This module introduces the foundational architecture of Internet of Things systems. Learners explore how IoT systems are built using embedded devices, sensors, communication protocols, cloud platforms, and data analytics. The module also explains different IoT system levels, helping learners understand how system complexity increases from simple local control to large-scale, cloud-based real-time systems used in real-world applications.

This module introduces learners to IoT system design using the IoT-A Reference Model. Learners understand why IoT systems are complex and how design abstraction helps manage this complexity. Through two case studies, a smart lighting system (Level 1) and a weather monitoring system (Level 6), learners see how the same design steps apply to both simple and large-scale IoT systems. The module builds a clear end-to-end understanding from requirements to devices and applications.

This module introduces learners to IoT end devices, which form the interface between the physical world and the digital IoT system. Learners will study the building blocks of an IoT end device, criteria for selecting suitable hardware platforms, and a detailed example using the STM32 microcontroller. The module also covers sensor and actuator interfacing, use of GPIO, ADC, timers, and interrupts, and communication with edge devices and cloud platforms. Finally, learners will design complete IoT end-device solutions through smart lighting and weather monitoring system (WMS) case studies.

This module introduces the networking foundations required for IoT systems. Learners will study the TCP/IP protocol stack, understand how data moves across networks and compare wired and wireless communication. The module then explores IoT communication models such as client–server, publisher–subscriber and peer-to-peer, along with infrastructure-less networks. Finally, it covers cloud computing concepts for IoT, including virtualisation and cloud service models (SaaS, PaaS, IaaS), and explains how cloud platforms support scalable IoT applications.

This module introduces learners to the networking technologies used in Internet of Things (IoT) systems. It covers short-range, medium-range, and long-range wireless standards such as Wi-Fi, Bluetooth, ZigBee, NFC and LoRa. The module also explores industrial networking concepts, including fieldbus protocols, industrial Ethernet, intra-vehicular networks and the integration of industrial systems with IoT and cloud platforms. By the end of the module, learners will understand how different networking technologies are selected based on range, power, latency, reliability and application requirements.

This module introduces security challenges in Internet of Things (IoT) systems. It covers different types of IoT threats, real-world security incidents, key security terminologies, and commonly used security solutions. The module also focuses on device and hardware-level security issues, including physical attacks, hardware trojans, side-channel attacks and insecure hardware interfaces, along with methods to counter these threats.

This module focuses on security challenges in IoT end devices and the techniques used to protect them. Learners will understand why traditional security approaches do not work well for resource-constrained devices and how lightweight cryptography, hardware-based security, and secure software execution help address these issues. The module also introduces common cryptographic algorithms, digital signatures, task and memory management for security and future security challenges and solutions in IoT systems.

This module introduces learners to real-world applications of IoT across multiple domains. It explains how IoT systems are used in healthcare, transportation, smart environments, personal and social settings, and futuristic applications. Learners will understand use cases, system components, data flow, benefits, and challenges of deploying IoT solutions in different sectors.

This module focuses on the use of IoT technologies for early detection and monitoring of forest fires. Learners study fire detection requirements, fire risk assessment using weather data, system architecture, large-scale deployment strategies and real-time monitoring for rapid response and damage reduction.