IoT Wireless and Cloud Computing Course

Lesson 1 • IoT Data Flow and Message Patterns

Explains publish-subscribe, request-response, and streaming data patterns. Sets the stage for cloud ingestion and protocol selection in later chapters.

Lesson 2 • Wireless Communication Fundamentals

Covers radio frequency basics, signal propagation, and modulation schemes. Provides the physical-layer knowledge needed to evaluate wireless protocols.

Lesson 3 • IoT Architecture and Ecosystem Overview

Defines the layered IoT stack from perception to application. Grounds subsequent wireless and cloud topics in a unified system model.

Lesson 4 • Low-Power Wide-Area Network Protocols

Introduces LoRaWAN, Sigfox, and NB-IoT for long-range, low-power deployments. Connects protocol trade-offs to battery life and coverage planning.

Lesson 1 • CoAP for Constrained Devices

Covers CoAP request methods, observe mode, and block-wise transfers. Addresses resource-limited devices where MQTT overhead is prohibitive.

Lesson 2 • AMQP and Advanced Messaging Patterns

Examines AMQP exchanges, queues, and routing keys for enterprise IoT pipelines. Bridges device messaging to backend analytics systems.

Lesson 3 • MQTT Protocol Design and Operation

Details MQTT broker architecture, QoS levels, and topic hierarchies. Directly enables cloud broker integration covered in later chapters.

Lesson 4 • Protocol Security and Authentication

Applies TLS, certificate-based auth, and token schemes to IoT protocols. Establishes secure communication practices before cloud deployment.

Lesson 1 • Virtualization and Containerization Basics

Introduces VMs, containers, and orchestration concepts underpinning cloud IoT services. Prepares students for deploying microservices in later chapters.

Lesson 2 • Cloud Storage Options for IoT Data

Compares object, block, file, and time-series storage for IoT telemetry. Guides storage selection based on query patterns and retention needs.

Lesson 3 • Cloud Cost Management and Optimization

Explains pricing models, reserved capacity, and cost monitoring tools. Enables students to design cost-efficient IoT cloud architectures.

Lesson 4 • Cloud Service Models Explained

Distinguishes IaaS, PaaS, and SaaS and their relevance to IoT workloads. Anchors cloud decision-making for device management and analytics chapters.

Lesson 1 • Device Monitoring and Health Management

Implements connectivity monitoring, heartbeat detection, and alert routing. Connects device health data to cloud dashboards and incident workflows.

Lesson 2 • IoT Platform Architecture Overview

Maps the functional components of cloud IoT platforms including hubs, registries, and rules engines. Frames the hands-on platform work in subsequent sections.

Lesson 3 • Device Provisioning and Onboarding

Covers zero-touch provisioning, enrollment groups, and attestation mechanisms. Reduces manual onboarding effort for large-scale IoT deployments.

Lesson 4 • Over-the-Air Firmware Update Management

Designs OTA update pipelines including delta updates, rollback, and deployment groups. Ensures fleet reliability and security patch delivery at scale.

Lesson 1 • Batch and Lambda Architecture Integration

Combines real-time stream and batch processing layers for complete IoT analytics. Prepares students for advanced analytics and machine learning integration.

Lesson 2 • Stream Processing Fundamentals

Introduces windowing, aggregation, and stateful processing for real-time IoT data. Enables anomaly detection and alerting without batch delays.

Lesson 3 • Message Enrichment and Transformation

Applies schema validation, unit conversion, and reference data lookups to raw telemetry. Produces clean, contextualized data for analytics and storage.

Lesson 4 • High-Throughput Data Ingestion Patterns

Covers event hub architectures, partitioning, and consumer groups for IoT scale. Establishes the ingestion layer that feeds all downstream processing.

Lesson 1 • Device-Level Security Controls

Implements secure boot, hardware security modules, and firmware signing on IoT devices. Prevents unauthorized code execution and physical tampering.

Lesson 2 • Identity and Access Management for IoT

Designs device identity lifecycle, role-based access, and least-privilege policies. Ensures only authorized entities interact with devices and cloud services.

Lesson 3 • Network Security for IoT Deployments

Configures network segmentation, VPNs, and intrusion detection for IoT traffic. Isolates device networks from enterprise systems to limit breach impact.

Lesson 4 • IoT Threat Modeling and Risk Assessment

Applies STRIDE and attack surface analysis to IoT system components. Produces a prioritized risk register that guides security control selection.

Lesson 1 • Edge-to-Cloud Synchronization

Designs store-and-forward, conflict resolution, and data reconciliation between edge and cloud. Maintains data consistency across intermittent connectivity scenarios.

Lesson 2 • Fog Computing and Hierarchical Architectures

Extends edge concepts to multi-tier fog nodes aggregating data from multiple edge sites. Prepares students for large-scale industrial and smart-city deployments.

Lesson 3 • Edge Computing Concepts and Drivers

Defines edge computing benefits including latency reduction, bandwidth savings, and offline resilience. Positions edge as a complement to cloud in the IoT architecture.

Lesson 4 • Local Data Processing and Filtering

Implements local stream processing, aggregation, and anomaly detection at the edge. Reduces cloud ingestion costs by sending only relevant data upstream.

Lesson 1 • IoT Analytics Patterns and Dashboards

Builds real-time and historical dashboards using time-series queries and visualization tools. Translates raw telemetry into actionable operational insights.

Lesson 2 • ML Model Deployment at Edge and Cloud

Deploys trained models as edge modules and cloud inference endpoints. Balances inference latency, cost, and accuracy across deployment targets.

Lesson 3 • End-to-End IoT System Design

Applies architecture patterns including reliability, scalability, and observability to full IoT solutions. Integrates all prior chapter skills into a cohesive design process.

Lesson 4 • Performance Tuning and Continuous Improvement

Profiles end-to-end latency, throughput bottlenecks, and cost inefficiencies in live systems. Establishes feedback loops for ongoing system optimization.