Measurement-based spectrum research isn’t new, but there is a renewed interest in understanding how the spectrum is being utilized. With the... Show moreMeasurement-based spectrum research isn’t new, but there is a renewed interest in understanding how the spectrum is being utilized. With the modern prevalence of connected devices and our increasing reliance on wireless technologies, there is increasing demand for additional spectrum. The question of how to meet this demand largely depends on how the spectrum is being used today and thus a need for advanced measurement-based research has emerged. Spectrum measurement and analysis is complicated; the data is multi-dimensional and dynamic in time, space, and frequency. Signal behavior is governed by complex mathematics and its use is regulated by government agencies across the world. Data collection relies on a complex system of expensive hardware where the physical attributes of antennas, analyzers, and deployment locations all impact the data that’s collected. These variables and concerns must all be considered while deploying a Spectrum measurement system. This paper presents the Semantic Spectrum Ontology (SSO), a model which aids researchers in designing and deploying Spectrum Measurement systems and publishing their data as community resources. The SSO exists within the paradigm of the Semantic Web and links into the wider Semantic graph by extending the W3C’s Semantic Sensor Network Ontology (SSN). The Semantic Spectrum Ontology also presents two new Semantic Constructs. The Scientific Provenance Model allows researchers to publish in-depth metadata concerning the measurements and the conditions under which they were collected, and the Scientific Property Model creates a framework for encoding knowledge from various sources including domain experts and machine learning statistics. These two models were constructed specifically for the SSO but were generalized to allow for their application within any ontology representing any scientific field. M.S. in Computer Science, May 2016 Show less
