Internet of Things and Robotics: a crash course
From volts and sensors to MQTT, security, kinematics, control, localisation and navigation, built around one running project: a Smart Greenhouse Rover. Twelve units with a PDF and runnable code each, a 14-lab track and an assessment bank. Built for a B.Tech student; simulation-first, no hardware needed.
Start with Unit 1 Download everythingThe running project
How this course works
1 · Read
Each unit is a web lesson (generated from the same source as its 15-20 page PDF) with derivations, worked numbers and exercises.
2 · Run
Every figure and every number comes from a script you can download and run; each unit's code zip includes the printed output of all of them.
3 · Be sceptical
Assumed numbers are labelled as assumed; firmware that was not run on hardware says so; external checks (paho-mqtt, cbor2, the MQTT and Mirai texts) are reported with what disagreed.
4 · Check
Every unit ends with graded exercises, a recall quiz with answers and a list of things to re-derive by hand.
The units
Foundations of IoT and Robotics
The four-layer architecture, the sense-think-act loop, and the thermostat that teaches delay, noise and sampling.
Electronics for Embedded Systems
Ohm's law, dividers, LEDs, PWM, ADCs, battery budgets and motor drivers, with numbers.
Sensors and Actuators
Accuracy against precision, calibration by least squares, aliasing, filters, encoders, servos and steppers.
Microcontrollers and Embedded Programming
GPIO, interrupts, debouncing, UART and I2C timing, and the sensor node as a state machine.
Networking and IoT Communication Protocols
Radios and link budgets, HTTP versus MQTT versus CoAP, and a home-made MQTT broker checked against real ones.
IoT Data, Cloud and Edge Computing
Payload formats, aggregation, anomaly detectors, a digital twin, edge versus cloud, and a dashboard.
IoT Security and Privacy
STRIDE, the Mirai botnet, authenticated commands, TLS cost, signed updates, ACLs and privacy.
Robot Kinematics
Frames, rotations, the two-link arm, the Jacobian, differential drive and odometry drift.
Control Systems for Robots
The motor model, P, PI and PID control, step-response metrics, tuning and windup.
Perception and Localisation
Gaussian noise, Bayes' rule, the Kalman filter, sensor fusion and occupancy grids.
Path Planning, Navigation and ROS
BFS, Dijkstra and A*, obstacle inflation, pure pursuit, and the ROS 2 concepts.
Capstone: the Smart Greenhouse Rover
Every layer in one system test: success rate, energy, latency, loss and the report.
Labs, assessment and playground
Laboratory track
Fourteen labs, each mapped to code you download with the units; every lab runs in simulation, with an optional ESP32 box.
Assessment
A question bank with answers, sample mid- and end-semester papers and an answer key.
Playground
Eleven interactive widgets: divider, ADC and aliasing, debouncing, MQTT QoS, detectors, 2-link arm, odometry drift, PID tuner, Kalman filter, occupancy grid, A*.