Led by Prof. Laxmeesha Somappa, the "Sensors & Interfaces" module offers a comprehensive exploration into the intricate world of sensors, their characteristics, and their interfacing techniques. Lesson 1 serves as a foundational introduction to the realm of sensors, delving into their classifications and providing in-depth case studies on select sensor types like accelerometers, displacement/proximity sensors, and temperature sensors. Building on this foundation, Lesson 2 dives into the vital characteristics that define and differentiate sensors, covering aspects such as sensitivity, accuracy, and response time. The subsequent lessons shift focus to the crucial process of sensor readout, with Lesson 3 elucidating the concepts and design choices surrounding Analog to Digital Conversion (ADC) and quantization, and Lesson 4 highlighting the non-idealities one might encounter, including opamp specific issues.

Led by Prof. Gaurav S. Kasbekar, the "Networking of IoT Nodes" module delves deep into the mechanisms and challenges of connecting diverse IoT nodes, ensuring seamless communication and data exchange. Lesson 1 lays the foundation by introducing the concept of IoT nodes, their diverse applications in sectors like agriculture, healthcare, smart homes, and infrastructure, and outlines the challenges in their networking. Lesson 2 ventures into low power, low data rate networks, illuminating participants on the IEEE 802.15.4 standard, as well as cutting-edge technologies like LoRa and SigFox. In Lesson 3, the spotlight is on the integration of constrained devices, offering insights into 6LoWPAN, header compression, fragmentation, and routing protocols. Rounding off the module, Lesson 4 dives into messaging protocols essential for IoT, with a comprehensive overview of CoAP and MQTT, their formats, and communication intricacies.

Led by Prof. Rajbabu Velmurugan, in the "Data Analysis for IoT" module, learners delve into the critical aspects of analyzing vast amounts of data generated by IoT devices. Beginning with an exploration of the significance of data analysis specifically for IoT in Lesson 1, participants will be introduced to the components, feasibility, and various types of learning methods tailored for IoT datasets, with hands-on Python-based implementations. Lesson 2 offers a deeper dive into specific data analysis techniques, encompassing linear regression, time-series models, and clustering methodologies. With a blend of theoretical insights and practical demonstrations, this module ensures that learners are well-equipped to harness the power of data in the IoT domain. Concluding with a special message from the CEO of TIH-IoT. This module is a blend of theoretical knowledge, practical insights, and real-world applications, ensuring learners are well-equipped to navigate the networking landscape of IoT.