Abstract:
"Mobile context-awareness is one of the most important topics in the mobile computing research
because of the ubiquitous nature of the smart phones. It’s pervasive across all walks of our life
by being the constant companion providing contextually relevant information. Just as the
mobile computing power has increased exponentially over the decade, the mobile experiences
have also evolved. The evolved mobile experiences have increasingly challenged mobile
energy consumption. With the limited resources available in the mobile, it’s practically
impossible to be used in a resource constrained environment where there’s no connectivity or
power source. There’s certainly a lack of research in this space. This research effort is to
explore new model for context management framework for mobile in resource constrained
environments. Main objective of this research is to identify Architecture, algorithms and
subcomponents for the new model. The research yielded positive results as it was able to find
new algorithms, subcomponents such as sensors and working model for the context
management framework for resource constrained environments with evident energy
conservation. This work will be a new model to the area of mobile computing context
awareness research and will be a contributor to the research in using mobile in resource
constrained environments. Future works can be potential extensions of these research into more
classification algorithms, light-weight machine learning based sensor calibrations, merging
multiple sensors and exploring for far more energy conserving models."
