Why?
Data science increasingly runs on data produced by connected devices. This course shows where that data comes from, how it moves, and how it is stored, so you can build datasets from sensors instead of only analysing ready-made ones.
What?
An introduction to the Internet of Things and data acquisition: applications, layered architectures, the IoT reference model, communication theory and the networking protocols that connect devices. The second part is an individual research project on an IoT or cloud computing topic.
Curriculum:
Introduction to IoT
Definition of the Internet of Things, its evolution, key components (sensors, actuators, gateways, cloud) and why it matters for data science.
IoT Applications
Smart homes, smart cities, healthcare, agriculture, industry and other application domains, with case studies.
IoT Architecture
Layered IoT architectures, from the perception layer through the network layer to the application layer.
IoT Reference Model
Standard reference models for IoT, their levels and how data flows between them.
Communication Theory
Fundamentals of signals, transmission, modulation and wireless communication used by IoT devices.
IoT Protocols
Communication and networking protocols for IoT such as MQTT, CoAP, Bluetooth, ZigBee and LoRa, and how to choose between them.
Research Project
Choosing a topic in IoT or cloud computing, writing a literature review, adding a contribution and presenting results in an IEEE style report.
Notes
Lectures are slide based and theory heavy. The research project (20% of the grade) is written on an IEEE two-column template, so start the literature review early.