Comparative analysis of time series databases in the context of edge computing for low power sensor networks

Abstract

Selection of an appropriate database system for edge IoT devices is one of the essential elements that determine efficient edge-based data analysis in low power wireless sensor networks. This paper presents a comparative analysis of time series databases in the context of edge computing for IoT and Smart Systems. The research focuses on the performance comparison between three time-series databases: TimescaleDB, InfluxDB, Riak TS, as well as two relational databases, PostgreSQL and SQLite. All selected solutions were tested while being deployed on a single-board computer, Raspberry Pi. For each of them, the database schema was designed, based on a data model representing sensor readings and their corresponding timestamps. For performance testing, we developed a small application that was able to simulate insertion and querying operations. The results of the experiments showed that for presented scenarios of reading data, PostgreSQL and InfluxDB emerged as the most performing solutions. For tested insertion scenarios, PostgreSQL turned out to be the fastest. Carried out experiments also proved that low-cost, single-board computers such as Raspberry Pi can be used as small-scale data aggregation nodes on edge device in low power wireless sensor networks, that often serve as a base for IoT-based smart systems.