Edge and Fog Computing Module Guide
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Depth: Practitioner
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Your guide: Edge Eddie
“Send the decision, not the raw feed — the edge earns its keep in milliseconds and megabytes saved.”
Start With the Module
This section: Start With the Module — This guide maps the current learning path for Edge Fog.
This guide maps the current learning path for Edge Fog. The module contains 20 chapters, linked below in the order learners can study them.
Follow Edge Eddie as a battery gateway chooses local, nearby, or cloud work from one measured workload budget.
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This battery gateway has three places to work.
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Measure time and energy for this one job.
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Pick the place that meets this job's needs.
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Test again when the conditions change.
Chapters by Part
This section: Chapters by Part
Edge-Fog Basics
- 2 Edge, Fog, and Cloud: The Three Tiers – Edge & Fog Computing
- 3 Edge, Fog, and Cloud: Architecture – Edge & Fog Computing
- 4 Latency Budgets for Tier Choice – Edge & Fog Computing
- 5 Bandwidth Optimization at the Edge – Edge & Fog Computing
- 6 Choosing Edge, Fog, or Cloud – Edge & Fog Computing
- 7 Edge-Fog Use Cases – Edge & Fog Computing
- 8 Operational Failure Modes – Edge & Fog Computing
- 9 Practice: Edge-Fog Computing – Edge & Fog Computing
Edge-Cloud Integration
Inspect Figure 1 to ask how one workload record carries normal and failed paths, rules, owners, and field proof from placement into large-scale operation.
Read Figure 1 across its four views. Keep the same work record in mind. Place assigns work and fallback to the device, site, or fleet. Architect maps data, command, care, and health paths across time, trust, control, version, and fault lines. Integrate gives those paths rules for identity, data shape, queues, replay, updates, rollback, and safe commands. Operate adds state control, failover, logs, repair, and proof of oversight. Follow the return arrow last. A field result can reopen any earlier choice instead of being filed as an operations issue alone.
Edge AI & ML
Inspect Figure 2 to ask which review lens should reopen when evidence about the model, run time, target board, or field use is missing.
Read Figure 2 from Fundamentals into the iterative target-fit group. Fundamentals asks why work is local and what fallback applies. TinyML tests MCU fit, Optimization responds to measured model-budget failures, and Hardware checks the target and runtime; arrows between them show that these are not one-way deployment stages. Applications owns field validation, rollout, monitoring, drift, rollback, and human review, and its evidence can reopen an earlier lens. The separate Lab lane is simulated practice. Repeat its measurements on the intended sensor path and hardware before making any release claim.
- 12 Edge AI Fundamentals: Why and When – Edge & Fog Computing
- 13 TinyML on Microcontrollers – Edge & Fog Computing
- 14 Model Optimization for Edge AI – Edge & Fog Computing
- 15 Hardware Accelerators for Edge AI – Edge & Fog Computing
- 16 Edge AI Fit and Deployment – Edge & Fog Computing
- 17 Practice: TinyML Gesture Classification – Edge & Fog Computing
Fog Architecture
Fog Production
Design Trade-off Scenarios
Choose a Starting Point
This section: Choose a Starting Point — If this subject is new, follow the parts and chapters in the order shown above.
If this subject is new, follow the parts and chapters in the order shown above. If you already have a specific design or troubleshooting question, use the relevant part heading and open the direct chapter link whose title matches that question.
Use the sidebar and site search for supporting material; use this chapter map as your main route through the module.
How to Use This Material
This section: How to Use This Material
- Start with the first chapter in the relevant part when the topic is unfamiliar.
- Use direct chapter links for study plans, lab preparation, and review evidence.