Unit_5_ML_IoT_CU3.0.pdf

Module - 2 Internet of Things Units for Discussion Internet of Things IoT Development Sensors & Boards Actuators Unit - 1 Unit...

Module - 2 Internet of Things Units for Discussion Internet of Things IoT Development Sensors & Boards Actuators Unit - 1 Unit - 2 Unit - 3 Networking & IoT Machine Protocol Concept Learning with IoT Unit - 4 Unit - 5  Unit - 5 Machine Learning with IoT  DISCLAIMER The content is curated from online/offline resources and used for educational purpose only.  Discussion: Some Recent Trends Source : Reference link Learning Objectives You will learn in this lesson: Significance of ML in IoT sector. Understand IoT Cloud features. Learn to connect device to cloud Acquire real time sensor data from cloud Integration of Machine learning with IoT Source : www.freepik.com/  Introduction: ML with IoT One of the top trending topics IoT Data fuels the ML engines Can work together to improve lives. Source : Reference link  Advantages of ML with IOT Gather big data to avail best services. Quick and accurate responses Avoid unnecessary spending to optimize business Can spot inefficiency and recommend best practices Secured M2M communication NLP: Speak with machines Source : Reference link Applications of ML with IOT Healthcare Used to monitor patients remotely and provide real-time health diagnosis Retail To enhance customer experiences and improve the efficiency of supply chain management. Manufacturing To optimize production processes, improve quality control, and reduce waste. Agriculture To improve crop yields, minimize waste, and reduce the use of harmful chemicals.  Transfer IoT Data to Cloud Services Register and login to Cumulocity IoT Source : Reference link  Lab -1 Transferring IoT Data to Cloud Services Cumulocity Dashboard Source : Reference link Connect Mobile Sensor Data to Cloud Click Connect Smartphone in the Welcome widget Install Cumulocity app and scan the QR code shown on your PC’s web browser  Lab -2 Registering a Raspberry PI on Cloud Registering Raspberry PI on Cloud Clone below repository on RaspberryPi https://github.com/SoftwareAG/cumulocity-devicemanagement-docker-example.git Open repository and notedown device id cat cumulocity-devicemanagement-docker-example/Agents/config/config.ini Registering Raspberry PI on Cloud.. On dashboard click Register Device Enter your device's serial number into the 'Device ID' field Source : Reference link Registering Raspberry PI on Cloud.. On dashboard Device shows awaiting state Run docker in Raspberry PI sudo bash cumulocity-devicemanagement-docker-example/start.sh Refer back to dashboard to accept connection and now your device is connected to cloud.  Lab -3 Collecting Sensors Data from Cloud Collecting Sensors Data from Cloud Open Cumulocity IOT Device Management dashboard Click on All Devices shown in left Collecting Sensors Data from Cloud.. Open your connected device of interest. Refer to Measurement section to view the device activity Click on More to download device sensor data Machine Learning on Sensor Data Downloaded data is majorly in form of unsupervised data. Try to aggregate data from various sensors and apply Machine Learning Algorithms. Identify the clusters and data homogeneity.  Lab -4 Machine Learning on Sensor Data Conclusion In this session we have learned: What does it mean by the term ML-IoT? How to connect sensor data from mobile to cloud? How to connect device data from Raspberrypi to the cloud? How to manage devices on the Cumulocity cloud platform? How to implement machine learning on IoT data? Different aspects that come under IoT cloud services. References https://www.softwareag.com/corporate/products/cumulocity-iot/overview.html https://www.softwareag.com/en_corporate/resources/iot/article/machine-learning.html https://www.iotforall.com/ https://aws.amazon.com/iot-core/ https://azure.microsoft.com/en-us/services/iot-hub/ https://cloud.google.com/iot-core https://www.ibm.com/cloud/watson-iot-platform https://docs.microsoft.com/en-us/samples/azure/iot-samples/ https://cloud.google.com/community/tutorials/iot-overview https://aws.amazon.com/getting-started/hands-on/build-end-to-end-ml/ Let’s Start Quiz 1. What is the primary purpose of integrating Machine Learning with the Internet of Things (ML-IoT)? a) To enhance network security b) To automate data collection and analysis c) To reduce IoT device costs d) To improve IoT device battery life Answer: B To automate data collection and analysis Quiz 2. Which of the following is a key challenge in implementing ML algorithms on IoT devices? a) Limited computational resources b) Abundance of power supply c) High-speed internet connectivity d) Expensive hardware components Answer: A Limited computational resources Quiz 3. In an ML-IoT system, what role does edge computing play? a) It handles all machine learning tasks in the cloud. b) It manages device connectivity. c) It processes data locally on IoT devices. d) It serves as a database for IoT data storage. Answer: C It processes data locally on IoT devices Quiz 4. What is the significance of data preprocessing in ML-IoT applications? a) It ensures that IoT devices are connected to the internet. b) It prepares raw sensor data for analysis by removing noise and outliers. c) It determines the physical location of IoT devices. d) It encrypts communication between IoT devices and the cloud. Answer: B It prepares raw sensor data for analysis by removing noise and outliers. Thank You