Department of Computer Science
Researcher:
Dr. Cho-Li Wang, Associate Professor (clwang@cs.hku.hk)
Project Title:
Case-based Component Selection Framework for Mobile Context-aware Applications
Project Description & Significance:
The computation scale has been much extended over time, space and application domains in our daily life. There is, no doubt, a trend towards more and more networked small devices with wireless access present in living and working spaces. Such a new computing environment has posted the following new requirements: heterogeneity, dynamism, mobility, and context-awareness. We feel building software infrastructure for pervasive computing should explicitly take into considerations on the following issues: Dynamic adaptation, User-level Mobility Support for context-aware applications and User-centric (Task-driven, Goal-oriented). To offer functionality adaptation, a new component paradigm: Facet Model is proposed. Facet separates code and data and state is kept in container so code and data can be adapted individually.
To satisfy the ever-increasing QoS demand especially those running on resource-constrained mobile devices, the software have to adapt to the runtime environment as the users are roaming around. Nevertheless, most existing approaches only support component selection based on predefined rules and strategies. Since case-based reasoning (CBR) system can be created with a small or limited amount of experience and then developed incrementally, we adopt the CBR to help the proactive component (Facet) selection. Context-awareness and personalization are embodied in the reasoning and selection process. We developed and evaluated a context-aware personal communicator (CAPC) application using adaptive component selection, with a synergistic execution trace obtained from real-life E-mail software sported to CAPC. Our results show that the adaptive component selection can reduce maximum memory consumption by at least 20%, and the context-guided reasoning technique can improve reasoning accuracy by nearly 10% within acceptable reasoning time.
 |
 |
