Embedded Systems and Internet of Things

Lecture: Thursday  |  Tutorial and Lab: Tuesday  |  Lectures: Offline

Announcements, assignment submission, and quiz evaluations via Google Classroom.

Grading

Midsem = 25 marks  |  End sem = 40 marks  |  Internal Assessment = 35 marks

No incompletes before mid-sem or end-sem — Talk to the TAs/instructor as soon as possible if you run into real problems. Please turn in working code: large software systems aren't just about producing working code, they're about producing code other people can understand. That is why we have group projects.

Course Outline

Module 1 — Introduction to IoT and Cyber-Physical Systems

1. What is IoT? Basic concepts, history, ecosystem
2. What is a CPS? How CPS differs from IoT and from a plain embedded system (the sense→compute→actuate feedback loop, real-time/safety constraints)
3. The four-layered IoT architecture (ITU-T)
4. Overview of CPS domains: industrial control, robotics, autonomous vehicles, smart grid
5. Why IoT/CPS now — enabling technologies

Module 2 — Embedded Systems Foundations

1. Microcontrollers vs. microprocessors; embedded system architecture; Programming embedded devices (Arduino, ESP32, Raspberry Pi)
2. Real-time operating systems (RTOS) — tasks, scheduling, interrupts
3. Memory, power and resource constraints in embedded design
4. Model-Based Design with MATLAB/Simulink - moving from hand-written C to executable models, simulate-before-you-build
5. Timed automata basics: clocks, clock constraints, guards, invariants
6. Lab : Simulink basics: block diagrams, signal flow, continuous vs. discrete-time blocks; Simulink Coder / Embedded Coder: auto-generating C code from a model and deploying to a microcontroller
7. Lab : Hardware-in-the-loop (HIL) concept — testing generated code against a simulated plant before physical deployment
8. Lab : Modeling embedded control logic in Stateflow (Simulink's statechart tool): a device mode-manager or a traffic-light/washing-machine-style example Integrating Stateflow charts with Simulink block diagrams — mixed discrete-event + continuous-time models

Module 3 - Cyber-Physical Systems Concepts

1. The sensing–computation–actuation loop; open-loop vs. closed-loop control
2. Basics of feedback control (PID, stability, response)
3. Hybrid systems and timing/real-time modelling
4. CPS case studies: SCADA/industrial control, autonomous vehicles, smart grid, robotics
5. Digital twins — modelling the physical system in software

Module 4 : IoT Application Development and Data Analytics

1. Sensor classification (physical, chemical, biological); key characteristics (accuracy, resolution, sensitivity, calibration, drift)
2. Common sensors: temperature/humidity, motion/IMU, proximity, optical, gas, biosensors
3. MEMS technology and miniaturization
4. Actuator types: motors, servos, relays, solenoids, pneumatic/hydraulic actuators
5. Signal conditioning, ADC/DAC, sensor fusion basics
6. Lab : Interfacing sensors/actuators with a Raspberry Pi

Module 5 — Communication Technologies: Non-IP and IP-Based

1. Communication models: device-device, device-gateway, device-cloud, back-end data sharing
2. Non-IP / short-range: Bluetooth/BLE, Zigbee, Z-Wave, RFID/NFC, LoRa
3. IP-based: WiFi (802.11), Ethernet (802.3), WiMax, cellular (2G/3G/4G/5G, NB-IoT)
4. Network layer: IPv4, IPv6, 6LoWPAN, RPL routing; Transport: TCP/UDP
5. Application layer protocols: HTTP/REST, MQTT, CoAP, AMQP, WebSocket, XMPP
6. Software-Defined Networking (SDN) for IoT
7. Lab : Working with MQTT and CoAP

Module 6 — Data Analytics for IoT

1. Data acquisition and preprocessing; edge vs. cloud analytics
2. Data acquisition and preprocessing; edge vs. cloud analytics
3. ML/DL for IoT: classification, regression, anomaly detection, time-series forecasting
4. Big data tools: Hadoop, MapReduce, Spark; stream processing basics
5. Lab : utitlizing Hadoop/MapReduce for IoT data analytics

Module 6 — Data Analytics for IoT

1. Data acquisition and preprocessing; edge vs. cloud analytics
2. Data acquisition and preprocessing; edge vs. cloud analytics
3. ML/DL for IoT: classification, regression, anomaly detection, time-series forecasting
4. Big data tools: Hadoop, MapReduce, Spark; stream processing basics
5. Lab : utitlizing Hadoop/MapReduce for IoT data analytics

Module 7 — Cloud, Fog and Edge Computing for IoT

1. Cloud service models: IaaS, PaaS, SaaS; role of cloud as an IoT enabler
2. IoT cloud platforms: AWS IoT, Azure IoT, ThingSpeak, Google Cloud IoT
3. Fog computing architecture; edge computing and edge AI/TinyML
4. Big data tools: Hadoop, MapReduce, Spark; stream processing basics
5. Lab : deploying a pipeline on a cloud IoT platform

Module 8 — — Security in IoT and CPS

1. Threat landscape for constrained devices; attack types (DoS, spoofing, MITM, physical tampering, side-channel)
2. Security by layer: device/hardware, communication (TLS/DTLS), cloud/application
3. Authentication and key management for constrained devices
4. CPS-specific security: attacks on control systems (e.g., Stuxnet), safety vs. security trade-offs
5. Privacy concerns and relevant standards

Module 9 — Applications, Fault Tolerance and Research Challenges

1. Application domains: smart grid, healthcare, Industry 4.0, agriculture, smart cities, autonomous systems/li>
2. Fault tolerance and reliability in distributed IoT/CPS deployments/li>
3. Interoperability, standardization and scalability challenges
4. Energy efficiency and real-time guarantees
5. Open research directions: edge AI, digital twins, 6G-enabled IoT, CPS security, sustainability

References

1. Pethuru Raj and Anupama C. Raman (CRC Press), The Internet of Things: Enabling Technologies, Platforms and Use Cases
2. Arshdeep Bahga and Vijay Madisetti, Internet of Things: A Hands-on Approach
3. IEEE Internet of Things Journal
4. IEEE Transactions on Mobile Computing

Grading Policy

• C1 component: weekly quiz, lab evaluation
• C2 component: weekly quiz, lab evaluation, written examination
• C3 component: lab evaluation, written examination, project evaluation
• Term project: either an IoT concept implementation project or a research idea. Done in groups (max 4 students per group). Project ideas and research topics are uploaded to Google Classroom within the first week of classes; groups are free to choose their own topic, subject to approval by the instructor or TAs.

Announcements

• Project submission: contact the TAs for the detailed problem statement of your project assignment and the milestones to be achieved during mid-sem and end-sem evaluation. Each group presents its work at both mid-sem and end-sem and is evaluated on the basis of the milestones assigned beforehand. If a student does not submit the assignments, their grade will remain as incomplete.
• For project topics and submission-related instructions, visit the Google Classroom page of the course.

Important Links

• Life Simplified with Connected Devices
• What is IoT?
• Smart Grids in India by TU Delft

Lectures & Weekly Schedule

The slides provided as lectures and additional resources have been compiled from different sources. Thanks to the original authors for their slides.

Announcements, assignment submission, and quiz evaluations happen via Google Classroom.