Search Results (4,900)

This study investigates the vulnerability of Internet of Things (IoT) devices to climate change in Kazakhstan, where extreme seasonal variability and rising climate risks threaten device reliability. Using high-resolution climate projection data from ERA5 and CMIP6 models (RCP4.5 and RCP8.5 scenarios), combined with qualitative interviews with stakeholders in agriculture, energy, transport, and urban infrastructure, we develop risk assessment models for IoT systems. The analysis quantifies device failure probabilities through temperature and humidity thresholds and extends risk curves to include additional climatic stressors such as solar radiation, wind, and snowfall. Results reveal that IoT devices face heightened risks in northern regions during extreme cold events (below −40 °C) and in southern regions during prolonged heatwaves (above +40 °C). Interviews confirm that maintenance, power supply reliability, and device calibration remain major concerns under harsh climate conditions. The findings provide evidence-based recommendations for adaptation strategies, including resilient hardware design, predictive maintenance protocols, and climate-informed deployment planning. This research contributes to the emerging field of climate-resilient IoT, offering both methodological advances and practical insights for policymakers and infrastructure planners in Central Asia. Full article

Location-based services (LBSs) are a crucial element of the Internet of Things (IoT) and have garnered significant attention from both researchers and users, driven by the rise of wireless devices and a growing user base. However, the use of LBS-enabled applications carries several risks, as users must provide their real locations with each query. This can expose them to potential attacks from the LBS server, leading to serious issues like the theft of personal information. Consequently, protecting location privacy is a vital concern. To address this, location dummy-based methods are employed to safeguard the location privacy of LBS users. However, location dummy-based approaches also suffer from problems such as low resistance against inference attacks and the generation of strong dummy locations, an issue that is considered an open problem. Moreover, generating many location dummies to achieve a high privacy protection level leads to high network overhead and requires high computational capabilities on the mobile devices of the LBS users, and such devices are limited. In this paper, we introduce the Caching-Aware Double-Dummy Selection (CaDDSL) algorithm to protect the location privacy of LBS users against homogeneity location and semantic location inference attacks, which may be applied by the LBS server as a malicious party. Then, we enhance the CaDDSL algorithm via encapsulation with agents to solve the tradeoff between generating many dummies and large network overhead by proposing the Cache-Aware Overhead-Aware Dummy Selection (CaOaDSL) algorithm. Compared to three well-known approaches, namely GridDummy, CirDummy, and Dest-Ex, our approach showed better performance in terms of communication cost, cache hit ratio, resistance against inference attacks, and network overhead. Full article

The growing need for sustainable energy practices necessitates technology-driven interventions that can effectively bridge the disparity between consumer intentions and actual behavior. This paper formulates and empirically substantiates an IoT-enabled digital nudge architecture designed to promote sustainable energy behavior. The architecture provides goal-setting, social comparison, feedback, and informational nudges across multiple digital channels, utilizing linked devices, data processing layers, and a rule-based nudge engine. An 815-responder survey was analyzed using structural equation modeling with partial least squares (SEM-PLS) to identify the drivers of sustainable energy behavior and explore technology readiness as a moderating factor. The results show that nudges utilizing the Internet of Things (IoT) significantly enhance the alignment between intention and behavior. Goal-setting and feedback mechanisms have the highest effects. The findings also demonstrate that being ready for new technology improves nudge response, highlighting the importance of user-centered system design. This paper presents a scalable infrastructure for integrating IoT into sustainability projects, as well as theoretical contributions to technology adoption and behavioral intervention research. The study enhances the dialogue on environmental technology by illustrating the implementation of digital nudges through IoT infrastructures to expedite progress toward the Sustainable Development Goals (SDGs). Full article

Prehabilitation programs for abdominal pre-operative patients are increasingly recognized for improving surgical outcomes, reducing post-operative complications, and enhancing recovery. Internet of Things (IoT)-enabled human movement monitoring systems offer promising support in mixed-mode settings that combine clinical supervision with home-based independence. These systems enhance accessibility, reduce pressure on healthcare infrastructure, and address geographical isolation. However, current implementations often lack personalized movement analysis, adaptive intervention mechanisms, and real-time clinical integration, frequently requiring manual oversight and limiting functional outcomes. This review-based paper proposes a conceptual framework informed by the existing literature, integrating Digital Twin (DT) technology, and machine learning/Artificial Intelligence (ML/AI) to enhance IoT-based mixed-mode prehabilitation programs. The framework employs inertial sensors embedded in wearable devices and smartphones to continuously collect movement data during prehabilitation exercises for pre-operative patients. These data are processed at the edge or in the cloud. Advanced ML/AI algorithms classify activity types and intensities with high precision, overcoming limitations of traditional Fast Fourier Transform (FFT)-based recognition methods, such as frequency overlap and amplitude distortion. The Digital Twin continuously monitors IoT behavior and provides timely interventions to fine-tune personalized patient monitoring. It simulates patient-specific movement profiles and supports dynamic, automated adjustments based on real-time analysis. This facilitates adaptive interventions and fosters bidirectional communication between patients and clinicians, enabling dynamic and remote supervision. By combining IoT, Digital Twin, and ML/AI technologies, the proposed framework offers a novel, scalable approach to personalized pre-operative care, addressing current limitations and enhancing outcomes. Full article

