An Effective Seamless Connectivity Scheme for Ubiquitous Applications

The idea of ubiquity and seamless connectivity in networks is gaining more importance in recent times because of the emergence of mobile devices with added capabilities like multiple interfaces and more processing abilities. The success of ubiquitous applications depends on how effectively the user is provided with seamless connectivity. In a ubiquitous application, seamless connectivity encompasses the smooth migration of a user between networks and providing him/her with context based information automatically at all times. In this work, we propose a seamless connectivity scheme in the true sense of ubiquitous networks by providing smooth migration to a user along with providing information based on his/her contexts automatically without re-registration with the foreign network. The scheme uses Ubi-SubSystems(USS) and Soft-Switches(SS) for maintaining the ubiquitous application resources and the users. The scheme has been tested by considering the ubiquitous touring system with several sets of tourist spots and users.

Context aware M-commerce services: C-IOB model approach

Context-aware computing is useful in providing individualized services focusing mainly on acquiring surrounding context of user. By comparison, only very little research has been completed in integrating context from different environments, despite of its usefulness in diverse applications such as healthcare, M-commerce and tourist guide applications. In particular, one of the most important criteria in providing personalized service in a highly dynamic environment and constantly changing user environment, is to develop a context model which aggregates context from different domains to infer context of an entity at the more abstract level. Hence, the purpose of this paper is to propose a context model based on cognitive aspects to relate contextual information that better captures the observation of certain worlds of interest for a more sophisticated context-aware service. We developed a C-IOB (Context-Information, Observation, Belief) conceptual model to analyze the context data from physical, system, application, and social domains to infer context at the more abstract level. The beliefs developed about an entity (person, place, things) are primitive in most theories of decision making so that applications can use these beliefs in addition to history of transaction for providing intelligent service. We enhance our proposed context model by further classifying context information into three categories: a well-defined, a qualitative and credible context information to make the system more realistic towards real world implementation. The proposed model is deployed to assist a M-commerce application. The simulation results show that the service selection and service delivery of the system are high compared to traditional system.