Jul 2026· SinkrOn· Vol 10, pp. 1451-1463· 0 citations
TL;DR
The findings suggest that the modular API-driven architecture not only improves the flexibility and scalability of multi-device IoT integration but also maintains reliable data consistency and efficient communication performance.
Abstract
The Internet of Things (IoT) has increasingly played a significant role in the development of adaptive and real-time environmental monitoring systems. However, integrating multiple IoT devices remains challenging due to variations in data transmission intervals, communication protocols, and processing capabilities across devices. These differences often complicate system interoperability and data management within a unified monitoring platform. To address this issue, this study proposes an API-based modular architecture as a solution for integrating heterogeneous IoT devices in environmental monitoring systems. The proposed architecture separates core system functions into independent modules, including data acquisition, device management, and data visualization. The proposed architecture is evaluated through a multi-device environmental monitoring implementation configured with different logging intervals in order to assess communication performance and data consistency. The novelty of this study lies in its architectural approach to handling heterogeneous data transmission intervals in multi-device IoT environments using a modular API-based design. The experimental results indicate that the average communication latency is approximately 200ms, while the average daily data logging volume exceeds 3,500 entries per device. Furthermore, analysis of logging interval variations shows a time deviation of less than 3 seconds, which remains within the acceptable range for real-time environmental monitoring applications. The results demonstrate that the proposed architecture achieves success rate of over 97%, confirming the reliability of the proposed API-based modular architecture. Overall, the findings suggest that the modular API-driven architecture not only improves the flexibility and scalability of multi-device IoT integration but also maintains reliable data consistency and efficient communication performance.
The findings suggest that the modular API-driven architecture not only improves the flexibility and scalability of multi-device IoT integration but also maintains reliable data consistency and efficient communication performance.
A. M. Elhanafi, Dedy Irwan, Kissi Lola et al.· 0 citations
The rapid growth of the Internet of Things (IoT) has significantly accelerated the development of smart home systems, enabling automation, energy efficiency, and enhanced user experience. However, the lack of interoperability among heterogeneous devices and platforms remains a major challenge, resulting in fragmented ecosystems and limited scalability. To address these issues, the Matter protocol has emerged as a unified, IP-based connectivity standard for smart home environments. This paper presents a comprehensive study of the Matter protocol, including its architecture, communication mechanisms, and security model. A practical IoT-based smart home system is designed and implemented using ESP32 platforms and the Matter SDK. The system integrates multiple devices such as smart lighting, switches, smart plugs, and environmental sensors, supporting crossplatform interaction across different ecosystems. Experimental evaluation is conducted under real-world conditions, focusing on interoperability, latency, system stability, and security. The results show that the proposed system achieves reliable crossplatform compatibility, stable network performance, low communication latency, and secure device authentication. Additionally, the system maintains core functionality even under limited network conditions, demonstrating strong robustness. These findings confirm that the Matter protocol is a promising solution for building scalable, secure, and interoperable next-generation smart home systems.
Nghia Duong Tan, H. Manh, P. N. Huu et al.· 2026 11th International Conf...· 0 citations
Abstract
The Internet of Things (IoT) has emerged as one of the most significant technological advancements in recent years, enabling seamless communication and interaction among physical devices through the Internet. IoT technology has transformed traditional monitoring and control systems by providing real-time data collection, remote accessibility, intelligent decision-making, and automated control mechanisms. The increasing demand for smart environments in sectors such as smart homes, healthcare, agriculture, industrial automation, and smart cities has accelerated the adoption of IoT-based solutions. This dissertation presents the design and implementation of an IoT-based smart system for real-time monitoring and automation using low-cost hardware components, cloud computing platforms, and mobile applications.
The primary objective of this research is to develop an efficient, reliable, and scalable IoT-based monitoring system capable of collecting environmental data, transmitting information to cloud servers, and enabling remote monitoring and automated control. The proposed system integrates NodeMCU ESP8266 as the central processing unit with sensors such as the DHT11 temperature and humidity sensor and the PIR motion sensor. These sensors continuously monitor environmental conditions and transmit the collected data to cloud platforms through wireless communication. ThingSpeak is used as the cloud platform for data storage, visualization, and analysis, while the Blynk mobile application provides users with real-time monitoring and remote control capabilities.
