Internet of Things(Iot) Data Analytics.pptx

Internet of Things(Iot) Data Analytics (Data Collection, Processing, and Storage) (Role of big data and machine learning in Iot) Introduction:  Internet of Things (Iot) data analytics involves collecting analyzing and interpreting data generated by Iot devices. These devices, whi...

Internet of Things(Iot) Data Analytics (Data Collection, Processing, and Storage) (Role of big data and machine learning in Iot) Introduction:  Internet of Things (Iot) data analytics involves collecting analyzing and interpreting data generated by Iot devices. These devices, which include sensors, smart appliances, wearables, and more, continuously gather data and transmit it over the internet. The goal of Iot data analytics is to transform this vast amount of unsfructured data into meanigful insights insights that can inform decision- making and improve various processes. Data Collection  Data collection – Iot devices, such as sensors and actuators, collect data from their environment. This data can include various type of information , such as temperature, humidity, motion, lights level, and more. Data collection process involves.  Sensors: Devices that detect and measure physical properties.  Data Transmission: Using communication protocols like Wi-Fi, Bluetooth, and cellular networks to send data to a central system.  A Thermocouple Temperature Sensor is a device that measures Temperature or heat energy and converts it Into electrical signal that can be read by monitoring system. A PIR (Passive Infrared) Sensor MotionDetector Is a device that detects movements Within a specific area and triggers An action, such as turning on lights, sounding Alarm, or sending a notification.  Smart Appliances are Wi-Fi Enabled devices that connect to a smart hub or voice command system, such as Google Asssistant Alexa, or a dedicated smart home app. These appliances can be controlled remotely via your smartphone or through voice commands, offering convenience, efficiency , and enhanced functionality Here are some 4 common examples of smart appliances: Smart Refrigerators: These can monitor food Inventory , suggest recipes, and even allow you to See inside without opening door. Smart Oven: It can be preheated remotely; have precise temperature controls, and offer guided cooking instructions.  Smart speaker: Acts as a hub for your smart home, Allowing you to control other smart Devices with voice command. It can also play music, Provide Weather updates, and answer questions. Smart Lights: These can be controlled remotely Scheduled to turn on or off, and even change colors On your preferences.  Data Processing Once the data is collected, it needs to be processed to extract meaningful insights. This involves: Edge Processing: Some data processing is done locally on the device or nearby edge severs to reduce latency and bandwidth usage. This can include filtering, aggregation, and preliminary analysis. Cloud Processing: More complex processing tasks are often handled in the cloud, where powerful servers can perform advanced analytics, machine learning, and data integration.  Data Storage The processed data needs to be stored for future use, analysis, and decision making. Storage solutions in Iot system include: Local Storage: Data can be stored locally on the device or edge servers for quick access and low latency requirements. Cloud Storage: For scalability and long-term storage, data is often stored in cloud databases and data lakes. Cloud storage provide the flexibility to handle large volumes of data and supports various data management and retrieval operations.  Role of Big Data and Machine Learning in Iot Big data refers to the large amounts of data generated by Iot devices like sensors and cameras. Heres how it works in Iot: Data Collection: Iot devices collect data from their surroundings. For example, a temperature sensor records temperature readings. Data Storage: This data is stored in large databases, often in the cloud, which can handle huge amounts of information. Data Analysis: The stored data is analyzed to find used useful patterns and insights. For example, analyzing temperature data predict weather changes. Real-time Processing: Some data is processed immediately to make quick decisions, like turning on a fan when it gets too hot.  Machine Learning (ML) In Iot (ML) Helps Iot Systems learn from the data they collect and make smart decisions. Here’s how ML is used in Iot: Predictive Maintenance: ML can predict when machine might break down, maintenance can be done before it fails. Anomaly Detection: ML can spot unusual patterns in data, which might indicate a problem, like a security threat. Automation: ML can automate tasks based on data, reducing the need for human intervention. For example, automically adjusting lighting based on room occupancy. Personalization: ML can tailor services to individual preferences, like recommending music based on listening habits. Optimization: ML can improve the efficiency of Iot systems, like optimizing energy use in smart homes.  Combining Big Data and Machine Learning When big data and machine learning are combined in Iot, they create powerful applications: Smart Cities: Using data from various sensors manage traffic, save energy, enhance public safety. Healthcare: Monitoring patient data in real-time to provide personalized treatments and early detection of health issues. Industrial Iot: Improving manufacturing processes through predictive maintenance and quality control. By using big data machine learning, Iot systems become smarter and more efficient, providing valuable insights and services in many areas