Towards reducing bandwidth consumption in publish/subscribe systems

Abstract: Efficient data collection is one of the key research areas for 5G and beyond, since it can reduce the network burden of transferring massive data for various data analytics and machine learning applications. Specifically, 5G offers great support for massive deployment of IoT devices, and the number of IoT devices is exploding.There are mainly two complementary ways for achieving efficient data collection: one is integrating data processing into the collection process via e.g. data filtering, aggregation; the other one is reducing the amount of the data needs to be transferred via e.g. data compression/approximation.In this thesis, efficient data collection is studied from the mentioned two perspectives. In particular, we introduce enhanced syntax and functionalities to the message queueing telemetry transport (MQTT) protocol, such as data filtering and data aggregation. Furthermore, we enhance the flexibility of MQTT by supporting customized or user-defined functions to be executed in the MQTT broker, and thus data processing in the broker will not be constrained to the predefined processing functions. Lastly, dual prediction is studied for reducing the data transmissions by maintaining the same learning model on both sides of the sender and receiver. In particular, we study and prototype least mean square (LMS) as the dual prediction algorithm. Our implementations are based on MQTT and the benefits are shown and evaluated via experiments using real IoT data.