Massive Internet of Things (IoT) deployments face critical spectrum crowding and energy scarcity challenges. Energy harvesting (EH) symbiotic radio (SR), where secondary devices share spectrum and harvest energy from non-orthogonal multiple access (NOMA)-based primary systems, offers a sustainable solution. We consider long-term throughput maximization in an EHSR network with a nonlinear EH model. To solve this non-convex problem, we designed a two-layered optimization algorithm combining convex optimization with a deep reinforcement learning (DRL) framework. The derived optimal power, time allocation factor, and the time-varying environment state are fed into the proposed long short-term memory (LSTM) attention mechanism combined Deep Deterministic Policy Gradient, named the LAMDDPG algorithm to achieve the optimal long-term throughput. Simulation results demonstrate that by equipping the Actor with LSTM to capture temporal state and enhancing the Critic with channel-wise attention mechanism, namely Squeeze-and-Excitation Block, for precise Q-evaluation, the LAMDDPG algorithm achieves a faster convergence rate and optimal long-term throughput compared to the baseline algorithms. Moreover, we find the optimal number of PDs to maintain efficient network performance under NLPM, which is highly significant for guiding practical EHSR applications.

The Internet of Things (IoT) model possesses much flexibility and mobility, hence the IoT system becomes more prone to security threats such as Distributed Denial-of-Service (DDoS) attacks due to its decentralized control, dynamic node moving, energy scarces and bandwidth limited. This development has demonstrated the capabilities of AI in providing improvements to performance of IoT network in terms of achieving faster rate, provide more throughput and achieve higher packet delivery ratio. Utilization of AI-based analytics technology with the use of adaptive methods can improve metrics such as End to End delay (E2E) and Average Received Packets (ARP), by improving response time and increasing intelligence in Intrusion Detection Systems in IoT based setups. In this paper, we use Feedforward Neural Networks (FFNN) and Convolutional Neural Networks (CNN) to detect malicious activities which can strengthen the capabilities of IDS in IoT routing. The suggested AI-optimized routing model outperforms existing models in detection accuracies, which are improved up to 82% and 85% with respective processing time of 18s and 17s. These findings demonstrate the potential to significantly improve security of IoT systems along with increasing overall network robustness and efficiency using this framework.

Hybrid CNN-LSTM model for ECG-based arrhythmia detection in internet of medical things

This study aims to explore the urgency of character education in the Industrial Revolution 4.0 era, characterized by the rapid development of digital technologies such as artificial intelligence, the Internet of Things (IoT), and big data. These changes provide significant opportunities for educational innovation, but also give rise to new challenges, such as the degradation of students' character and the dominance of technical skills over moral values. Using a qualitative approach using literature study methods, this study analyzes theories, concepts, and research findings related to character education in the global and Indonesian contexts. The results show that character education is a key element in preparing the younger generation to face the social and ethical challenges presented by technology. Integrating values ​​such as honesty, responsibility, empathy, and digital ethics into the curriculum is crucial for shaping individuals who are not only technically skilled but also possess a solid moral foundation. Character education acts as a moral bulwark that helps students overcome the negative impacts of technology, such as digital addiction and data misuse. In conclusion, character education should not be merely an addition to the curriculum, but must become a central pillar in shaping a generation ready to compete globally while upholding noble values. In the face of rapid change, character education must be strategically designed to strengthen individual moral identity and promote social harmony in the digital era.

The use of Artificial Intelligence (AI) together with the Internet of Things (IoT) in smart healthcare represents a promising direction for innovation. Recent progress in Deep Learning techniques has significantly advanced the automation of diagnostic and classification systems. Furthermore, the emergence of 5G wireless networks has provided faster and more reliable data transmission, accelerating the deployment of intelligent healthcare solutions. The COVID-19 pandemic highlighted the critical importance of such systems. Voice disorders affect a large number of individuals worldwide, yet early detection can make them highly treatable. In this study, we introduce a smart healthcare framework for the detection of voice pathology. Voice and electroglottography (EGG) signals are recorded using IoT-enabled devices, including microphones and EGG sensors. These signals are converted into Mel-spectrograms and analyzed through pre-trained convolutional neural networks (CNNs). The extracted features are then integrated using a bidirectional LSTM (BiLSTM) network. The proposed framework is evaluated with the Saarbrücken speech database. Experimental results indicate that bimodal inputs provide better performance than unimodal ones, achieving a classification accuracy of 95.65%.

