Internet of Things

Module 1: IoT Introduction and Fundamentals IoT Definition, Applications, Benefits/challenges IoT layers and components: Sensors, signal processing, data transmission (wired/wireless), data analysis. IoT levels based on complexity IoT hardware and computing platforms Lab: (2 labs) 1. IoT kit introduction: Hardware and software (programming environment), IDE 2. Embedded software relevant to microcontroller and IoT platform 3. Introduction to peripheral interfacing related to IoT-system design (Sensor, ADC, and wireless module available on kit). This can include SPI/I2C etc. 4. Peripheral interfacing like LEDs/ keys (display, keypad) Module 2: Signals, Sensors, Actuators, Interfaces Sensors, different types/classes of sensors, Sensor parameters: non-idealities, Sensitivity, SNR, power/energy, form-factor Sensor read-out, ADCs, interfacing of sensors Circuit component mismatch and mitigation techniques (calibration, chopping, autozeroing etc.) Datasheet aspects relevant to sensors, sensor selection Basic signal processing (sampling, filtering, quantization, computation, storage) Lab: (1 lab) 1. Interfacing sensor, ADC with the available board, programming for interfacing minimum of two sensors (temperature, soil moisture) simultaneously 2. Interfacing actuators Module 3: Networking, Communication and computing Introduction to basic Communication Network functioning: Layers, Spectrum bands used for IoT communications, Challenges in Networking of IoT Nodes IoT node access methods, technologies and protocols: WiFi, 5G, MQTT, LPWAN, LoRa, , IEEE 802.15.4, etc Cloud computing Optional: Other related topics ▪ Machine-to-Machine (M2M) and IoT Technology Fundamentals, Medium Access Control (MAC) Protocols for M2M Communications ▪ 5G Cellular Networks and 5G IoT Communications, Low-Power Wide Area Networks ▪ Wireless Communications and Networking: channel models, power budgets, data rates ▪ IoT security and privacy Lab: (3 labs) 1. Interfacing or using wireless modules with available boards/kits 2. Communication protocol for connectivity of IoT-CPS system with cloud (IoT platform) 3. Connectivity of gateway to cloud server and control using dashboard/webpage 4. Protocols for node communication to gateway/internet/cloud, and also among nodes (send-receive data) Module 4: Data Analysis Preprocessing, data handling, and computing with Python Basic statistics and probability relevant to IoT data analysis Linear regression, clustering, classification Supervised and unsupervised learning, distributed learning Visualization (dissemination) Lab: (2 labs) 1. To use and analyze data collected from sensors/cloud 2. Bring in closed loop application of data analytics (like controlling action/ alert) 3. Perform classification/prediction based on collected data 4. Use of basic visualization for data interpretation Module 5: Case studies and Project Discuss IoT case studies (these could be running throughout the modules) Lab: (2 labs) ◦ Put together a complete and practical IoT system with possibly more than one nodes connecting to a gateway. This will be connected to the cloud to perform data analysis, make inference, and provide actuating signals.