Cloud Computing_C113: Edge Computing and IoT Integration and AI in Cloud

Topic 1: Introduction to Edge Computing and Its Role in Modern Architectures

LO1: Define edge computing and explain its significance in modern distributed architectures

LO2: Describe how edge computing differs from traditional cloud computing in terms of data processing and latency

LO3: Analyze use cases demonstrating the impact of edge computing on performance and scalability

Topic 2: Understanding the IoT Device Ecosystem and Communication Protocols

LO1: Identify key components of the IoT ecosystem and describe their interactions

LO2: Explain common communication protocols (MQTT, CoAP, HTTP) and their relevance to IoT systems

LO3: Evaluate protocol selection for different IoT deployment scenarios based on efficiency and scalability

Topic 3: Edge-to-Cloud Integration: Concepts and Frameworks

LO1: Explain the principles of edge-to-cloud integration and its benefits for data-driven systems

LO2: Analyze frameworks that support seamless data flow between edge and cloud environments

LO3: Evaluate integration challenges such as data synchronization, latency, and interoperability

Topic 4: Designing Resilient and Scalable Edge Computing Architectures

LO1: Describe the design principles for creating resilient and scalable edge computing systems

LO2: Apply architectural patterns to optimize performance and reliability in distributed systems

LO3: Evaluate trade-offs between scalability, resilience, and cost efficiency in edge design

Topic 5: Latency and Bandwidth Considerations in Edge Computing

LO1: Define latency and bandwidth in the context of edge computing

LO2: Analyze how edge computing minimizes latency and optimizes bandwidth usage

LO3: Evaluate strategies for improving real-time responsiveness in edge-based networks

Quiz-I

Topic 6: Data Distribution Strategies for Edge and Cloud Environments

LO1: Explain different data distribution models between edge and cloud systems

LO2: Analyze how data locality affects performance and decision-making

LO3: Evaluate strategies for balancing data processing between edge nodes and the cloud

Topic 7: Cloud Platforms for Edge Computing (AWS IoT Greengrass, Azure IoT Edge, Google Edge TPU)

LO1: Describe the key features of major cloud-edge platforms and their architecture

LO2: Compare deployment and management approaches across AWS, Azure, and Google edge solutions

LO3: Evaluate the suitability of each platform for different industrial and enterprise use cases

Topic 8: Implementing Real-time Data Processing at the Edge

LO1: Explain the importance of real-time data analytics in edge computing environments

LO2: Demonstrate the use of stream processing frameworks for real-time insights

LO3: Evaluate edge processing solutions for latency reduction and efficiency

Topic 9: Deploying AI/ML Models on Edge Devices

LO1: Describe methods for deploying AI/ML models on constrained edge devices

LO2: Apply model optimization techniques such as pruning and quantization for edge deployment

LO3: Evaluate trade-offs between model accuracy and computational efficiency

Topic 10: Training and Managing AI Models in the Cloud for Edge Inference

LO1: Explain how cloud platforms support AI model training and lifecycle management

LO2: Analyze the process of model deployment from cloud to edge environments

LO3: Evaluate challenges in model versioning, updates, and continuous learning

Quiz-II

Topic 11: Data Lifecycle Management Across Edge, IoT, and Cloud Systems

LO1: Define the data lifecycle in distributed computing systems

LO2: Describe best practices for managing data creation, processing, and storage across environments

LO3: Evaluate compliance and governance considerations in distributed data management

Topic 12: Security Challenges in Edge and IoT Architectures

LO1: Identify security risks inherent in edge and IoT ecosystems

LO2: Explain how distributed architectures amplify attack surfaces and vulnerabilities

LO3: Evaluate mitigation techniques such as zero-trust frameworks and network segmentation

Topic 13: Implementing Encryption and Secure Data Transmission for Edge Devices

LO1: Describe encryption methods used to secure edge and IoT data transmission

LO2: Implement secure communication protocols for protecting data in transit

LO3: Analyze trade-offs between performance and encryption strength

Topic 14: Authentication and Identity Management in Distributed IoT Systems

LO1: Explain the principles of authentication and identity in distributed IoT systems

LO2: Apply identity management frameworks to secure device interactions

LO3: Evaluate challenges in maintaining identity integrity at scale

Topic 15: Access Control and Role Management for Edge/Cloud Devices

LO1: Define access control models suitable for distributed systems

LO2: Demonstrate how to implement role-based access control (RBAC) in edge/cloud platforms

LO3: Evaluate policy management tools for enforcing security and compliance

Topic 16: Monitoring and Managing Distributed Edge Environments

LO1: Describe monitoring tools and techniques for edge computing systems

LO2: Analyze performance metrics to maintain system health and reliability

LO3: Evaluate the role of automation and orchestration in large-scale edge management

Topic 17: Integration of Edge AI with Predictive Maintenance and Anomaly Detection

LO1: Explain how AI-driven analytics enable predictive maintenance at the edge

LO2: Implement anomaly detection models to identify performance irregularities

LO3: Evaluate the impact of predictive analytics on operational efficiency and cost savings

Topic 18: Case Studies in Edge-IoT-Cloud Solutions Across Industries

LO1: Describe real-world implementations of edge-IoT-cloud integration in key sectors

LO2: Analyze success factors and challenges in industrial edge deployments

LO3: Evaluate lessons learned from case studies to design effective edge-IoT-cloud solutions

Topic 19: Challenges and Future Trends in Distributed Edge and Cloud AI Systems

LO1: Identify emerging trends in edge AI, federated learning, and distributed intelligence

LO2: Analyze technical and ethical challenges shaping the evolution of edge-cloud ecosystems

LO3: Evaluate how innovation in AI and IoT will redefine next-generation computing architectures

Topic 20: Final Exam Review

LO1: Summarize key concepts from edge computing, IoT, and cloud AI integration

LO2: Review design and security principles for distributed computing architectures

LO3: Apply acquired knowledge to evaluate real-world edge-cloud integration scenarios

Final Exam