Things Technology Research Articles

ABSTRACT This study examines how Thai food manufacturers adopt Internet of Things (IoT) technologies and progress through digital transformation. Guided by the Technology–Organization–Environment (TOE) framework and supported by Dynamic Capabilities, Institutional, and Stakeholder theories, survey data from 194 firms were analyzed using Latent Class Cluster Analysis (LCCA) in R. Five adoption stages emerged, from Limit-Adopters to End-to-End IoT Leaders. While early adoption depends on executive support and service providers, advanced stages diverge into two distinct paths. The Strategic Capability Path emphasizes internal development of absorptive and innovative capabilities under competitive pressure, whereas the Integration & Collaboration Path relies on ecosystem engagement through trust-based partnerships and platform interoperability. The resulting Two-Path IoT Adoption Maturity Model contributes theoretically and practically by revealing alternative digital progression mechanisms—capability accumulation versus ecosystem embeddedness—within emerging economy food supply chains.

Abstract Total electron content (TEC) is a important parameter in the domains of space weather studies and Global Navigation Satellite System (GNSS)-based navigation and communication applications. Conventional linear forecasting models face difficulties in effectively representing the complex nonlinear behaviors of the ionospheric dynamics. On the other hand, nonlinear approaches derived from advanced learning methods offer higher accuracy, but they necessitate substantial computational resources, building them impractical for real-time use in resource-constrained IoT environments. The emergence of Internet of Things (IoT) technology has facilitated the accessibility of affordable GNSS data associated through cloud platforms, allowing for ongoing and instantaneous collection of TEC data. In this paper, an efficient Successive Variational Mode Decomposition (SVMD) and Random Vector Functional Link (RVFL) framework is implemented to predict TEC via cloud platforms through Think Speak channels. The TEC observations from the year 2018 at Bengaluru (Geographic: 13.02° N, 77.57° E) is consider for analysis. The SVMD adaptively decomposes the TEC signal without requiring predefined mode selection, while RVFL enables fast training using random weights, direct connections, and universal approximation capabilities. The proposed model was evaluated using GNSS data from Bengaluru (13.02° N, 77.57° E). The results demonstrate that the SVMD–RVFL has an accuracy of 0.55 TECU for Root Mean Square Error (RMSE), 0.61 TECU for Mean Absolute Error (MAE), 7.64 % for Mean Absolute Percentage Error (MAPE), a correlation coefficient of 99.32 % and a training time of 3.82 s. The proposed approach demonstrates high precision and a low computational load, making it suitable for real-time ionospheric monitoring systems and IoT technologies.

Hydroponic agriculture, when combined with Internet of Things (IoT) technologies, provides a promising pathway to sustainable and efficient food production. This paper aims to systematically review and analyze recent advancements in IoT-based management for hydroponic systems, with a particular focus on assessing the technological maturity of current solutions, identifying existing gaps, and outlining promising directions for future research and development. Based on a review of 74 recent studies, the findings reveal a fragmented landscape characterized by custom-built solutions, predominantly relying on open-source microcontrollers and WiFi connectivity, but with limited adoption of standardized protocols and interoperable platforms. The majority of applications emphasize monitoring of core hydroponic parameters such as pH, EC, and temperature, while emerging uses of machine learning remain at an early stage. Few systems demonstrate readiness for commercial deployment or integration within broader smart agriculture ecosystems. By clarifying the current state of IoT-enabled hydroponics, this review highlights both the opportunities and the challenges in advancing from isolated prototypes toward robust, scalable systems capable of real-world application.

