| Summary: | Context-aware computing is a mobile computing paradigm that helps designing and implementing next generation smart applications, where personalized devices interact with users in smart environments. Development of such applications are inherently complex due to these applications adapt to changing contextual information and they often run on resource-bounded devices. Most of the existing context-aware development frameworks are centralized, adopt clientserver architecture, and do not consider resource limitations of context-aware devices. This thesis presents a systematic framework to modelling and implementation of multi-agent context-aware rule-based systems on resource-constrained devices, which includes a lightweight efficient rule engine and a wide range of user preferences to reduce the number of rules while inferring personalized contexts. This shows rules can be reduced in order to optimize the inference engine execution speed, and ultimately to reduce total execution time and execution cost. The use of the proposed framework is illustrated using five different case scenarios considering different smart environment domains.
|
|---|