Extending semantic sensor networks with QueryML

Abstract

As sensors become more affordable and versatile, more and more sensors are deployed in different environments to help people observe their surroundings. However, due to their various physical structures, it is very challenging to have a universal schema to identify, search, and query sensors and sensors' data. Fortunately, there are two main approaches to address some of these problems, namely Semantic Sensor Network (SSN) from W3C and Sensor Web Enablement (SWE) from the Open Geospa-tial Consortium (OSG). Both utilize XML to extend sensors' metadata and let machines understand the semantic meaning of a sensor. However, even though they provide a universal way to describe and deliver high-level sensor information, neither enable the querying of historical data. In this paper, we briefly examine the current semantic sensor web developments, SNN and SWE along with their advantages and challenges. Then we present our extensions to enable querying historical data within the semantic sensor domain that we call QueryML. QueryML can be used to extend the capabilities of either SSN or SWE to support querying historical data.