Evaluating Sensor Data Quality in Internet of Things Smart Agriculture Applications

Abstract

The unprecedented growth of Internet of Things (IoT) underpinned by machine-to-machine communication, analytics, and actuation is spearheading the development of IoT-based smart agriculture applications (IoTSAs). Various factors (e.g., location and weather) impact data availability for such IoT applications, which impacts the decision making and actuation process of IoTSA. This article proposes a conceptual framework and a novel model to compute sensor data quality model that can be used by IoTSA in their decision/actuation process to adapt to uncertainty in IoT sensor data. To demonstrate the efficacy of the proposed data quality metrics and model, we apply them to an IoTSA for monitoring milk condition in dairy farms (an instance of IoTSA). Our real-world experimental evaluations conclude that the proposed model 1) can be employed by IoTSAs to adapt to different factors that may impact the quality of decision making (actuation) and 2) aids in developing data quality-aware IoTSA and beyond.