Abstract The monitoring and understanding of animal health and emotional well-being are critical for effective animal welfare, agriculture, conservation, and the development of compassionate human-animal relationships. Recent advances in the Internet of Things (IoT), artificial intelligence (AI), and data science have transformed traditional animal care by enabling continuous, automated monitoring of health, behavior, and emotional states. This paper presents an introductory survey of the integration of IoT technologies for predicting animal health and emotion. We emphasize the current state-of-the-art, challenges, and emerging applications, building upon a review of recent literature in this growing interdisciplinary field.

This paper presents the design, implementation, and validation of MediGlove, a smart rehabilitation glove that integrates gamified therapy with gesture-based Internet of Things (IoT) control to address limitations in traditional hand motor rehabilitation. The system employs a sensor fusion approach, utilizing five flex sensors for finger flexion tracking and an MPU6050 Inertial Measurement Unit (IMU) for wrist movement, all processed by an ESP32 microcontroller. The processed data enables two primary functionalities: it drives an interactive Unity-based game environment for engaging therapeutic exercises and triggers a relay module for controlling smart home appliances via predefined gestures. Experimental results demonstrate the system's efficacy, achieving high-fidelity motion tracking, real-time responsiveness with latency below 100 ms, and zero cross-interference between control zones. MediGlove provides a cost-effective, portable, and patient-centric solution that enhances motivation through gamification, offers assistive IoT control, and establishes a foundation for remote progress monitoring, demonstrating significant potential for modernizing physical therapy protocols.

The Internet of Things is an intelligent network of cognitive sensors that are embedded into nearby objects and appliances to create a connected environment. It collects, processes, and transmits data for automation and decision-making while providing ubiquitous services. Sensors create primarily redundant data, with several repeating bit streams and matching patterns. Due to resource restrictions, sensors are carefully scheduled to activate and deactivate sensing operations. As a result, it is highly recommended to create schemes that can handle large volumes of data while maintaining a careful strategy for data collection. This article has identified and analyzed various approaches for comprehensively scheduling cognitive sensors and then evaluated the efficiency of the three schemes, CADS, EASS, and EDASS. Based on these schemes, a hybrid approach is proposed. The hybrid scheme, "Hybrid of CADS, EASS, and EDASS" (HCEE), is recommended to be used in those areas where each scheme is implemented and evaluated for applicability individually. Using the best features of these schemes, one combined scheme can be used to provide better results than those three in their individual forms. The basic three systems were evaluated with HCEE, for communication overhead and latency, node processing and buffering, the number of messages in the network, and energy usage in various scenarios. Every scheme has its primary goal, based on specific factors and a unique set of applications. Each of them exhibits unique behavior and has a variety of uses, but using one scheme in all these scenarios has achieved better results than any of these schemes. In performance analysis, CADS and EASS are more energy efficient with 67.58 and 64.25 mean values, respectively. HCEE has an extra message overhead of 6.61% compared to the average of the three schemes. HCEE has a transmission delay of 60.07% compared to other systems, and requires less buffer than CADS but more than EASS and EDASS.