The progress of artificial intelligence (AI) and Internet of Things (IoT) technologies has resulted in a steady upsurge in the intellect and network abilities of vehicles. As an outcome, the IoT-based vehicle-to-everything (V2X) interaction methods are also recognized as the Internet of Vehicles (IoV). The IoV has garnered significant attention from both industry and academia. Whereas an inter-vehicular system links an automobile to exterior devices utilizing the technology of V2X. To reduce accidents of smart vehicles and detect malicious assaults in vehicular systems, numerous scholars have performed machine learning (ML)-based techniques for intrusion detection in IoT environments. In this study, we focus on the design and implementation of Privacy-Preserving Vehicle-to-Everything Technologies using Hybrid Deep Learning and Optimization Algorithms (PPV2XT-HDLOA) for Smart Transportation in Dubai. The presented PPV2XT-HDLOA model enhances V2X transportation by leveraging advanced data-driven techniques to optimize vehicle communication. To achieve this, the PPV2XT-HDLOA model applies the z-score normalization approach for data normalization to ensure data uniformity and enhance model convergence. To reduce dimensionality, the reptile search algorithm (RSA) can be employed to recognize the most relevant features. For the classification process, the hybrid deep learning model combining bidirectional temporal convolutional networks and bidirectional gated recurrent units (BiTCN-BiGRU) technique is exploited. Finally, the hyperparameter tuning of the BiTCN-BiGRU technique is carried out using the mountain gazelle optimization (MGO) algorithm to achieve optimal fine-tuning of parameters, ensuring superior classification performance. To demonstrate the better solution of the PPV2XT-HDLOA technique, a wide range of simulations have been tested, and the outcomes are inspected under several measures. The comparison investigation reported the improvement of the PPV2XT-HDLOA technique under various metrics.

This study lays the foundation for a multidimensional framework aimed at facilitating the effective integration of Internet of Medical Things (IoMT) technologies into real-world health management systems. It critically examines the technological, organizational, and societal barriers that hinder this transition and identifies key enabling conditions, such as interoperability, user co-design, and ethical design principles, that promote sustainability, inclusiveness, and trust. By proposing a structured approach to integration, this paper aims to bridge the gap between innovation and long-term, reliable adoption across diverse healthcare contexts.

The increasing global demand for electricity has heightened the need for stable and reliable power distribution systems. Disruptions in power distribution can cause substantial economic losses and societal impact, underscoring the importance of accurate, timely, and scalable monitoring. The integration of Internet of Things (IoT) technologies into smart grids offers promising capabilities for real-time data collection and intelligent control. However, the application of IoT has created new challenges such as high communication overhead and insufficient user privacy protection due to the continuous exchange of sensitive data. In this paper, we propose a method for power distribution analytics in smart grids based on IoT called PSDA. PSDA collects real-time power usage data from IoT sensor nodes distributed across different grid regions. The collected data is spatially organized using Hilbert curves to preserve locality and enable efficient encoding for subsequent processing. Meanwhile, we adopt a dual-server architecture and distributed point functions (DPF) to ensure efficient data transmission and privacy protection for power usage data. Experimental results indicate that the proposed approach is capable of accurately analyzing power distribution, thereby facilitating prompt responses within smart grid management systems. Compared with traditional methods, our scheme offers significant advantages in privacy protection and real-time processing, providing an innovative IoT-integrated solution for the secure and efficient operation of smart grids.

Hydroponics offers a promising alternative to soil-based agriculture, enabling higher yields, resource efficiency, and improved crop quality. This study compares traditional hydroponic setups with systems enhanced through the Internet of Things (IoT) framework using the Nutrient Film Technique and a proportional–integral controller, focusing on growth performance and environmental control. Systems incorporating Internet of Things technology achieved a growth rate of 0.94 cm/day versus 0.16 cm/day for conventional setups, due to precise water temperature control, optimized lighting, data acquisition, targeted nutrients, and reduced pest incidence. The integration of Industry 4.0 principles further enhances sustainable production and resource management. Statistical validation under diverse conditions is recommended. Future work will add environmental sensors, refine mechanical design, and explore machine learning for adaptive control, highlighting the potential of Internet of Things–based hydroponics to transform agriculture through intelligent, efficient, and eco-friendly cultivation.

