Activity Context Modeling in Context-Aware Environment

Abstract

Abstract:The explosion of mobile devices has fuelled the advancement of pervasive computing to provide personal assistance in this information-driven world. Pervasive computing takes advantage of context-aware computing to track, use and adapt to contextual information. The context that has attracted the attention of many researchers is the activity context. There are six major techniques that are used to model activity context. These techniques are key-value, logic-based, ontology-based, object-oriented, mark-up schemes and graphical. This paper analyses these techniques in detail by describing how each technique is implemented while reviewing their pros and cons. The paper ends with a hybrid modeling method that fits heterogeneous environment while considering the entire of modeling through data acquisition and utilization stages. The modeling stages of activity context are data sensation, data abstraction and reasoning and planning. The work revealed that mark-up schemes and object-oriented are best applicable at the data sensation stage. Key-value and object-oriented techniques fairly support data abstraction stage whereas the logic-based and ontology-based techniques are the ideal techniques for reasoning and planning stage. In a distributed system, mark-up schemes are very useful in data communication over a network and graphical technique should be used when saving context data into database.

Artificial Neural Network Context-Aware Reasoning Engine Model for Predicting User-Ranked Activities

“…The completed questionnaire was normalized by using zeros and ones which resulted in 32 unique records as depicted in Table I. A value of one (1) indicates that a category of activity is available for ranking and selection, otherwise, the value is zero (0). Table I shows the input and output values.…”

“…The ability to design an intelligent reasoning engine for context-aware mobile applications has prompted many researchers to devise various mechanisms. Previous mechanisms included if-then rules, key-value, logic-based and ontology-based models [1]. Following the use of ontology-based models complemented with other technologies in acquiring activity context, Dey [2], Naeem et al [3] and Sherif and Alesheikh [4] suggested the need to use machine learning approach in designing context-aware applications.…”