The development process involved hardware configuration, software programming, cloud integration, and system testing. The NodeMCU microcontroller was programmed using Arduino IDE to acquire sensor readings, establish wireless connectivity, and communicate with cloud services. The collected data was uploaded to the cloud platform and displayed through graphical dashboards for real-time observation. Furthermore, automation functionalities were incorporated through relay modules that enable automatic control of connected devices based on predefined threshold values and environmental conditions.
Experimental investigations were conducted to evaluate the performance and effectiveness of the proposed system. The results demonstrated successful real-time monitoring of temperature, humidity, and motion detection parameters. Sensor data was accurately transmitted to the cloud platform and displayed on the mobile application with minimal delay. The automation features successfully triggered control actions whenever predefined conditions were satisfied. The system also demonstrated reliable communication between sensors, cloud servers, and end users, thereby validating the feasibility of the proposed approach.
A comparative analysis between traditional monitoring systems and the proposed IoT-based solution revealed significant improvements in terms of automation, accessibility, data management, operational efficiency, and remote monitoring capabilities. The implementation confirmed that IoT technology can substantially reduce manual intervention, improve response time, and enhance system effectiveness. Despite challenges such as network dependency, security concerns, and sensor limitations, the developed system proved to be a cost-effective and practical solution for intelligent monitoring and automation applications.
Zarreen Fatima, Dr. Abdul Majid Farooqi· International Scientific Jou...· 0 citations
The integration of IoT devices and the development of smart cities have brought about significant changes in urban infrastructure. Smart spaces represent a pivotal use case, exemplifying the integration of IoT sensors to enhance automation and decision-making. In these environments, interoperability is critical when incompatible devices interact, enabling seamless communication and optimized performance. To the best of our knowledge, this is the first work to present a comparative evaluation of systems with and without interoperability, focusing on end-to-end system performance and highlighting the importance of interoperability in real-time smart space control. Towards this, we implemented a multi-layered architecture consisting of a novel Controller Layer (CL) that drives the interactions between air quality sensing and actuation of the window and air purifier. Additionally, the architecture consists of the Device Layer (DL), Data Monitoring Layer (DML), and Data Storage Layer (DSL). The DML uses oneM2M as middleware to achieve interoperability among indoor and outdoor air-quality sensors and actuators, such as a window controller and an air purifier. Our focus is on assessing the end-to-end performance of interconnected dependent actions and the significance of response time across incompatible devices. Experimental results show correlations between window controller and air-purifier states based on sensor data, offering insights into achieving interoperability in smart spaces and improving real-time air-quality management.
Sasidhar Varada, Ushasri Mogadali, Deepak Gangadharan et al.· International Conference on...· 0 citations
An overview of the IoT architecture is provided, comparing leading IoT platform such as AWS and Microsoft Azure vendor to understand the overall architecture of these platforms and explore their main cores for potential customization in developing a proprietary IoT platform.
Sothea Phann· International Journal of Fut...· 1 citation
Nowadays, the Internet of Things (IoT) integrates connectivity into devices to improve processes and optimize resources in various scenarios (e.g., hospitals, factories, and homes). However, the diversity of technologies and devices makes interoperability a challenge. For this reason, ontologies have emerged to model knowledge and to establish guidelines for proper communication. As the number of devices and the volume of data increase, additional considerations, such as data privacy, security, environmental awareness, and ethics, become essential to ensure appropriate data protection and efficiency. To address these issues, we demonstrate the applicability of the IoTO++ ontology, which aims to represent not only the core components of the system (such as sensors and actuators) but also the data they produce. A museum simulation was created in Node-RED that models an IoT ecosystem with five types of sensors and an access control system for visitors and personnel. This application was validated in order to evaluate correctness and quality. To this end, correctness was assessed using the Pellet reasoner, while quality was evaluated through tailored questionnaires. These assessments were successfully addressed using both description logic representations and SPARQL queries, thereby demonstrating the ontology’s expressiveness and its alignment with domain requirements. This dual perspective further strengthens the ontology’s credibility and readiness for deployment in diverse IoT environments. Performance analysis, averaged over ten runs on a dataset of 100 sensor observations and 1000 triples, produced query execution times between 0.0017 and 0.0268 s ( $\approx$ 0.0061 s average). Furthermore, scalability tests with 10–10 000 observations showed stable loading times ranging from 0.58 to 0.62 s, averaging 0.5931 s. These results demonstrate the system’s efficiency and consistent performance for real-time IoT applications as datasets scale.
Maria Cornejo-Lupa, A. Aguilera, Irvin Dongo· IEEE Internet of Things Jour...· 0 citations