The exponential growth of sophisticated cyber threats in Internet of Things (IoT) environments has exposed fundamental weaknesses in existing Cyber Threat Intelligence (CTI) platforms, including centralized architectures, trust deficits, privacy vulnerabilities, and single points of failure. To overcome these limitations, this paper proposes BlockIntelChain, a blockchain-based framework for secure, scalable, and collaborative CTI sharing across distributed IoT networks. The system integrates a hybrid consensus mechanism that combines Proof-of-Stake with reputation-based validator selection, supported by a multi-layered privacy framework employing Differential Privacy (DP), Zero-Knowledge Proofs (ZKP), Homomorphic Encryption, and Secure Multi-Party Computation. BlockIntelChain further embeds Federated Learning (FL) to enable distributed model training directly on IoT edge nodes without exposing raw threat telemetry. Comprehensive evaluations on real-world Malware Information Sharing Platform (MISP) datasets show that BlockIntelChain achieves 923 Transactions per Second at 500 nodes with 99.6% consensus success, while maintaining resilience against 51% and Byzantine attacks tolerating up to 33% malicious validators. Privacy analysis confirms an optimized utility-privacy trade-off, with DP (ε = 0.1) preserving 92% data utility and ZKP achieving 94% verification accuracy. The FL-based models outperform centralized baselines, reaching 96.4% accuracy for IoT malware classification, 94.7% for phishing detection, and 95.2% for network anomaly identification. Economic modeling validates sustainability through contributor growth (156 → 1,245 in 12months) and improved contribution quality (0.73 → 0.92). The proposed framework directly benefits Security Operation Centers and edge-deployed IoT systems by enabling real-time threat intelligence exchange with strong security, privacy, and efficiency. Comparative benchmarking demonstrates BlockIntelChain's superiority over MISP, ThreatConnect, and IBM X-Force in decentralization, privacy, and cost efficiency, positioning it as a transformative solution for next-generation privacy-aware CTI ecosystems.

In the evolving landscape of Internet of Things (IoT), the integration of interconnected devices and cloud computing has revolutionized data collection and processing. However, this connectivity poses numerous security challenges about data privacy, integrity, and security. Traditional cloud-based security approaches inadequate for managing the distributed and dynamic nature of IoT ecosystems. The emergence of the edge computing paradigm allowed for the transfer of data processing and storage closer to local edge devices, but introduces new vulnerabilities at the edges. Thus, an Intrusion Detection System (IDS) is required in this situation. IDS built at the edge can quickly detect and mitigate possible attacks by continually monitoring network traffic, device interactions, and real-time anomalies. Therefore, in this study, we propose an Enhanced Deep Learning (DL)-based IDS integrated with a Blockchain-Based Cryptographic-Algorithm to ensure secure data transmission in an IoT edge computing environment. Initially, the intrusion dataset undergoes preprocessing step to enhance its quality by eliminating unnecessary data and normalizing the dataset. then, the pre-processed data is classified using an Enhanced Capsule Network (ECaps-Net), which incorporates a Squeeze and Excitation (SE) block to highlight important features and surpasses less important ones. After classification, the classified normal data is converted into blocks using Blockchain technology. Every block is hashed using the Merkle-Damgard cryptographic algorithm to ensure data integrity and confidentiality. The proposed framework outperformed existing methods with a maximum accuracy of 98.90% and 98.78% on the KDD Cup-99 and UNSW-NB 15 datasets, respectively. The proposed mechanism protects cloud server and edge devices from malicious access, offering a reliable and efficient solution for secure data transmission in IoT edge environments.

Multimodal sensor data, integrating signals such as RGB, LiDAR, and IMU, plays a pivotal role in enabling intelligent decision-making in real-time Internet of Things (IoT) systems. However, these data streams are inherently prone to complex noise patterns, cross-sensor inconsistencies, and scaling disparities that conventional preprocessing techniques often fail to address comprehensively. This paper presents a hybrid data preprocessing framework that unifies advanced denoising and adaptive normalization in a single, context-aware pipeline. The framework leverages wavelet-based denoising for high-frequency noise suppression, Kalman filtering for dynamic state estimation, and a real-time adaptive normalization mechanism that calibrates data scaling based on temporal and environmental contexts. Evaluations on synchronized multimodal IoT datasets comprising RGB, LiDAR, and IMU recordings under low-light, high-noise, and adverse-weather conditions (≈ 18,000 aligned samples; 30 Hz, 10 Hz, 100 Hz) show significant performance gains. Results indicate a 30.4% RMSE reduction (p < 0.05), 33% faster convergence, and only 34% computational overhead, while maintaining real-time feasibility with a 41 ms latency per frame. These findings confirm that combining complementary denoising paradigms with adaptive, context-driven normalization enhances signal fidelity and responsiveness in dynamic sensing environments. This contribution presents a reproducible, statistically validated hybrid preprocessing framework for enhancing the quality of multimodal sensor data, enabling more reliable deployments in industrial automation, environmental monitoring, and intelligent transport systems.

