Internet-of-Things-IoT-Databases-and-Edge-Computing (1).pptx
- 1. Internet of Things (IoT) Databases and Edge Computing Presented by: Zain ul Hassan (22-Arid-4400), Tamoor Ali (22-Arid-4395), Muhammad Irfan (22-Arid-4382), Haider Ali (22-Arid-4361), Faraz Ahmed (22-Arid-4360) HA by Haider Ali
- 2. Introduction to IoT (Internet of Things) Definition The internet of things (IoT) refers to the network of physical devices, vehicles, buildings, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables them to collect and exchange data. Key Concepts IoT devices can sense their environment, gather data, and communicate with each other, and often with human users, through the internet.
- 3. Overview of IoT Database Concepts Data Storage IoT databases are designed to handle massive volumes of data generated by sensors and devices. Real-Time Analytics They facilitate real-time analysis of data, enabling immediate insights and decision-making. Scalability and Availability IoT databases need to be scalable to accommodate growing data volumes and ensure high availability for continuous operation.
- 4. Benefits of IoT Databases 1 Improved Efficiency Real-time data analysis optimizes processes and resource utilization. 2 Enhanced Decision Making Data-driven insights enable informed decisions and proactive problem-solving. 3 Personalized Experiences IoT databases support customized experiences tailored to individual user preferences. 4 New Business Models Data-driven services and monetization opportunities emerge, opening new revenue streams.
- 5. Challenges in Traditional Databases for IoT Data Volume Traditional databases struggle to handle the sheer volume of data generated by IoT devices. Real-Time Requirements Latency in traditional databases can hinder real-time analytics and critical decision-making. Device Heterogeneity Diverse devices with varying data formats and communication protocols pose challenges for integration.
- 6. Introduction to Edge Computing 1 Edge computing brings data processing closer to the source, reducing latency and improving responsiveness. 2 It enables real-time decision-making by processing data locally before sending it to the cloud. 3 Edge computing offers a distributed architecture for enhanced scalability and fault tolerance.
- 7. Advantages of Edge Computing for IoT Reduced Latency Data processing at the edge minimizes delays, crucial for real- time applications. Improved Responsiveness Faster response times enable quicker actions based on real- time data insights. Increased Bandwidth Efficiency Edge computing reduces the amount of data transmitted to the cloud, optimizing bandwidth usage.
- 8. Edge Computing Architecture 1 Cloud Centralized data storage and analysis. 2 Edge Server Data processing and aggregation. 3 IoT Devices Data generation and collection.
- 9. Integration of IoT Databases and Edge Computing 1 Data Collection IoT devices collect data and send it to the edge server. 2 Edge Processing The edge server pre-processes and analyzes data, reducing the volume sent to the cloud. 3 Cloud Storage and Analytics Data is stored in the cloud for long-term analysis and insights.
- 10. Use Cases and Examples 1 Smart Cities Traffic management, resource optimization, and public safety. 2 Industrial Automation Predictive maintenance, process optimization, and quality control. 3 Healthcare Remote patient monitoring, personalized medicine, and drug delivery. 4 Agriculture Precision farming, crop monitoring, and irrigation control.