IoT Wallet: Machine Learning-based Sensor Portfolio Application
This is an incremental development for IoT users, offering a system to manage sensor data with added machine learning capabilities.
The paper presents an IoT wallet application that collects, stores, and displays sensor data, with a machine learning service used to estimate soil moisture from LoRa signal strength in a case study.
In this paper an application for building sensor wallet is presented. Currently, given system collects sensor data from The Things Network (TTN) cloud system, stores the data into the Influx database and presents the processed data to the user dashboard. Based on the type of the user, data can be viewed-only, controlled or the top user can register the sensor to the system. Moreover, the system can notify users based on the rules that can be adjusted through the user interface. The special feature of the system is the machine learning service that can be used in various scenarios and is presented throughout the case study that gives a novel approach to estimate soil moisture from the signal strength of a given underground LoRa beacon node.