Sep 2026· IEEE Internet of Things Journal· Vol 13, pp. 40474-40490· 0 citations· 50 references
Abstract
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.
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
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
As the concepts of IoT systems deepen, the world inclines towards safety, energy management, sustainability, efficiency, predictability and prevention. With an estimated global market spending on IoT of about $15 trillion by 2030, seamless integration of IoT devices and big data platforms is considered vital to ensure a robust, efficient, and secure IoT ecosystem. Despite its wide application the integration of IoT and big data frameworks is hindered by heterogeneous devices and protocols, semantic incompatibility, and scalability gaps between real-time IoT streams and Big Data systems. Data quality issues, lack of unified standards across edge, fog and cloud layers, security model mismatch, and fragmented data formats and vendor-specific ecosystems are additional challenges that create significant obstacles to seamless data exchange, unified analytics and reliable end-to-end integration. Through a conceptual research method of study, this paper highlights the interoperability of the IoT framework and big data analytics, the application areas of the framework providing sustainable solutions, challenges and opportunities that reflect the need for advancements of the framework strategies followed by recommendations to effectively mitigate the existing and future risks and challenges.
V. R. Naidu, Nafhath Rasheeda Rafiq, Syed Najeeb Rafiq· Digital Technologies Researc...· 0 citations
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 Armedia Br Siregar· SinkrOn· 0 citations
With the growing demand of Internet of Things (IoT), there is a need for seamless and reliable communication between heterogeneous IoT devices and the cyber-world to ensure autonomous control over any application process. More specifically, seamless communication requires interoperability between heterogeneous devices (actors) having different semantics and data formats (syntaxes), while making it more challenging. In this paper, we propose a middleware solution for unified semantic and syntactic interoperability in the publisher-subscriber framework of IoT network. The proposed framework automatically translates the subscribers (users) compatible syntax and semantics of the receiver message from the publishers (IoT devices). First, we propose a novel method of syntax translation of messages, to solve the syntactic disparities between users and devices, while providing the information in the user requested syntax. Thereafter, a multilayer perceptron (MLP)-based semantic interoperability framework is proposed to translate the device information to the user requested semantics. Additionally, a novel algorithm is proposed for extracting raw and discriminative features, which are to be fitted to the MLP model as inputs. To show the effectiveness of the proposed middleware, we evaluate different parameters, while considering various publicly used data formats and semantic annotations of attributes to ensure the versatility of the proposed middleware in the practical scenario. The overall classification accuracy using MLP is $95.78$\% for determining the standard meaning of each attribute of the incoming message from the publisher to address the semantic interoperability problem in IoT.
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