| Summary: | Dissemination of information and advertising services to targeted nodes are important aspects in the performance of future large scale network systems. Context-awareness is a key ingredient in any ubiquitous and pervasive system and provides intelligence to the system, allowing computing devices to make appropriate and timely decisions on behalf of users. In this thesis, first the problem of access network selection based on the context of users to support intelligent services and applications that demand a certain quality of service level is addressed. As the contextual information are collected from various sources with uncertain quality, a decision methodology for access network selection that takes the quality of provided information into account is proposed and it is shown that it yields a more confident decision when the available information lack certainty. In addition, collected data that are uncertain and fuzzy in nature pose another problem for selecting services. Therefore, a service selection approach to leverage the context similarity among the users, services and applications to solve this problem is proposed. Information Centric Networking (ICN) is a shift in networking paradigm that is based on named data as the main token of networking instead of Internet Protocol (IP) addresses. Based on ICN, the problem of information dissemination is tackled by proposing the new notion of information topology and using the information about the spectral characteristics of the topology for an enhanced network coding scheme. The success of the proposed approach is demonstrated on the basis of achieving a better reliability and lowering the processing cost for the entire system. Furthermore, the feasibility of ICN for vehicular clouds is investigated and a method based on dimensionality reduction to reduce processing overhead is suggested. The proposed method enhances the control plane performance in support of real time applications in the presence of intermittent connectivity.
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