Data-driven Analysis of Internet of Things Technologies
The Internet of Things (IoT) deployment is underway all over the world. This makes possible the experimental analysis of several IoT technologies in real scenarios and use cases, aiming to pinpoint possible issues and moving forward enhancements and optimization.
The IoT paradigm has been proposed since two decades, and its deployment is expected to connect up to 75 billion devices by 2025, with an economic impact of around $11.1 trillion per year. Due to extreme heterogeneity of application domains and requirements, a large variety of IoT technologies has gradually emerged over the years. Considering coverage and energy efficiency, Low-Power Short-Area Networks (LPSANs) and Low-Power Wide-Area Networks (LPWANs) have recently emerged. On the one hand, LPSANs include Near Field Communications (NFC), ZigBee, Bluetooth and its Low Energy version (BLE), and specific WiFi protocols. On the other, LPWANs include proprietary systems working in the unlicensed spectrum, such as LoRaWAN and Sigfox, and 3GPP-standardized cellular-based technologies, such as NarrowBand IoT (NB-IoT) and Long Term Evolution for Machines (LTE-M). Altogether, these technologies enable massive machine-type connectivity and additional scenarios and services, thus playing a key role in advanced communication paradigms.
The goal is to perform a practical, experimental, and data-driven analysis of IoT technologies. In order to do so, we envision to create a small IoT testbed at the MOSAIC Department, comprising of real IoT devices having multi-connectivity capabilities (WiFi, Bluetooth, LoRaWAN, SigFox, and NB-IoT). This makes possible the connection to operational networks and in turn their experimental validation.
Deep knowledge of IoT technologies, practical experience with real devices, Data-driven and Machine Learning (ML)-oriented result analysis.
Foundations of wireless communications and ML, Python programming, Matlab knowledge (optional).