The development of IoT devices of various kinds has caused the amount of data generated to be explosive, raising several challenging issues, such as dynamic attack patterns for threat prediction, data privacy issues, and constrained computing resources at the network edge. Central IDS and Static machine learning models do not scale, adapt to new threats, or maintain the confidentiality and integrity of the data. However, most federated learning mechanisms lack trust in model aggregation and fail to prioritise interpretability. As such, a real-time, secure and explainable IoT threat analytics is a research need. To alleviate these limitations, this work proposes ThreatFedChainAI, an architecture dedicated to threat anticipation and detection for IoT that integrates an edge-blockchain. The proposed model employs a quantum-inspired particle swarm optimisation algorithm to solve the feature selection problem and filters out irrelevant features from edge nodes by reducing data dimensionality with minimal loss of robbery-related information. An adaptive federated learning method is combined with blockchain smart contract validation to ensure secure, tamper-proof, and privacy-preserving model updating. In addition, SHAP-based interpretability techniques are utilised to increase the explainability of model predictions. Experimental results on the CICIDS2017 and TON_IoT datasets show that our ThreatFedChainAI outperforms baseline models by up to 5.3% in accuracy and that F1-scores are consistently above 97%. The effectiveness of the proposed system is validated through ablation studies, and visualisations and tables are presented for interpretation. Overall, the proposed system introduces a scalable, secure, and interpretable approach for real-time IoT threat detection that addresses first-rank issues in privacy, trust, and adaptability. This makes ThreatFedChainAI ideally suited for deploying at scale in mission-critical IoT networks.

With the exponential enhancement of 6G network performance, its energy consumption has become a core challenge constraining sustainable development. This paper systematically surveys the research progress of Deep Reinforcement Learning (DRL) in the field of 6G energy saving, focusing on three key technologies: resource scheduling, power control, and sleep strategies. It analyzes the application effects in typical scenarios such as ultra-dense urban networks, Space Air Ground Sea Integrated Networks (SAGIN), and Industrial Internet of Things (IIoT). Our analysis indicates that DRL, utilizing its autonomous decision-making and optimization capabilities, can effectively address the high dynamics and resource coupling inherent in 6G environments. However, bottlenecks still exist, including policy lag and long-term credit assignment. Future research directions need to make breakthroughs in dynamic coordination of renewable energy sources, lightweight engineering deployment, and cross-scenario generalization through meta-learning. This survey an attempt to construct a DRL driven 6G energy-saving technology system framework, providing a theoretical foundation for academia and industry to collaboratively advance high-performance, low-energy-consumption 6G networks.

This article addresses a critical security challenge in Internet of Things (IoT) systems, which are vulnerable to traffic detection attacks due to their reliance on shared wireless communication channels. We propose a novel cooperative covert transmission strategy to enhance the security of IoT communications against these attacks through the implementation of physical-layer security mechanisms. Inspired by zero-forcing precoding techniques, the proposed approach enables cooperation between different IoT devices in the system to increase the likelihood of adversaries making incorrect conclusions about the communication activity of the targeted IoT device. The proposed covert communication strategy complements traditional security measures, provides a scalable solution, and is suitable for resource-constrained IoT environments. The numerical results in this article demonstrate significant improvements in protecting communications against traffic detection attacks, which contributes to the overall security and privacy of IoT systems.

Soil health plays a fundamental role in ensuring sustainable agricultural productivity and ecological stability. Traditional methods of soil health assessment such as manual sampling and laboratory analyses are labour-intensive, time-consuming, and limited in their spatial and temporal resolution. With the growing demands of food production and the pressing threats posed by climate change and land degradation, there is an urgent need for real-time, cost-effective, and scalable soil health monitoring systems. The Internet of Things (IoT) has emerged as a transformative solution, offering continuous, remote, and sensor-based monitoring of key soil parameters. This review presents an in-depth examination of IoT-linked technologies and their application in assessing soil physical, chemical, and biological properties. The paper also explores the architectural framework of IoT systems, including sensing, communication, data processing, and application layers. Further, it discusses recent case studies involving smart irrigation systems, real-time nutrient monitoring, and integration with unmanned aerial vehicles (UAVs). While the benefits of IoT-based systems are substantial including increased efficiency, reduced labour, and improved decision-making several challenges remain, such as high initial costs, energy requirements, and limited connectivity in rural areas. Overall, IoT presents promising potential for revolutionizing soil health monitoring and promoting resilient and sustainable agricultural systems.

