Degree Granting Department
Context-Awereness, Energy Efficiency, Human Activity Recognition, Machine Learning, Optimal Sensing, Ubiquitous Sensing
The ever-increasing technological advances in embedded systems engineering, together with the proliferation of small-size sensor design and deployment, have enabled mobile devices (e.g., smartphones) to recognize daily occurring human based actions, activities and interactions. Therefore, inferring a vast variety of mobile device user based activities from a very diverse context obtained by a series of sensory observations has drawn much interest in the research area of ubiquitous sensing. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users, and this allows network services to respond proactively and intelligently based on such awareness. Hence, with the evolution of smartphones, software developers are empowered to create context aware applications for recognizing human-centric or community based innovative social and cognitive activities in any situation and from anywhere. This leads to the exciting vision of forming a society of ``Internet of Things" which facilitates applications to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network which is capable of making autonomous logical decisions to actuate environmental objects. More significantly, it is believed that introducing the intelligence and situational awareness into recognition process of human-centric event patterns could give a better understanding of human behaviors, and it also could give a chance for proactively assisting individuals in order to enhance the quality of lives.
Mobile devices supporting emerging computationally pervasive applications will constitute a significant part of future mobile technologies by providing highly proactive services requiring continuous monitoring of user related contexts. However, the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth as compared to the capabilities of PCs and servers. Above all, power concerns are major restrictions standing up to implementation of context-aware applications. These requirements unfortunately shorten device battery lifetimes due to high energy consumption caused by both sensor and processor operations. Specifically, continuously capturing user context through sensors imposes heavy workloads in hardware and computations, and hence drains the battery power rapidly. Therefore, mobile device batteries do not last a long time while operating sensor(s) constantly.
In addition to that, the growing deployment of sensor technologies in mobile devices and innumerable software applications utilizing sensors have led to the creation of a layered system architecture (i.e., context aware middleware) so that the desired architecture can not only offer a wide range of user-specific services, but also respond effectively towards diversity in sensor utilization, large sensory data acquisitions, ever-increasing application requirements, pervasive context processing software libraries, mobile device based constraints and so on. Due to the ubiquity of these computing devices in a dynamic environment where the sensor network topologies actively change, it yields applications to behave opportunistically and adaptively without a priori assumptions in response to the availability of diverse resources in the physical world as well as in response to scalability, modularity, extensibility and interoperability among heterogeneous physical hardware.
In this sense, this dissertation aims at proposing novel solutions to enhance the existing tradeoffs in mobile sensing between accuracy and power consumption while context is being inferred under the intrinsic constraints of mobile devices and around the emerging concepts in context-aware middleware framework.
Scholar Commons Citation
Yurur, Ozgur, "Energy Efficient Context-Aware Framework in Mobile Sensing" (2013). USF Tampa Graduate Theses and Dissertations.