Context-aware dissemination of information and services in heterogeneous network environments

Abstract

The Internet of Things and the advent of cloud computing promotes a global scale Machine-to-Machine (M2M) communication. Dissemination of information and advertising services to the targeted nodes is an important aspect in the performance of the future large scale network systems. With the contextual information inferred from available information, delivery of services can be performed in a more efficient way. Context-awareness is a key ingredient in any ubiquitous and pervasive system and provides intelligence to the system, allowing computing devices make appropriate and timely decisions on behalf of users. Semantic support for M2M communication can be enhanced if it leverages the available information and contextual data. In this paper we present a methodology for a context-aware publish-subscribe based approach to dissemination of information and services and a service selection method which is based on the idea of utilizing network information as services that is delivered via application programming interfaces. We propose a fuzzy MADM method and a context similarity measure. The proposed method of information dissemination is based on mining the value of a node based on information inferred from the contextual information of the node and contextually similar nodes. We take into account the quality of contextual information in aggregating contextual information from different sources.