communication that enables data transmission between network devices. This process depends on a routing protocol, which assesses and selects the best route for data communication from source to destination using routing metrics. Consequently, the network's performance is directly impacted by the routing metric selection. Therefore, the choice of a routing metric directly influences the performance of the network. For example, an inadequate routing metric can increase data packet loss, latency, and energy consumption. In this study, we recognise the importance of routing metrics. For instance, the Expected Transmission Count (ETX) is a good measure of link quality. It estimates the transmission count required for successful communication. However, even though ETX is effective in terms of link reliability measurement, it fails to consider the consumed energy during communication. Yet, every data transmission consumes energy because a path with high reliability can still consume a lot of energy due to its length. For this reason, we propose Energy Aware_ ETX (EA_ETX), an enhanced form of ETX, which considers both link quality and energy consumption during communication. We integrate this proposed routing metric in the Routing Protocol for Low-Power and Lossy Networks (RPL) and propose an objective function, which is labelled OF_EA_ETX. We conducted simulations in Contiki Cooja and compared the results of OF_EA_ETX with the predefined objective functions of RPL (MRHOF and OF0). The results demonstrate that our approach outperforms OF0 and MRHOF, respectively, by reducing energy consumption by 11.29% to 28.48%, minimising end-to-end delay by 2.08% to 21.58%, and improving packet delivery ratio by 16.67% to 36.13%.

Neonatal health requires precise lipid quantification in human milk to ensure proper nutritional development. Traditional manual methods, such as the creamatocrit, are limited by human-induced bias and significant measurement uncertainty. This study presents a low-cost Computer Vision System acting as an automated optical sensing modality for estimate the cream fraction (c) using advanced Machine Learning regression, which is subsequently used to derive fat and energy quantification through established analytical equations. The system is optimized for the Gold-LED spectrum, which enhances the dynamic range to 226 a.u. for robust feature extraction. We evaluated 28 distinct ML regression models across three feature spaces (Gray Scale, RGB, and Combined). The results, based on 6400 samples, demonstrate that the Rational Quadratic GPR model achieved the highest predictive stability with a coefficient of determination of R2=0.867. This computational framework achieved a 57.5% reduction in relative error compared to manual benchmarks. SHAP analysis indicates that the model selectively attributes higher importance to Red channel intensities and Blue contrast gradients, which correspond to the optical scattering characteristics of lipid globules. These findings validate the system as a stable sensing modality for non-invasive quantification. The proposed architecture integrates cost-effective hardware with high-precision analytical modeling, offering a reagent-free and operationally feasible alternative for standardized nutritional assessment in neonatal intensive care units and milk banks.

Smart city infrastructures have increasingly benefited from the Internet of Drones (IoD), enabling critical applications such as public safety, surveillance, traffic monitoring, and environmental monitoring. However, drones’ inherent mobility and severe resource constraints pose significant challenges to secure communication over unsecured wireless networks. This paper presents the design and evaluation of a lightweight blockchain-assisted authentication and key agreement protocol for IoD environments. The proposed scheme combines temporary identities, elliptic curve signatures, Diffie–Hellman key exchange, nonces, and a permissioned blockchain with practical byzantine fault tolerance (PBFT)-based consensus. Unlike conventional blockchain-assisted approaches, the proposed architecture decouples real-time authentication from blockchain governance, preventing consensus latency from affecting the authentication critical path. Security analysis demonstrates resistance against impersonation, replay, and man-in-the-middle attacks, while semi-automated formal verification confirms resilience against active adversaries. Comparative performance evaluation shows that the proposed protocol achieves low computational, communication, and energy overhead, while maintaining minimal authentication delay compared with related IoD schemes. These results demonstrate that the proposed protocol provides an efficient, lightweight, and secure authentication solution for resource-constrained IoD environments.

