A Case-Based Component Selection Framework for Mobile Context-Aware Applications

Abstract

Abstract-In the ever-changing pervasive computing paradigm, applications, especially those running on resource-scarce mobile devices, have to adapt to the runtime environment as the users are roaming around. Various adaptation techniques, relying on dynamic composition of components, have been proposed by a number of researchers. Nevertheless, most existing approaches only support component selection based on predefined rules and strategies. Because of the limitation of pure rule-based approach, context-awareness can not be well supported. In this paper, we propose a software component selection framework for mobile pervasive computing. Our approach adopts the case-based reasoning technique to provide proactive component selection. Context-awareness and personalization are embodied in the reasoning and selection process. As a proof of concept, we developed and evaluated a context-aware personal communicator (CAPC) application using adaptive component selection, with a synthesized execution trace obtained from real-life E-mail softwares ported 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.

Ontological user modelling and semantic rule-based reasoning for personalisation of Help-On-Demand services in pervasive environments

“…The work in [30] used CBR to provide context-awareness within a smart home environment. Within their research, this reasoning technique was used to adapt specific services to suit the changing needs of users according to their preferences.…”

Using SWRL and ontological reasoning for the personalization of context-aware assistive services

“…This method can enable personalization as it is able to create beneficial results despite inaccuracies with past case information. The work in [16] used CBR to provide contextawareness within a smart home environment. Within their research, this reasoning technique was used to adapt specific services to suit the changing needs of users according to their preferences (stored within individual cases).…”

BetterLife 2.0: Large-Scale Social Intelligence Reasoning on Cloud

“…One the other hand, Case-based Reasoning (CBR) technique has long been used in context-aware recommendation systems as a reasoning technique, which is the process of solving new problems based on the solutions of similar past problems [6], [7]. Similarity-based retrieval is a beneficial feature of case-based recommenders [8].…”