Due to the heterogeneity of ubiquitous wireless systems, which may be found in populated areas, there is a growing interest in data analytics and network configurations that open up a range of new possibilities and applications with the forthcoming ecosystem of the wireless technologies. Toward this goal, our research line deals with RF-based positioning techniques, IoT networks, machine learning tools in wireless systems and, spectrum sharing strategies.

Expertise

RF-based positioning and activity detection:

• Non-intrusive detection of people in WiFi networks.
• Machine learning techniques in expert systems.

Cognitive radio and spectrum sharing:

• Development of spectrum sensing algorithms: adaptive techniques, cooperative networks.
• Development of spectrum access strategies: licensed shared access, opportunistic access, databases and radio environment maps.
• Prototype implementation with software-defined radio (SDR) frameworks.

IoT networks:

• IoT network planning.

         -Efficient deployment and dimensioning.

         -Coverage in IoT LPWA networks.

• New paradigms of coordinated management and cooperation in massive IoT systems.

         -Network management and optimization.

         -Massive data collection.

• Implementation on LPWA network development kits.

EHUCOUNT Dataset:

Wi-Fi CSI Dataset for device-free counting people according to:

I. Sobron, J. Del Ser, I. Eizmendi and M. Vélez, "Device-Free People Counting in IoT Environments: New Insights, Results, and Open Challenges," in IEEE Internet of Things Journal, vol. 5, no. 6, pp. 4396-4408, Dec. 2018. doi: 10.1109/JIOT.2018.2806990

The following dataset consists of a zip file which contains a README text file and six mat files compatible with MATLAB and OCTAVE.

Download here