The detection and monitoring of oil spills at sea (OSS) are essential for environmental disaster management and the protection of marine ecosystems. The objective was to integrate scattering coefficient (σ0) data from Sentinel-1 (S1) with multispectral images from Sentinel-2 (S2) for OSS detection, as in the case that occurred on January 15, 2022, at the La Pampilla refinery in Ventanilla, Lima, Peru. To achieve this, the σ0 values in VV polarization from S1-GRDH were analyzed, establishing characteristic threshold ranges of -21 dB to -13 dB for fresh oil (January 25) and -19 dB to -13 dB for weathered oil (February 2). This 3 dB shift in the median quantifies the weathering and emulsification processes of the hydrocarbon. The methodology employed includes the calculation of spectral indices (OSI, VNRI, IVI, Zakzouk) and the evaluation of S2 spectral signatures. The OSI, with a range of 1.1 to 1.5, achieved the highest agreement (κ = 0.808, “Very good”), establishing itself as the most reliable indicator for operational binary detection. Although OSI is optimal for binary detection, net area quantification was performed by spectral unmixing, yielding 6,818 ha (∼68 km2). Validation using the Kappa index and Mann-Whitney-Wilcoxon U test (p<0.05) demonstrated statistical significance and confirmed the spectral homogeneity of the signatures. The findings include this oil spill map, which establishes the Ventanilla spill as the largest ecological disaster in Peruvian history. This methodological framework is effective for the studied event and provides a reference that can be adapted to future oil spill events, contributing to the development of capabilities for rapid response to marine environmental disasters.

Volatile organic compounds (VOCs) are central to the aromatic and therapeutic properties of essential oils (EOs), with their profiles serving as reliable indicators of EO quality. In Cistus ladanifer, the synthesis and emission of VOCs—particularly terpenic hydrocarbons—are strongly influenced by environmental variables such as temperature, humidity, and solar radiation. Traditional EO quality assessment methods, including gas chromatography (GC), although highly accurate, are costly, labor-intensive, and destructive. This study proposes a smart sensor system that utilizes a low-cost array of MQ gas sensors combined with machine learning (ML) algorithms for non-destructive classification of VOC fingerprints in Cistus ladanifer EO. VOC data from 33 EO samples were collected using MQ sensors and paired with environmental datasets (Daily, 15-Day, and All) obtained from weather stations in the cultivation areas. Gas chromatography coupled with flame ionization detection and mass spectrometry (GC-FID/MS) was used as the reference method to quantify the concentrations of terpenic hydrocarbons. A total of 5,154 data points were used, with 75

Purpose Collaborative robots, or "cobots", are central to the development of Industry 5.0, enabling enhanced human-machine collaboration and improved operational productivity. However, their increasing integration within cyber-physical systems raises significant cybersecurity concerns. This study examines the interplay between cobots, cybersecurity vulnerabilities and Industry 5.0, with particular emphasis on software vulnerabilities and cyber-attacks and their effects on productivity. Drawing on dynamic capabilities theory, the research further investigates how the integration of Internet of Things (IoT) networks shapes these relationships.Design/methodology/approach The study employs quantitative analysis based on data collected from 134 industry professionals involved in cyber-physical production systems.Findings The results indicate that software vulnerabilities negatively affect productivity within cyber-physical production environments and disrupt operational efficiency in IoT-enabled networks. Cyber-attacks are found to significantly reduce employee productivity and undermine network resilience. At the same time, IoT integration enhances product testing efficiency, while cobot deployment contributes to improved workplace safety.Originality/value This study contributes to the emerging literature on cybersecurity in Industry 5.0. It highlights the role of privacy-enhancing technologies (PETs) and robust IoT protocols in strengthening data security and operational resilience. The findings offer practical insights for managers seeking to integrate cybersecurity measures effectively, thereby supporting resilient, sustainable, secure and human-centric automation in Industry 5.0 contexts.

G networks demand a shift from pure throughput maximization to attending heterogeneous Quality of Service (QoS), particularly for Ultra-Reliable Low-Latency Communication (URLLC). This manuscript proposes a nested metaheuristic framework for solving the joint radio resource allocation (RRA) and power control (PC) problem in D2D-underlay multi-cell networks. Formulated as a mixed-integer nonlinear programming (MINLP), the problem is decomposed into a discrete resource allocation subproblem solved by an Enhanced Whale Optimization Algorithm (E-WOA) and a continuous power control subproblem handled by an Improved Gray Wolf Optimizer (IGWO). Extensive simulations demonstrated the E-WOA/I-GWO framework consistently satisfies stringent URLLC requirements across all evaluated scenarios. The results validated the scheme’s scalability and its ability to ensure QoS in densified environments.

