A Combined IoT-Enabled SensorDB and Spatial Query Framework for Smart Farming

Smart farming is an emerging concept that appeared in the context of agriculture 4.0. It aims at providing the agricultural industry with the infrastructure to leverage advanced technology including the internet of things (IoT) in building Digital Farms. To face the expanding global population and the increasing demand for crop yield, food production needs to reach higher levels of automation and efficiency, and thus the need for tracking, monitoring, automating, and analyzing operations. A wireless sensor network (WSN), and through an adequate IoT application, collects environmental and soil data of a monitored field to help make informed decisions. The nature and frequency of collected data may change throughout the agricultural season or due to a change in agricultural activities. Such changes require reprogramming all the sensor nodes unless the WSN is modeled as a distributed database referred to as SensorDB. The data collection is then reduced to a simple declarative request, in an SQL-like language, formulated by the user to specify the sensory measure of interest, the measurement frequency, and the required execution time, etc. In this research paper, we present QLowPAN, an IoT-enabled SensorDB coupled with a spatial query system that further helps the execution of in-network operations. A performance evaluation of QLowPAN, in the context of a smart farming application to fight against the Late blight potato epidemic pest, shows performance gains in terms of energy consumption, reaching up to 400% on average, when compared to a standard basic approach.