The proposed forest monitoring photo trap ecosystem integrates a cost-effective architecture for observation and transmission using Internet of Things (IoT) technologies and long-range digital radio systems such as LoRa (Chirp Spread Spectrum—CSS) and nRF24L01 (Gaussian Frequency Shift Keying—GFSK). To address low-bandwidth links, a novel approach based on the Monte Carlo sampling algorithm enables progressive, bandwidth-aware image transfer and its thumbnail’s reconstruction on edge devices. The system transmits only essential data, supports remote image deletion/retrieval, and minimizes site visits, promoting environmentally friendly practices. A key innovation is the integration of no-reference image quality assessment (NR IQA) to determine when thumbnails are ready for operator review. Due to the computational limitations of the Raspberry Pi 3, the PIQE indicator was adopted as the operational metric in the quality stabilization module, whereas deep learning-based metrics (e.g., HyperIQA, ARNIQA) are retained as offline benchmarks only. Although single-pass inference may meet initial timing thresholds, the cumulative time–energy cost in an online pipeline on Raspberry Pi 3 is too high; hence these metrics remain offline. The system was validated through real-world field tests, confirming its practical applicability and robustness in remote forest environments. Full article

Particulate matter (PM) poses serious health risks, including respiratory and cardiovascular diseases, and is classified as a carcinogen by the World Health Organization and International Agency for Research on Cancer. Roadside air pollution, which is strongly affected by traffic emissions, is a major contributor to urban air quality deterioration. This study investigated the feasibility of establishing a low-cost, Internet of Things (IoT)-based, high-density monitoring network for roadside PM₁₀ and PM_2.5 to support safer and more sustainable road environments. We developed low-cost IoT sensing devices, deployed them at three urban roadside sites with different environmental conditions, and compared their performances with those of nearby public monitoring stations. One-minute resolution data were analyzed using Pearson correlation, cross-correlation, dynamic time warping, Z-score, and the roulette index. The IoT sensor data were strongly correlated with public station data, confirming its reliability as a complementary observation method. Notable site-specific patterns were sharp concentration increases with traffic at an intersection and distinct diurnal and weekly cycles at residential and rooftop sites. These findings demonstrate that low-cost IoT sensing can complement sparse public networks by providing microscale air quality information. This approach offers a practical foundation for smart city development and intelligent roadside environmental management. Full article

Mitigating the consequences of flash rainfall has become essential for the safety of populations and the promotion of local tourism. A non-structural measure could involve a sensor-based nowcasting system to detect increasingly frequent and intense rainfall events driven by climate change. Therefore, developing wide-range, connected, cheap, small, and easy-to-install rain gauges is desirable. To achieve a useful network of monitoring, a set of technologies such as electrodynamic sensor devices supported by real-time processing and the Internet of Things is proposed. This comparative investigation aimed to evaluate the implementation-friendly network of small, low-cost, solid-state pluviometers for near-real-time monitoring of an early warning system. The ability of a recent patent to provide cumulative rainfall estimates every ten seconds was evaluated for river system flooding, which extends the warning time by 3–4 min in a 1 km² basin. Our results found that even with a rainfall uncertainty of 10%, a network of these new instruments reduced errors in flood wave severity and time estimations. Moreover, intensity–duration thresholds of landslide triggering and debris movements can be modified by flash rainfalls. Specifically, coastal areas with high-density populations can greatly benefit from this solution. Full article