Abstract The Sierra de Albarracín (Teruel, Spain) hosts notable post-Palaeolithic rock art, including the Toros del Prado del Navazo shelter. Although the Rock Art of the Mediterranean Arc is a World Heritage Site, its conservation faces environmental and human threats. Since 2013, monitoring in Aragón’s cultural parks has depended on periodic on-site data collection, limiting timely analysis. The integration of Internet of Things (IoT) technology at Toros del Prado del Navazo has improved conservation by enabling continuous remote environmental monitoring. This reduces the need for physical visits, lowering annual greenhouse gas emissions by 75% (from 197.20 to 49.30 kg CO₂eq) and minimising data gaps from 36% with traditional dataloggers to 5.9% in this particular case. IoT-based diagnostics allow faster decision-making, enhancing the preservation of rock paintings and promoting a sustainable, integrated management model for long-term protection of cultural heritage.

The widespread adoption of Internet of Things (IoT) technologies has transformed smart home environments by enhancing automation, connectivity, and user-centric functionality. However, the advancements also introduce different security vulnerabilities, among which SQL injection attack has become notably pervasive and damaging. The attack involves adversaries injecting or manipulating SQL queries through insecure IoT device interfaces, corrupting databases or exfiltrating data, which lead to unauthorized access, data leakage, device control compromise, and cascading effects across connected systems. In response to this attack, this study proposes an Intrusion Detection System (IDS) to detect and mitigate SQL injection attacks in IoT-based smart home networks. To ensure resilience and performance, the proposed IDS framework integrates three principal categories of artificial intelligence algorithms based on their distinct methodological advantages: first, traditional machine learning techniques, which provide foundational classification and clustering capabilities with interpretable decision boundaries and efficient performance on structured data; second, boosting-based ensemble methods, which contribute enhanced predictive accuracy and robustness through iterative refinement and sensitivity to complex feature interactions; and third, deep learning architectures, which further enrich the system by enabling hierarchical feature extraction and temporal pattern modeling, particularly suited to high-dimensional and sequential intrusion data. The strategic integration of these algorithmic classes allows the IDS to leverage complementary strengths, resulting in improved detection accuracy and adaptability across diverse threat environments.

Background: Handgrip strength (HGS) is a widely recognized biomarker of physical capability and overall health. However, conventional assessments rely on static peak values and fail to capture temporal variations that reflect fatigue accumulation and muscular asymmetry. Recent advances in Internet of Things (IoT) technologies now enable continuous biomechanical monitoring, offering new opportunities for precision occupational health management. Objective: This study aimed to examine dynamic grip performance among Korean Coast Guard officers using an IoT-enabled handgrip device and to evaluate the feasibility of artificial intelligence (AI) models in predicting fatigue risk and interlimb asymmetry. Methods: A total of 160 participants completed bilateral grip trials using a continuous IoT-based dynamometer that recorded mean force, asymmetry, fatigue index, coefficient of variation, and other derived parameters. Random Forest and Gradient Boosting algorithms were trained to classify participants into high- and low-fatigue risk groups. Model performance was evaluated using AUC, F1-score, and accuracy metrics, while explainable AI analysis (SHAP) identified key predictors. Results: Both models demonstrated strong predictive performance (AUC = 0.86–0.88; accuracy > 0.83). Fatigue index and asymmetry were identified as the most influential predictors, followed by years of service and mean handgrip strength. Continuous data analysis revealed that temporal grip variability provides valuable insights into neuromuscular efficiency beyond absolute force measurements. Conclusion: IoT-enabled continuous grip monitoring combined with interpretable AI offers a novel approach for detecting occupational fatigue and muscular imbalance. These findings suggest that dynamic digital biomarkers can enhance preventive ergonomics, inform personalized rehabilitation, and support the development of real-time fatigue management systems for high-demand professions.

