"Prediction-based strategies for reducing data transmissions in the IoT"

-Dr. Gabriel Martins

Thesis supervisors: Dr. Boris Bellalta and Dr. Simon Oechsner





Brief Description of the Thesis
Part of the data in the Internet of Things (IoT) will be generated by wireless sensor nodes organized in Wireless Sensor Networks (WSNs). Moreover, modern applications rely on the knowledge acquired by WSNs to trigger other systems and sensed data has become critical to avoid economic and living losses. However, these wireless sensor nodes are mainly designed to have low costs, which implies constrained memory and energy supplies, and does not permit high data transfer rates.  Therefore, it is important to optimize data transmissions in WSNs to support more wireless sensor nodes and a higher diversity of sensed parameters.
My thesis (called "Prediction-Based Strategies for Reducing Data Transmissions in the IoT") extends a paradigm that exploits WSNs to the utmost: data that can be predicted does not have to be transmitted. It will permit a strict control over the quality of the reported data without being harmed by the adoption of a higher number of sensor nodes; hence, collaborating to the IoT's growth.
Experience as a PhD student
As a PhD student, it was a great challenge and a wonderful period in which I could work with excellent people from different fields. For example, I had the opportunity to teach local undergraduate students and to participate in a European project and meet people from several other countries. Apart from that, people from Barcelona were very welcoming and made this experience even more significant to me. I am truly thankful to my supervisors and the UPF staff that provided me all the support I needed during these years.