Artificial Intelligence (AI) is a key technological enabler for the transition of agricultural production and management from experience-driven to data-driven, continuously advancing modern agriculture toward smart agriculture. This evolution ultimately aims to achieve a precise agricultural production model characterized by low resource consumption, high safety, high quality, high yield, and stable, sustainable development. Although machine learning, deep learning, computer vision, Internet of Things, and other AI technologies have made significant progress in numerous agricultural production applications, most studies focus on singular agricultural scenarios or specific AI algorithm research, such as object detection, navigation, agricultural machinery maintenance, and food safety, resulting in relatively limited coverage. To comprehensively elucidate the applications of AI in agriculture and provide a valuable reference for practitioners and policymakers, this paper reviews relevant research by investigating the entire agricultural production process—including planting, management, and harvesting—covering application scenarios such as seed selection during the cultivation phase, pest and disease identification and intelligent management during the growth phase, and agricultural product grading during the harvest phase, as well as agricultural machinery and devices like fault diagnosis and predictive maintenance of agricultural equipment, agricultural robots, and the agricultural Internet of Things. It first analyzes the fundamental principles and potential advantages of typical AI technologies, followed by a systematic and in-depth review of the latest progress in applying these core technologies to smart agriculture. The challenges faced by existing technologies are also explored, such as the inherent limitations of AI models—including poor generalization capability, low interpretability, and insufficient real-time performance—as well as the complex agricultural operating environments that result in multi-source, heterogeneous, and low-quality, unevenly annotated data. Furthermore, future research directions are discussed, such as lightweight network models, transfer learning, embodied intelligent agricultural robots, multimodal perception technologies, and large language models for agriculture. The aim is to provide meaningful insights for both theoretical research and practical applications of AI technologies in agriculture. Full article

Blockchain technologies offer transformative potential in terms of addressing the security, trust, and identity management issues that exist in large-scale Internet of Things (IoT) deployments. This narrative review provides a comprehensive survey of various studies, focusing on decentralized identity management, trust mechanisms, smart contracts, privacy preservation, and real-world IoT applications. According to the literature, blockchain-based solutions provide robust authentication through mechanisms such as Physical Unclonable Functions (PUFs), enhance transparency via smart contract-enabled reputation systems, and significantly mitigate vulnerabilities, including single points of failure and Sybil attacks. Smart contracts enable secure interactions by automating resource allocation, access control, and verification. Cryptographic tools, including zero-knowledge proofs (ZKPs), proxy re-encryption, and Merkle trees, further improve data privacy and device integrity. Despite these advantages, challenges persist in areas such as scalability, regulatory and compliance issues, privacy and security concerns, resource constraints, and interoperability. By reviewing the current state-of-the-art literature, this review emphasizes the importance of establishing standardized protocols, performance benchmarks, and robust regulatory frameworks to achieve scalable and secure blockchain-integrated IoT solutions, and provides emerging trends and future research directions for the integration of blockchain technology into the IoT ecosystem. Full article

Industrial Internet of Things (IIoT) systems face severe security and trust challenges, particularly under cross-domain data sharing and federated orchestration. We present QuantumTrust-FedChain, a cyber-resilient federated learning framework integrating quantum variational trust modeling, blockchain-backed provenance, and Byzantine-robust aggregation for secure IIoT collaboration in 6G networks. The architecture includes a Quantum Graph Attention Network (Q-GAT) for modeling device trust evolution using encrypted device logs. This consensus-aware federated optimizer penalizes adversarial gradients using stochastic contract enforcement, and a shard-based blockchain for real-time forensic traceability. Using datasets from SWaT and TON IoT, experiments show 98.3% accuracy in anomaly detection, 35% improvement in defense against model poisoning, and full ledger traceability with under 8.5% blockchain overhead. This framework offers a robust and explainable solution for secure AI deployment in safety-critical IIoT environments. Full article

The rapid proliferation of devices on the Internet of Things (IoT) in smart city environments enables autonomous decision-making, but introduces challenges of scalability, coordination, and privacy. Existing reinforcement learning (RL) methods, such as Multi-Agent Actor–Critic (MAAC), depend on centralized critics and recurrent structures, which limit scalability and create single points of failure. This paper proposes a Federated Decision Transformer (FDT) framework that integrates transformer-based sequence modeling with federated learning. By replacing centralized critics with self-attention-driven trajectory modeling, the FDT preserves data locality, enhances privacy, and supports decentralized policy learning across distributed IoT nodes. We benchmarked the FDT against MAAC in a mobile edge computing (MEC) environment with identical hyperparameter configurations. The results demonstrate that the FDT achieves superior reward efficiency, scalability, and adaptability in dynamic IoT networks, although with slightly higher variance during early training. These findings highlight transformer-based federated RL as a robust and privacy-preserving alternative to critic-based methods for large-scale IoT systems. Full article