With the deepening of the transformation and upgrading of the construction industry and high-quality development, the intrinsic relationship and optimization path between Construction Quality Management (CQM) and Organizational Performance (OP) have become a core issue of concern for both the industry and academia. The traditional quality management system faces many challenges in responding to increasingly complex engineering requirements and urgently needs to integrate emerging technologies to achieve strategic upgrades. This study focuses on exploring the empirical correlation between construction quality management practices and organizational performance (covering multiple dimensions such as cost, schedule, safety, satisfaction, etc.), and deeply analyzes the intelligent technology driven path centered on the deep integration of Building Information Modeling (BIM) and Internet of Things (IoT) technology, aiming to construct a strategic framework of "quality technology performance" linkage. This study reveals the correlation mechanism between construction quality management (QC) and organizational performance (OP) through empirical analysis, and proposes a performance upgrade path by integrating BIM IoT technology. Using a mixed research method (quantitative questionnaire+qualitative case), the significant impact of QC elements on OP was verified (β=0.78, p<0.01), and a "technology management performance" strategic framework was constructed. The results show that BIM IoT improves quality and efficiency through data collaboration (reducing design changes by 30%), real-time monitoring (reducing rework rates by 25%), and decision optimization, ultimately driving OP growth of 18% -24%. The research provides a feasible strategic paradigm for the digital transformation of the construction industry, verifying the necessity of technology integration and institutional innovation to drive organizational sustainable competitiveness. This study not only reveals the close relationship between construction quality management and organizational performance from an empirical perspective, but also innovatively proposes a quality management upgrade path with BIM IoT technology linkage as a strategic lever, providing theoretical basis and practical guidance for construction enterprises to break through quality management bottlenecks and achieve performance leaps. The study ultimately emphasizes that promoting the deep integration of intelligent technologies such as BIM IoT and quality management, and building an organizational process and performance evaluation system that is compatible with them, is a key strategic choice for the construction industry to achieve high-quality, efficient, and sustainable future development.

Effective detection of unexpected events in wide-area surveillance remains a critical challenge in the development of intelligent monitoring systems. Recent advancements in Narrowband Internet of Things (NB-IoT) and 5G technologies provide a robust foundation to address this issue. This study presents an integrated architecture for real-time event detection and response. The system utilizes the Constrained Application Protocol (CoAP) to transmit encapsulated JPEG images from NB-IoT modules to an Internet of Things (IoT) server. Upon receipt, images are decoded, processed, and archived in a centralized database. Subsequently, image data are transmitted to client applications via WebSocket, leveraging the Message Queuing Telemetry Transport (MQTT) protocol. By performing temporal image comparison, the system identifies abnormal events within the monitored area. Once an anomaly is detected, a visual alert is generated and presented through an interactive interface. The test results show that the image recognition accuracy is consistently above 98%. This approach enables intelligent, scalable, and responsive wide-area surveillance reliably, overcoming the constraints of conventional isolated and passive monitoring systems.

Modern architecture has witnessed radical transformation with emergence of the concept of a smart building, which relay on internet of Things (IOT) technologies to improve the environmental and operational Energy Performance this study aims to explore the impact of interactive architectural design using (IOT) technologies on the development of a smart building in Iraq's hot and dry climate by comparing global and local architectural models. The papers examine five principal case studies- World smart projects and local project covering heritage designs and simi- smart models focusing on green. performance indicators, energetic performance, and climate navigation. The result demonstrated the advantages of interactive smart projects in decreasing internal heat load and energy consumption by as much as 50 percent below conventional buildings. The work also revealed a potential for and local buildings (the Sunni endowment building in the International City Stadiums, towards wise and interactive through consideration of local potentials. and conditions.

Modern architecture has witnessed radical transformation with emergence of the concept of a smart building, which relay on internet of Things (IOT) technologies to improve the environmental and operational Energy Performance this study aims to explore the impact of interactive architectural design using (IOT) technologies on the development of a smart building in Iraq's hot and dry climate by comparing global and local architectural models. The papers examine five principal case studies- World smart projects and local project covering heritage designs and simi- smart models focusing on green. performance indicators, energetic performance, and climate navigation. The result demonstrated the advantages of interactive smart projects in decreasing internal heat load and energy consumption by as much as 50 percent below conventional buildings. The work also revealed a potential for and local buildings (the Sunni endowment building in the International City Stadiums, towards wise and interactive through consideration of local potentials. and conditions.

