| Abstract |
The technological development and dissemination of IoT equipment have led to large volumes of environmental data which, in some cases, are incomplete, follow different formats of representation, and even have different semantic approaches. All such aspects and the heterogeneity of different IoT components (e.g., network interfaces, communication protocols, data structure, and data semantics) have caused interoperability issues which might hamper the effectiveness of support decision systems for smart cities, where the use of big data and machine learning techniques has been considered, in addition to the exploration of smart city data. This article proposes an environment IoT-based platform for smart cities that grants interoperability from data capture to knowledge extraction and visualization through the use of Semantic Web Technologies and the definition of an ontology for environment indicators. The components of the platform include IoT devices, gateways, cloud, and fog computing, which are used for a better application of big data techniques. A real environment quality monitoring use case was considered for the validation of the platform. Metrics, such as latency and resources consumption, were analyzed for three communication protocols, namely, MQTT, CoAP, and REST. CoAP adapter provided the best results regarding latency, RAM, and CPU consumption. |