Machine learning–enabled cognitive approaches for handling IoT-based environmental data

Monitoring parameters for changes and initiating actions is key to managing an environment effectively. Internet of Things (IoT)-based platforms have been widely discussed as a viable and efficient solution across different domains. A robust, IoT-based cognitive environment monitoring solution needs to combine factors such as data collection, routing, preprocessing, storage, data analysis techniques, and building intelligent environment-specific models, visualization to easily interpret the outcome, and an optimum platform and technologies. Evolution is seen across the spectrum of technologies, frameworks, and techniques. Data collection has improved, owing to advancements in sensor technology and hardware. The adoption of free and open-source software (FOSS) and open-IoT ecosystem has provided the necessary impetus. Accelerated development and deployment is possible using pretrained models. Available implements offer us the capability to aggregate and analyze live data streams in the cloud in real-time using different analytical models and instant visualizations of data posted by the devices at the far-site.