Jul 2026· International Conference on Future Internet of Things and Cloud· pp. 19-26· 0 citations· 27 references
Abstract
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.
The developed system proved to be a cost-effective and practical solution for intelligent monitoring and automation applications and confirmed that IoT technology can substantially reduce manual intervention, improve response time, and enhance system effectiveness.
Zarreen Fatima, A. Farooqi· International Scientific Jou...· 0 citations
Smart-building research increasingly requires IoT platforms that support reproducible deployment and long-term operation under real conditions. This paper presents a field-operable and reproducible IoT testbed for smart-building HVAC, grounded in an architecture aligned with ISO/IEC 30141 and designed for zero-touch onboarding, fleet-wide Over-The-Air (OTA) lifecycle management, and end-to-end observability. The architecture integrates ESP32-based devices running FreeRTOS, multi-protocol communication using MQTT and CoAP, and a lightweight containerized backend with time-series persistence and monitoring.The testbed was deployed in a university laboratory, where a rule-based HVAC control strategy reduced monthly energy consumption from 101.68 to 45.04 kWh (55.7%) while maintaining thermal comfort. Controlled experiments further evaluate protocol trade-offs, quantifying MQTT QoS impacts on actuation latency and comparing MQTT and CoAP for continuous telemetry under constrained conditions. In addition, the architecture enables progressive edge intelligence through data-driven model deployment without structural changes.Rather than proposing new protocols or control algorithms, this work contributes a standards-aligned, observable, and evolvable IoT testbed, together with empirical evidence and practical design insights for field-oriented smart-building research.
Lucas de Souza Marques, Pedro Porto Teixeira, Renan Correia Monteiro Soares et al.· International Conferences on...· 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 et al.· 0 citations
Continuous monitoring of water quality is essential for supporting environmental management and enabling timely decision-making in wastewater treatment facilities. Although numerous Internet of Things (IoT) solutions have been proposed for environmental monitoring, many rely on proprietary cloud platforms or commercial gateways that limit flexibility, scalability, and integration with customized applications. This paper presents the design, implementation, and field validation of a modular IoT architecture for real-time water quality monitoring based on distributed sensor nodes, long-range LoRa communication, and a self-hosted web platform. The proposed architecture integrates sensor nodes equipped with calibrated pH, dissolved oxygen, and turbidity sensors, a hybrid LoRa/Wi-Fi Main Controller implementing a custom master–slave communication protocol, and a Python-based back-end with a PostgreSQL database for data acquisition, storage, visualization, and historical analysis. The complete system was deployed and experimentally validated in a real coupled constructed wetland located at the Universidad del Atlántico, Colombia, where three monitoring stations continuously acquired and transmitted water quality measurements over a one-month evaluation period. During the experimental deployment, the system generated more than 4.5 million measurement records (297 MB) while recording average RSSI values between −55.7 and −58.1 dBm (standard deviation: 1.6–2.2 dB). The developed web platform successfully supported real-time visualization and historical analysis of all acquired measurements. These results demonstrate the feasibility of the proposed architecture as a practical, scalable, and modular solution for continuous environmental monitoring that can be readily adapted to other distributed water quality monitoring applications.
This research endeavors to address challenges in ensuring reliable and efficient communication in FANET-IoT-IoT-IoV interactions within the context of 6G-enabled smart city applications by systematically evaluating the performance metrics, identifying optimization opportunities, and developing novel methodologies.
The Internet of Things (IoT) technologies and their high growth rate in the context of data science approaches have led to the significant shift in paradigm of smart home automation. Conventional home automation systems are mainly dependent on the rule-based measures of control and do not offer flexibility and intelligence. Modern smart homes are expected to be more automated and energy efficiency, comfortable to the user, and secure through the combination of data-driven solutions like machine learning, predictive analytics, and real-time data processing. This essay is a detailed research of the use of IoT and data science in the automation of smart homes. The suggested interface focuses on sensor-based acquisition of data, cloud storage, and the smart decision-making process based on machine learning models. Modular architecture: It is a form of architecture that is created to support interoperability and scalability among heterogeneous devices. The most common challenges include privacy of data, system consistency and efficiency. The experimental performance proves that data science-based automation is much more efficient than the conventional approaches to automation concerning energy optimization and prediction of user behavior. The results validate the fact that the combination of IoT and data science forms a strong venue of the future smart houses.
Fatou Diop· International Journal of App...· 0 citations
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