Document Type



Doctor of Philosophy (PhD)


Computer Science

First Advisor's Name

Niki Pissinou

First Advisor's Committee Title

Committee chair

Second Advisor's Name

S.S. Iyengar

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Kia Makki

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Deng Pan

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Naphtali Rishe

Fifth Advisor's Committee Title

Committee Member

Sixth Advisor's Name

Kang Yen

Sixth Advisor's Committee Title

Committee Member


Computer sciences

Date of Defense



Learning at any-time, at anywhere, using any mobile computing platform learning (which we refer to as “education in your palm”) empowers informal and formal education. It supports the continued creation of knowledge outside a classroom, after-school programs, community-based organizations, museums, libraries, and shopping malls with under-resourced settings. In doing so, it fosters the continued creation of a cumulative body of knowledge in informal and formal education. Anytime, anywhere, using any device computing platform learning means that students are not required to attend traditional classroom settings in order to learn. Instead, students will be able to access and share learning resources from any mobile computing platform, such as smart phones, tablets using highly dynamic mobile and wireless ad-hoc networks. There has been little research on how to facilitate the integrated use of the service description, discovery and integration resources available in mobile and wireless ad-hoc networks including description schemas and mobile learning objects, and in particular as it relates to the consistency, availability, security and privacy of spatio-temporal and trajectory information. Another challenge is finding, combining and creating suitable learning modules to handle the inherent constraints of mobile learning, resource-poor mobile devices and ad-hoc networks.

The aim of this research is to design, develop and implement the cutting edge context-aware and ubiquitous self-directed learning methodologies using ad-hoc and sensor networks. The emphasis of our work is on defining an appropriate mobile learning object and the service adaptation descriptions as well as providing mechanisms for ad-hoc service discovery and developing concepts for the seamless integration of the learning objects and their contents with a particular focus on preserving data and privacy. The research involves a combination of modeling, designing, and developing a mobile learning system in the absence of a networking infrastructure that integrates sensory data to support ubiquitous learning. The system includes mechanisms to allow content exchange among the mobile ad-hoc nodes to ensure consistency and availability of information. It also provides an on-the-fly content service discovery, query request, and retrieving data from mobile nodes and sensors.





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