Validating Quality of Context in Pervasive Computing Systems: Surf Life Saving Use Case
Kanaka Sai Jagarlamudi, Arkady Zaslavsky, Seng W. Loke, Kevin Lee · 2023
We present context Cost and Quality computational engine 2.0 (conCQeng 2.0, in short); this system addresses a significant drawback in Quality of Context (QoC) measurement models that lead to QoC-aware selection uncertainties in Context Management Platforms (CMPs). Current QoC measurement models rely on the QoC parameters in context (such as time-stamps) to assess QoC metrics, representing the context's usability for the pervasive computing applications and selecting the better-performing context providers. Nevertheless, such parameters are prone to misrepresentation, limiting the credibility of QoC measurement. In this paper, we propose a QoC validation mechanism through which conCQeng 2.0 determines the genuineness of measured QoC-metrics, further contributing to a credible QoC-aware selection. We motivate the proposal through its significance in the surf life saving use case. Our evaluation demonstrates that conCQeng 2.0 improves the credible QoC acquisition through the selection process - with slight but addressable processing overhead compared to its baseline version that attains higher QoC adequacy than heuristic models.