Description
Date depot: 17 septembre 2019
Titre: NDN/NFN for Real-Time In-Network Query Processing
Directeur de thèse:
Giovanni PAU (BSS)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Systèmes et réseaux
Resumé:
Presently, plenty of low-cost IoT devices are available, allowing rapid deployment of Wireless Sensor Networking (WSN) for data collection in multiple and large geographical areas. However, most of the system may still rely on a centralized data processing service concept which generally faces the scalability problem.
Named Data Networking (NDN) is an implementation of the Information-Centric Networking (ICN) paradigm. ICN uses naming to indicate directly to the resource wherever it is stored. ICN address does not tie to a specific host, as opposed to host-centric addressing the traditional Internet Protocol. So, an ICN resource can be stored or replicated at multiple locations. NDN uses the hierarchical namespace as the address to identify the resource. It can provide an abstraction layer that enables a distributed database with clustering and computational capabilities. Named-Function Networking (NFN) which is an extension of ICN, enables functional language abstraction in the namespaces, allow including programming expression in the NDN naming hierarchy.
In-network computation is a technique that performs mathematical calculations as well as data query processing computations on several networked, collaborative nodes. The NDN/ICN and NFN name abstraction can enable this capability. An NDN/NFN capable node can break down a query into smaller sub-queries, distribute these sub-queries to several nodes that own the informational contents, and then merge the results back.
In this thesis, we study the technical issues associated with NDN and NFN on the application of real-time in-network processing on time series data from WSN. We aim to propose, implement, and evaluate an efficient NDN/NFN data harvesting and query processing scheme for geographically deployed WSNs.
Doctorant.e: Mekbungwan Preechai