IOT DATA MANAGEMENT REQUIREMENTS AND ARCHITECTURE OF IOT.pdf
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This is regarding the Iot introduction with data management requirements. The Ppt covers the definition, wide range of applications, classification of Iot data, the needs for iot data management.
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IOT DATA MANAGEMENT REQUIREMENTS AND ARCHITECTURE OF IOT.pdf
- 2. IOT Network of physical objects that are connected to the internet and can communicate with each other and other systems.
- 3. ADVANTAGES OF IOT APPLICATIONS OF IOT • Smart Grids and energy saving • Smart cities • Smart homes/Home automation • Healthcare • Earthquake detection • Radiation detection/hazardous gas detection • Smartphone detection • Water flow monitoring • Traffic monitoring • Smart door lock protection system • Robots and Drones • Healthcare and Hospitals, Telemedicine applications • Biochip Transponders (For animals in farms) • Heart monitoring implants (Example Pacemaker, ECG real time tracking)
- 4. IoT data management is the process of collecting, storing, analyzing, and sharing from Internet of Things (IoT) devices data
- 5. IMPORTANCE OF IOT DATA MANAGEMENT Improved security Regulatory compliance Actionable insights Reduced costs Improved decision- making
- 6. Status data Location data Automation data Basic, raw data that shows the status of a device or system Data that shows the geographical location of a device or system. Often used in manufacturing, logistics, and warehousing Data created by automated devices and systems, such as smart thermostats and automated lighting Temperature of a smart thermostat,Battery level of a wearable device,Operational status of an industrial machine (e.g., running, idle, error)Health monitoring status (e.g., heart rate, oxygen levels, blood pressure),Smart door status (locked/unlocked) GPS coordinates of a vehicle, Location of a person (via wearable or mobile device), Asset tracking (e.g., RFID tags on products), Indoor location (using Bluetooth beacons or Wi-Fi triangulation), Real-time location of a drone during flight. Automated lighting control based on motion detection Heating/cooling adjustments based on occupancy or time of day Watering schedule for a smart irrigation system Automated door lock/unlock based on user proximity (geofencing)
- 7. IOT DATA MANAGEMENT REQUIREMENTS • Data Collection and Acquisition • Data Storage and Organization • Data Security and Privacy • Data Governance • Data Quality ManagementData Integration and Interoperability • Data Analysis and UsageData Backup and Recovery • Data Retention and Archiving • Compliance and Legal Requirements • Performance and Scalability • Data Collaboration and Sharing
- 8. Data Volume: • Data compression,deduplication, tiered storage. • cloud storage offers more flexibility and scalability. • The amount of data produced requires scalable storage solutions and fast data processing capabilities • IDC 2025 Report: Global IoT data will grow to 79.4 zettabytes (ZB) by 2025, driven by rapid device proliferation and high data velocity in sectors like healthcare, smart cities, and industrial IoT Data velocity: • High-velocity data streams demand powerful infrastructure with low-latency processing to ensure timely decision-making. • deploying in-memory databases, using edge computing to process data closer to its source, and implementing streaming analytics platforms. • Gartner IoT Report: By 2025, over 75 billion IoT devices will be connected, generating real-time data at speeds up to 20 Gbps (5G), enabling faster analytics and responses. Data variety: • structured, semi-structured, and unstructured data • solutions like data lakes, which can store varied types of data in their native format, and advanced analytics platforms, which can process mixed datasets. Data Security : • A comprehensive security framework that encompasses device security, network security, and application security can provide a multi- layered defense strategy. • Encrypting user passwords to protect them from hackers.
- 10. 1.Cloud Data Lakes: Hosted on cloud platforms (e.g., AWS, Azure, Google Cloud). Scalable and cost-effective. 2.On-Premises Data Lakes: Deployed within an organization’s infrastructure (e.g., Hadoop). Full control but higher maintenance. 3.Hybrid Data Lakes: Combine cloud and on-premises components for flexibility (e.g., Azure Arc). 4.Lakehouse Architecture: Combines features of data lakes and warehouses for unified analytics (e.g., Delta Lake, Redshift Spectrum). 5.Self-Managed Data Lakes: Built with open-source tools (e.g., Hadoop, Spark). Full control but complex setup. 6.Managed Data Lakes: Fully managed by cloud providers (e.g., AWS Lake Formation). Simplified setup and management. 7.Transactional Data Lakes: Support transactional data for real-time processing (e.g., Delta Lake, Apache Hudi). 8.Real-Time Data Lakes: Designed for near real-time data ingestion and analytics (e.g., Kafka, Flink). 9.Federated Data Lakes: Aggregate data from multiple sources without centralizing it (e.g., data virtualization tools).
- 11. Data Accuracy and Quality : • data collected from IoT devices is accurate, complete, and consistent. • Maintaining high data quality involves continuous monitoring and refinement of data collection methods. This includes establishing protocols for anomaly detection, error correction, and routine audits of data sources. Data Storage : • Traditional relational databases may struggle with the volume and variety of IoT data. • Organizations may consider NoSQL databases, time- series databases, or object storage solutions, providing greater flexibility and performance for IoT applications. Integrating cloud-based storage options provides additional scalability and accessibility. Cloud storage allows for dynamic allocation of resources, adapting to changing data loads. Data privacy • Privacy protection mechanisms include data anonymization, secure data sharing protocols, and user consent management systems. • Organizations should adopt a privacy-by-design approach, integrating privacy considerations into the development phase of IoT projects. • A website asking for user consent before collecting cookies.
- 12. Data Accessibility: • Factors to consider include network connectivity, user authentication, and interface usability. • To achieve optimal data accessibility, cloud-based storage and computing solutions can provide on- demand data access from anywhere, at any time. • APIs allow for efficient data exchange between disparate systems, allowing IoT data to be integrated into existing workflows and applications. Data Integration : • It requires middleware solutions that can connect data sources, transform data into compatible formats, and support real-time data flows. • technologies such as ETL (Extract, Transform, Load) tools, IoT platforms with built-in integration capabilities, and API management systems Data Analytics and Utilization : • Data analytics involves the systematic computational analysis of data or statistics. • Organizations must invest in advanced analytics platforms that support predictive analytics, machine learning algorithms, and data visualization techniques. These technologies transform raw IoT data into actionable intelligence, driving innovation.
- 13. IOT ARCHITECTURE