Nowadays, Low-Power Internet of Things (LP-IoT) devices are widely utilized due to their affordability and low energy consumption. However, LP-IoT devices face significant security challenges, including data breaches, unauthorized access, and malicious attacks, due to their constrained hardware resources. These challenges are particularly critical in applications that involve the transmission of sensitive data. To enhance the security of LP-IoT devices, we propose a lightweight mutual authentication mechanism designed explicitly for LP-IoT devices. This mechanism utilizes simplified cryptographic operations to strike a balance between security requirements and resource constraints, thereby ensuring secure and reliable data transmission, as well as mutual authentication, between devices and servers. In addition, we demonstrate the potential of this mechanism in protecting data integrity and device security through a scenario in a financial technology application. Our proposed mechanism adapts to the characteristics of low-power devices while enhancing their security and practicality across different application environments, offering a secure and lightweight solution to the security challenges of LP-IoT devices. Full article

The increasing complexity of the Edge–Cloud Continuum (ECC), driven by the rapid expansion of the Internet of Things (IoT) and data-intensive applications, necessitates implementing innovative methods for automated and efficient system management. In this context, recent studies focused on the utilization of self-* capabilities that can be used to enhance system autonomy and increase operational proactiveness. Separately, anomaly detection and adaptive sampling techniques have been explored to optimize data transmission and improve systems’ reliability. The integration of those techniques within a single, lightweight, and extendable self-optimization module is the main subject of this contribution. The module was designed to be well suited for distributed systems, composed of highly resource-constrained operational devices (e.g., wearable health monitors, IoT sensors in vehicles, etc.), where it can be utilized to self-adjust data monitoring and enhance the resilience of critical processes. The focus is put on the implementation of two core mechanisms, derived from the current state-of-the-art: (1) density-based anomaly detection in real-time resource utilization data streams, and (2) a dynamic adaptive sampling technique, which employs Probabilistic Exponential Weighted Moving Average. The performance of the proposed module was validated using both synthetic and real-world datasets, which included a sample collected from the target infrastructure. The main goal of the experiments was to showcase the effectiveness of the implemented techniques in different, close to real-life scenarios. Moreover, the results of the performed experiments were compared with other state-of-the-art algorithms in order to examine their advantages and inherent limitations. With the emphasis put on frugality and real-time operation, this contribution offers a novel perspective on resource-aware autonomic optimization for next-generation ECC. Full article

The Internet of Medical Things (IoMT) comprises the application of traditional Internet of Things (IoT) technologies in the healthcare domain. IoMT ensures seamless data-sharing among hospitals, patients, and healthcare service providers, thereby transforming the medical environment. The adoption of IoMT technology has made it possible to provide various medical services such as chronic disease care, emergency response, and preventive treatment. However, the sensitivity of medical data and the resource limitations of IoMT devices present persistent challenges in designing authentication protocols. Our study reviews the overall architecture of the IoMT and recent studies on IoMT protocols in terms of security requirements and computational costs. In addition, this study evaluates security using formal verification tools with Scyther and SVO Logic. The security requirements include authentication, mutual authentication, confidentiality, integrity, untraceability, privacy preservation, anonymity, multi-factor authentication, session key security, forward and backward secrecy, and lightweight operation. The analysis shows that protocols satisfying a multiple security requirements tend to have higher computational costs, whereas protocols with lower computational costs often provide weaker security. This demonstrates the trade-off relationship between robust security and lightweight operation. These indicators assist in selecting protocols by balancing the allocated resources and required security for each scenario. Based on the comparative analysis and a security evaluation of the IoMT, this paper provides security guidelines for future research. Moreover, it summarizes the minimum security requirements and offers insights that practitioners can utilize in real-world settings. Full article

This paper presents the development of a modular and low-cost IoT (Internet of Things) system for remote monitoring of essential parameters during wine fermentation, designed for small and medium-sized wineries—Winnie. The system combines distributed embedded sensing units with centralized colorimetric analysis and real-time data transmission to a remote server. Barrel-mounted devices measure wine and cellar parameters (temperature, humidity, and CO₂ concentration), while a central hub performs colorimetric SO₂ analysis using an RGB color sensor and automated fluid handling. Communication between the Barrel and Hub device relies on the RS-485 protocol, providing robustness in harsh winery conditions. All measurements are securely transferred via Wi-Fi. A hash-based integrity check ensures continuous and reliable data collection. The modular design, simple installation, and user-friendly web interface make the system accessible to winemakers. This technology provides a scalable method for digitalizing conventional winemaking processes by reducing the cost and complexity of wine quality monitoring. Full article