In this research project we delve into the realm of energy efficiency and multi hop communication methods, in sensor networks (UWSNs) focusing on overcoming obstacles, like restricted energy supply, dependable communication connections and ever-changing environmental conditions. The proposed Energy-Efficient Clustering Multi-Hop Routing Protocol (EECMR) utilizes depth-based clustering and adaptive multi-hop communication to enhance network lifetime and energy efficiency. By dynamically selecting cluster heads based on residual energy and incorporating advanced methods like packet size optimization, the protocol ensures balanced workload distribution and improved data reliability. Simulation results show that EECMR is more effective than previous protocols, helping to save energy and keep the network running for a longer time. This research provides a foundation for sustainable UWSN designs with applications in environmental monitoring, disaster management, and marine exploration, while highlighting the need for real-world validation and future advancements in hybrid communication systems and lightweight algorithms.

The kind of cyber threat prevalent and most dangerous to networked systems is the Distributed Denial of Service (DDoS), especially with expanded connection of Internet of Things (IoT) devices. This article categorizes DDoS attacks into three primary types: volumetric, protocol based and application layer of cyber attacks. It discusses the application of security threats that arise from the use of the DL models, accusing recently introduced ideas and stressing pitfalls: the issues of data and methods scarcity. There is the same need for the greater use of explainable and transparent AI to improve confidence in such security systems as is noted in the review. It also reveals that present detection performance is constrained and frequently obstructed by the poor quality of the datasets. The future work is proposed to build superior datasets and use accurate algorithm to improve the security models. This paper focuses on explainability as a way of making the AI model creation process and any consequent decisions explainable and transparent. The use of deep learning enhances the capability of cybersecurity in handling DDoS attacks and preventing or controlling them. But it has to be a part of a more large-scope platform, based on multiple types of longitudinal or cross-sectional data combined with high efficiency, explainable AI. The article ends with call to proceed with studying and advancing the AI application in response to new threats, and make the most of it to enhance protection of the contemporary networked environment.

The recent developments in telecommunication technologies and monitoring devices have brought many changes in modern electronic healthcare systems (EHSs) by improving quality and decreasing healthcare expenses. Despite the benefits, they have privacy and security issues because the communication between patients and service providers takes place generally over public channels. Several user authentication protocols using distributed ledger technology (DLT) have recently been proposed to address these issues in EHSs. However, many are still vulnerable to a single point of failure (SPoF), privacy, and security attacks. Besides, they suffered from high communication and computational costs. Therefore, in this paper, we proposed a user authentication protocol using DLT to avoid these issues. A Burrows-Abadi-Needham (BAN) logic proof method has been used to check the security of the proposed protocol and ensure it achieves the desired security goals. In addition, an informal security analysis has been conducted to verify its important security requirements. A formal security analysis has been performed via the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and Real-or-Random (ROR) model for further security strength. The results demonstrate that the proposed user authentication protocol is SAFE against all types of Man-in-the-Middle (MitM) attacks, impersonation, replay, and forgery attacks. Finally, performance analysis has been performed and results show that it achieves better performance by consuming 29.63 % and 13.21 % less communication and computational overheads as compared to existing related user authentication protocols. The security and performance analysis make it a more appropriate choice for the EHSs.

The Internet of Things (IoT) and its industrial counterpart, the Industrial Internet of Things (IIoT), have transformed sectors such as home automation, healthcare, and manufacturing by enhancing data management through advanced networking. However, the rapid growth of IIoT has introduced significant cybersecurity challenges, necessitating a comprehensive approach to securing data across the TCP/IP model. This paper presents a novel cybersecurity investment strategy formulated as a bi-objective optimization problem, validated through genetic and iterative algorithms. The strategy effectively balances security and cost, achieving nearly 50% efficiency in solution effectiveness. By utilizing these optimization techniques, the approach provides a practical and cost-effective solution to improve IIoT security within budget constraints, offering valuable insights for cybersecurity professionals seeking robust and economically viable solutions.