Purpose. This study aims to assess the impact of Industry 4.0 technologies - specifically Big Data, Internet of Things (IoT), collaborative robots, and Cyber-Physical Systems (CPS) - on the financial performance of manufacturing companies in Cameroon, addressing the research gap in the Sub-Saharan context. Methodology. Adopting a quantitative approach, primary data were collected via questionnaires from 104 manufacturing firms. The study employed Chi-square tests and binary logistic regression to analyse the relationship between technological adoption and key performance indicators, including Return on Assets (ROA), Return on Equity (ROE), turnover, and productivity. Results. The empirical findings indicate that integrating Big Data and IoT has a statistically significant positive effect on all measured financial indicators. Collaborative robots positively impact turnover, whereas Cyber-Physical Systems showed no significant correlation with financial performance in the studied context. The theoretical contribution. This research extends economic production theory to developing economies. It provides empirical evidence that digital transformation serves as a critical production input, significantly enhancing firm output and challenging the “IT productivity paradox” in African manufacturing sectors. Practical implications. The study suggests that manufacturing leaders in developing regions should prioritise investments in Big Data and IoT for immediate efficiency gains. Furthermore, it advocates for government-led subsidy policies to lower entry barriers for automation and foster international competitiveness. Sustainable Development Goals (SDGs): SDG 8: Decent Work and Economic Growth; SDG 9: Industry, Innovation and Infrastructure

Purpose: This study examines the extent to which digital technologies - including artificial intelligence (AI), Internet of Things (IoT), blockchain, and predictive analytics - contribute to sustainability outcomes in Dubai's retail sector. Methodology: A mixed methods approach was employed. Quantitative data were collected from 100 retail professionals in Dubai's retail industry and analysed through Pearson's correlation, Chi-square, Friedman, and Spearman tests. Comparative case studies of five international retailers (IKEA, Inditex/Zara, Tesco, Carrefour, and Unilever) contextualised survey findings. Results: The analysis reveals a strong positive correlation between technology adoption and sustainability performance (r = 0.823, p < 0.001). Integration complexity (84%) and workforce skill gaps (79%) constitute the most significant barriers. Case evidence substantiates the effectiveness of phased implementation approaches, employee training investments, and blockchain-enabled traceability systems. Contribution: The research extends the Technology-Organisation-Environment framework to Gulf economies, demonstrating that technology adoption is driven by consumer and competitive forces rather than regulatory compliance. Practical implications address retailer and policy responses facilitating digital transformation aligned with UN Sustainable Development Goals (SDG 9, SDG 12, SDG 17). Sustainable Development Goals (SDGs): SDG 9: Industry, Innovation and Infrastructure; SDG 12: Responsible Consumption and Production; SDG 17: Partnerships for the Goals

The Internet of Things (IoT) has improved efficiency and quality of life by connecting devices to the internet. It has seen success in areas such as smart vehicles and Unmanned Aerial Vehicles (UAVs), but faces processing limitations due to the need to send large amounts of data to other devices for processing. When heavy processing is required, it uses offloading techniques to send the data to other devices for processing. Secure data offloading transmission remains a fundamental challenge in this field. This paper presents an innovative authentication and key exchange method that uses Elliptic Curve Cryptography (ECC) and incorporates Handover for secure offloading, offering a safe, lightweight solution within a blockchain network. To evaluate the resistance of the proposed scheme against active and passive attacks, we employed the AVISPA tool to apply both formal and informal methods. Subsequently, to demonstrate the scheme's lightweight nature, we examined it with respect to computation and communication costs, the number of bits used, and security requirements. Additionally, we simulated the proposed scheme using the NS3 tool in two scenarios: urban and highway, with varying numbers of vehicles. The results indicate that the proposed scheme performs acceptably in urban and highway scenarios.

