SENDAI: A framework for joint reasoning about sensor data acquisition and sensor data analytics

Sensors are increasingly being deployed to monitor critical infrastructure. However, as the number of sensors being deployed increases, so does the amount of sensor data that must be transmitted, stored, and analyzed. Thus, a significant number of methods have been proposed to improve sensor data acquisition and analytics. However, the proposed strategies and methods generally focus exclusively on either sensor data acquisition or analytics, thus ignoring the possible optimization that can be performed by taking a holistic view. To explore this opportunity, this paper provides an overview of sensor data acquisition and analytics and an analysis of two very different use cases, specifically monitoring wind turbines and measuring utility consumption using smart meters. Based on this analysis, the Framework for joint Sensory Data Acquisition and Analytics (SENDAI) is proposed, an integrated framework that models sensor data acquisition and analytics together, thus enabling holistic reasoning about sensor data acquisition and analytics. To demonstrate how the information in SENDAI can be used to reason about sensor data acquisition and analytics together, we show how sensor data acquisition can be optimized to respond efficiently to query workloads.

Authors

• Søren Kejser Jensen
• Josefine Kejser
• Federico Chiariotti
• Christian Thomsen
• Anders Ellersgaard Kalør
• Petar Popovski
• Beatriz Soret
• Torben Bach Pedersen

Keywords

• Sampling
• Sensor data acquisition
• Sensor data analytics
• Wind turbine
• Smart meter
• Framework