Context Selection Optimization Using Quality of Context for Context Refinement in Internet of Things
Keywords:
Ntalasha Derrick, Li Renfa, and Wang YonghengAbstract
Context awareness plays a critical role in the
Internet of things (IoT) paradigm in providing services
appropriate to persons and devices through context information gathering, adaptation and distribution. In this paradigm, context is generated by billions of sensors spreading over a large geographical location. The context generated may not be accurate and appropriate to be used by other context aware
applications. Context information is usually not correct because sensor technology used cannot produce error free or accurate sensor data due to various technical and environmental factors. Factors like capability of sensing devices, precision and accuracy of the methods used to collect sensor data, instability of sensors and computing devices, and weather conditions impact
the quality of sensor data. To improve the input quality of context refinement process in the middleware framework that deals with context management for intermediating between sensing systems and context aware applications, context selection optimization using Quality of Context is proposed (QoC). This paper provides a methodology that uses QoC in the
context refinement process because quality of low level context information is an indicator of whether or not the high level context information makes sense or not. IoT context selection optimization uses Particle Swarm Optimization (PSO) and combined confidence for QoC to select context objects from the IoT domain. This advanced algorithm uses QoC confidence as criteria to search and extract context objects with the highest
combined confidence value. The results of the experiments indicate that input context refinement is improved through selecting contexts that are highly reliable
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