Monitoring natural environments is currently a hot research topic, as the early detection of an anomaly or the presence of contaminants in such a setting allows for mitigating environmental damage. In this regard, these tasks have been carried out using nodes that executed code sequentially, mainly due to their inability to execute multiple threads. However, in this article, we propose the collaborative use of sensor nodes within a network, so that a single node can be multipurpose and provide service by monitoring different sets of variables for the same environment. To this end, we propose using autonomic computing, a self-management mode for nodes that allows them to interact with each other and execute different codes as needed. After testing the system, it has been confirmed that this type of operation generates significant energy savings for each node in the network and that, ultimately, it is possible to work with a smaller number of nodes, which implies consequent economic savings.

The rapid expansion of connected devices has ushered in the Internet of Everything (IoE), enabling seamless integration among machines, people, and systems across diverse applications. However, the IoE faces significant challenges in ensuring efficient, reliable, and energy-conscious data transmission at scale. To address these issues, we present CLIC-IoE (Cross-Layer Solutions to Improve Communications under IoE), an innovative cross-layer framework designed to significantly enhance communication performance within IoE environments. By intelligently coordinating multiple communication layers, CLIC-IoE achieves remarkable results: a 39.47% reduction in data errors, a 38.33% increase in delivery rates, and a decrease of 0.8 nanoseconds in end-to-end delays. Additionally, it optimizes energy consumption, demonstrating a 51.67% improvement in energy efficiency (CEA) and a 20% boost in Active Things Rate (ATR). These advancements position CLIC-IoE as a transformative solution that enhances the scalability and reliability of IoE systems while promoting sustainable energy use. This manuscript provides a comprehensive exploration of the CLIC-IoE architecture, algorithms, and performance evaluation, emphasizing its potential impact on future IoE deployments. By addressing the critical challenges faced in IoE environments, CLIC-IoE not only enhances communication performance but also paves the way for more sustainable and efficient IoT systems.

The research proposes a method to enhance the accuracy of resistance measurements for Resistive Sensors (R-x) using a voltage divider and Anderson current loop circuits connected to the analog-to-digital converter (ADC) of a microcontroller. Traditional circuits use a fixed reference resistor (FRR) (R-ref), which causes significant errors when the sensor's voltage drop differs greatly from the reference resistor. In order to address this, an adaptive reference resistor (ADRR) using a digital potentiometer (DPOT) is introduced. The voltage drop across the resistive sensor is used to determine the control code for the DPOT. This adjusts the reference resistance to be close to the sensor resistance. The introduction of the Tuning Factor (k) for calibrating the measurement system, moreover, significantly reduces errors. The experiment used an MCP41010 DPOT with 256 steps, whose resistance was measured and calibrated by a factor, k. Known resistances were used for accuracy testing within the range of 100-9960 Omega. According to the results, setting the reference resistance interval to 100 St led to an error of less than 0.45% for the voltage divider circuit across the measurement range. Meanwhile, the series circuit based on the Anderson current loop demonstrated an error of less than 0.35%.

Blockchain strengthens reliable collaboration among entities through its transparency, immutability, and traceability, leading to its integration into Multi-access Edge Computing (MEC) and promoting the development of a trusted JointCloud. However, existing transaction propagation mechanisms require MEC devices to consume significant computing resources for complex transaction verification, increasing their vulnerability to malicious attacks. Adversaries can exploit this by flooding the blockchain network with spam transactions, aiming to deplete device energy and disrupt system performance. To cope with these issues, this paper proposes a reputation-based energy-efficient transaction propagation mechanism that alleviates spam transaction attacks while reducing computing resources and energy consumption. Firstly, we design a subjective logic-based reputation scheme that assesses node trust by integrating local and recommended opinions and incorporates opinion acceptance to counteract false evidence. Then, we optimize the transaction verification method by adjusting transaction discard and verification probabilities based on the proposed reputation scheme to curb the propagation of spam transactions and reduce verification consumption. Finally, we enhance the transaction transmission strategy by prioritizing nodes with higher reputations, enhancing both resilience to spam transactions and transmission reliability. A series of simulations demonstrates the effectiveness of the proposed mechanism.