Benchmarking Database Systems for the Requirements of Sensor Readings

Improving energy efficiency in order to reduce CO2 emissions is a permanent challenge in the European space. Smart metering could help for improving energy efficiency by offering information about the way in which the energy is used. Smart metering will be based on large volumes of sensor data, since energy monitoring will bring together sensor data from various critical areas. The main purpose of this paper was to present the selection mechanism for a scalable storage solution, based on the requirements of the DEHEMS (Digital Environment Home Energy Management System) project. With regular sensor readings coming at every 6 seconds, there is an impressive amount of data collected even for the minimal target of about 250 households, 10 sensors per user. With these huge data streams that are non-stationary time-series data, collected at discrete intervals, the DEHEMS project has to offer a solution for storing and retrieving sensor data in a responsive way. We have tested both collection speed and aggregation speed for reasonable data streams of sensor data. The tests were performed on various database models, with their associated representations, including relational databases, key-value stores, column stores, self-tuning databases, as well as time-series enabled database systems. These experiments confirmed that column stores and key-value stores perform better than relational databases, while time-series databases outperform all the others.