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PraNA Project

These posters and demos are presented by PraNA projects funded by TEKES.

Off-the-Shelf Software-defined WiFi NetworksSeppo HätönenWiFi networks were one of the first use-cases for Software-defined networking (SDN). However, to deploy a software-defined WiFi network today, one has to rely on research prototypes with availability, documentation, hardware requirements, and scalability issues. To alleviate this situation, we demonstrate two simple techniques to bring SDN functionality to existing WiFi networks and discuss their benefits and short-comings. Researchers can use our techniques to convert their existing WiFi testbeds into software defined WiFi testbeds.  Our two techniques thus significantly lower the barrier-to-entry for deploying software-defined WiFi networks.
My operator is favoring X applicationMohammad HoqueWe shed light on application, system and in-network services contribution in providing quality of service through analyzing traffic. We study traffic from different applications. It is also possible that application may have different behavior when they are in different networks, therefore, we also investigate traffic pattern for originating from different networks. Finally, we shed light how different end points contribute in setting content. Our initial observation is that mobile applications rarely tag the flows with QoS requirements irrespective of the network type. Finally, we are developing an application for Android devices to probe the cellular networks around world to investigate their performance in dealing with various content type.
Edge computing with IoT HubsJulien MineraudThe wide digitalization of services drives the need for processing gigantic volumes of data to extract meaningful information. This phenomenon is emphasized with the paradigm of the Internet of Things where things consume and produce this data in real-time. Today, the data is transported and analyzed in the Cloud. However, the rise of IoT has forced network operators to bring more intelligence toward the edge of the network (where the data is produced). We believe that edge IoT platforms can be key elements in supporting edge computing capabilities of future networks. Therefore, we demonstrate the feasibility of performing distributed computing at the edge of the network for IoT-specific data. Our initial experiments on an IoT testbed show that distributing computation amongst physically close IoT devices can produce non-negligible gains of performances.
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