Digital transformation has changed the landscape of the global manufacturing industry, including in Indonesia. This study analyzes the optimization of automation technology as a strategy to improve the competitiveness of Indonesia's manufacturing industry in the digital economy era. Using literature review and descriptive analysis methods on secondary data from various sources, this study explores the implementation of automation technology in Indonesia's manufacturing sector, the challenges faced, and its impact on productivity and global competitiveness. The results show that the adoption of automation technology in Indonesia will reach 35 percent for large manufacturing companies in 2024, with the manufacturing industry sector growing by 5.68 percent in the second quarter of 2025.The Making Indonesia 4.0 program is a strategic foundation with a target contribution of Industry 4.0 to the Gross Domestic Product of US$133 billion by 2025. Internet of Things technology, artificial intelligence, and robotics are the main pillars of this transformation. Despite challenges such as limited investment and availability of skilled human resources, the potential for increased operational efficiency, reduced production costs, and improved product quality are the main drivers for automation adoption. This study recommends strengthening collaboration between the government, industry, and academia to accelerate the digital transformation of Indonesia's manufacturing industry.

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.

The increasing complexity of wireless sensor networks (WSNs) combined with the advancement of Internet of Things (IoT) technologies presents significant challenges regarding efficient node clustering, crucial for optimizing network performance and energy consumption in various applications. A primary research problem arises from the inefficiencies in current clustering algorithms that often fail to account for dynamic changes in network environments, such as varying node mobility and fluctuating energy levels, leading to suboptimal routing decisions and increased latency in data transmission.

The integration of Internet of Things (IoT) technology into smart grids is essential for enabling uninterrupted, bidirectional communication across all components of the power system. This connectivity enhances system reliability, efficiency and operational effectiveness. Among the various domains within the smart grid, sensor networks present the greatest potential for IoT deployment due to their role in real-time data collection and monitoring. The architecture of IoT-based smart grids encompasses key layers facilitating distributed generation and intelligent control. These structures span from power generation to end-user consumption, allowing each segment of the grid to benefit from IoT applications. In this study, Routing protocols, Optimization approaches and Cryptographic techniques are studied and analysed to ensure secure and efficient data transmission within intelligent grids. The comparative analysis of these techniques is conducted to evaluate the performance, highlighting their effectiveness in enhancing smart grid communication and management.

An automated monitoring system for scheduled watering and fertilization of rose plants was developed using Internet of Things (IoT) technology to support smart and efficient plant maintenance. The ESP8266 microcontroller serves as the central control unit, integrated with a soil moisture sensor to detect water levels in the growing medium and a Real Time Clock (RTC) module to enable automatic scheduling of fertilization. The system is equipped with an LCD display that shows real-time information on soil moisture, ambient temperature, pump status, and fertilization timing. All data can be monitored remotely in real-time via the Arduino IoT Cloud platform using a smartphone. A buzzer is used as an indicator to provide notification when the fertilization pump is activated or deactivated. The system is designed to assist users with limited time for plant care, ensuring that water and nutrient requirements are consistently maintained. The system was developed using a prototyping approach, followed by functional testing through blackbox testing and User Acceptance Testing (UAT). Results indicate that all main features operate as intended, the user interface is intuitive, and the system demonstrates stable performance. Therefore, this system provides an effective and practical solution for automated, schedule-based rose plant care with remote monitoring capabilities.

The recent studies have highlighted the transformative potential of the convergence of Internet of Things (IoT) technologies and predictive analytics into the healthcare systems, especially in the early diagnosis and treatment of hypertension and cardiovascular diseases (CVD). The paper analyzes how IoT-based predictive analytics has contributed to cardiovascular health by monitoring in real-time and providing insights using data. Using IoT devices (wearables, biosensors, and interconnected medical equipment), clinicians have access to continuous streams of patient data and these streams are assessed with machine learning (ML) and artificial intelligence (AI) algorithms. Such innovative technologies will help to predict risks accurately, allow implementing preventive measures, and adopt personalized treatment plans, which will reduce the impact of cardiac disease on the patient population and hospital facilities. The review covers several frameworks of the IoT, predictive models, and real-time monitoring systems, and their application in the development of preventive medicine. Besides, it focuses on the issues of data privacy, security, and the incorporation of the IoT systems into the current healthcare facilities. The paper will finally end with a provocative indication of the future course of the IoT-enabled healthcare analytics and how the notion of synergy with cloud computing and edge intelligence can be adopted to achieve even better patient outcomes and system optimization.