Handling Irregularly Sampled IoT Time Series to Inform Infrastructure Asset Management

Abstract

Infrastructure systems in today's increasingly interconnected world employ the capabilities of the Internet of Things (IoT) technologies for their monitoring, operational control, and asset management. IoT devices can be defined as sensors (of different types) collecting, processing, and sharing time series of data. The analysis of such data often face challenges as a consequence of the high frequency of data collection and the increasing number of sensors placed on infrastructure. Power related issues, timestamp misalignment, and heterogeneous sampling designs are among the most common issues that the IoT data collection may suffer alongside the inherent complexities of large scale databases. This paper provides an overview of time series mining techniques adapted to tackle such issues in IoT data. The aim is to have a pattern recognition tool-set for developing anomaly detection algorithms. Particularly, the paper investigates how to efficiently handle large-scale time series coming from multiple sensors in a stream and following an unevenly spaced - irregular - sampling. The analysis is demonstrated through a case study of time series data mining of sensors installed for supporting the predictive maintenance of quay-cranes at the Port of Felixstowe, the largest container port in Britain.