Research Background: Globalisation and rapid technological change are reshaping manufacturing and trade. Industry 4.0, underpinned by cyber-physical production systems, the Internet of Things (IoT), and artificial intelligence, is pivotal to this transformation. In Slovakia, the automotive sector is a national export pillar, while small and medium enterprises (SMEs) underpin the economy. Recent studies have separately examined advanced vision and sensing in automotive production, wireless networks and smart manufacturing for export growth, and barriers to AI and robotics adoption in SMEs. However, an integrated analysis of how these digital innovations collectively drive Slovak Industry 4.0, spanning both the automotive value chain and SME contexts, is lacking. Purpose of the article: This article consolidates and extends findings from three prior manuscripts to provide a unified, in-depth examination of Industry 4.0 applications in Slovakia. We analyse how computer vision, remote sensing, and data fusion enhance automotive manufacturing and supply chains; how wireless and cyber-physical systems accelerate export value-add; and how machine intelligence and autonomous robotics address SMEs’ operational gaps. The goal is to deepen the analysis of digital transformation in Slovak industry, identify synergies and shortfalls, and propose strategic directions. Methodology: We conducted a comprehensive secondary data analysis and case study synthesis. Data sources included governmental and EU statistics, industry reports, and prior survey data. We re-examined datasets from all three studies, including Slovak export and value-added statistics, foreign direct investment (FDI) structures, and automobile manufacturer supply-chain data, combining statistical and visual analysis techniques. Graphical analytics from the previous case study of PSA Group Slovakia were retained (supply-chain graphs for Citroën C3 and Peugeot 208 vehicles), and new charts were created from the same data (e.g., bar charts of FDI by sector and country). All original tables and figures are preserved for reference. We also synthesised qualitative insights from literature reviews across global value chains, Industry 4.0 frameworks, and SME adoption studies. Findings and value added: The analysis reveals that advanced sensing, AI, and network technologies can substantially raise Slovakia’s export value-added. In the automotive sector, Industry 4.0-driven computer vision and IoT platforms are integral to smart factories and connected vehicle networks. Slovakia ranks second in added value for key car models, but its national R&D base lags, threatening future competitiveness. Wireless networks and cyber-physical systems are shown to accelerate high-value exports, particularly in the automotive sector, but Slovakia’s integration level (DESI index) is moderate. For SMEs, deep learning and robotics promise process optimisation, but financial and skills gaps hinder adoption. Notably, the lack of skilled labour is cited as a more critical barrier than financing for SMEs. This synthesis highlights that combining Industry 4.0 elements, from autonomous vision and data fusion in cars to smart manufacturing networks, can generate new sources of competitiveness, but requires coordinated investment in R&D, workforce development, and supportive innovation policies. The value of this contribution is an original, holistic framework linking Industry 4.0 technologies with value chain enhancement in the Slovak context, along with concrete policy and managerial recommendations (e.g., establishing an “Intelligent Industry Platform” and targeted innovation incentives). Sustainable Development Goals (SDGs): SDG 7: Affordable and Clean Energy; SDG 9: Industry, Innovation and Infrastructure; SDG 13: Climate Action

Smart farming is an emerging approach that integrates advanced technologies to revolutionize traditional agricultural practices. It leverages tools like the Internet of Things (IoT), Artificial Intelligence (AI), drones to improve productivity, sustainability, and decision-making in agriculture. In India, where over 70% of the rural population depends on farming, precision agriculture offers a powerful solution to challenges faced by farmers and also to adopt the climate changes. IoT sensors enable real-time monitoring of soil moisture, weather, and crop health, optimizing irrigation and input use. AI-driven analytics support early detection of pests and diseases and guide farmers on crop management strategies. Smart farming significantly boosts crop yield, reduces resource wastage, and improves market access for farmers. Customized models for small and large landholders ensure affordability and scalability. Real-world implementations like Harita-Priya, AgSpeak, Cropin, and AgriRain have shown measurable success in improving yields and farmer incomes, which was included as uses case in this paper. This paper attempts to address the needs of farmers of below 5 acres and above 5 acres and suggests the smart farming technology required and cost of implementation suits to both categories of farmers groups. As the country advances toward a digital agricultural ecosystem, smart farming will be pivotal for achieving food security and climate resilience and marks the beginning of a transformative era in Indian agriculture, promoting environmental sustainability.