Apply fog computing to industrial scenarios: Design predictive maintenance systems
Understand autonomous vehicle requirements: Recognize why edge is mandatory for safety-critical decisions
Implement privacy-preserving architectures: Process sensitive data locally
Calculate real-world benefits: Quantify latency, bandwidth, and cost improvements
336.2 Use Case 1: Smart Factory Predictive Maintenance
336.2.1 Scenario
Manufacturing facility with hundreds of machines, each instrumented with vibration, temperature, and acoustic sensors generating data at 1kHz sampling rate.
336.2.2 Requirements
Real-time anomaly detection (<100ms)
Predictive failure alerts (hours to days advance warning)
Minimal network load
Continued operation during internet outages
336.2.3 Fog Architecture
Edge Tier: Machine Controllers - Collect sensor data at 1kHz - Basic filtering and feature extraction - Detect critical threshold violations (immediate shutdown)
Fog Tier: Factory Edge Servers - Deployed per production line - Run ML models for anomaly detection - Analyze vibration patterns, thermal signatures - Predict component failures - Store recent data (rolling 24-hour window) - Generate maintenance work orders
Cloud Tier: Enterprise Data Center - Aggregate data from all factories - Train improved ML models - Long-term trend analysis - Supply chain and inventory optimization - Dashboards for management
336.2.4 Benefits
Latency: Immediate shutdown on critical failures; real-time anomaly alerts Bandwidth: 99.9% reduction (1kHz data -> event summaries) Reliability: Continues operating during internet outages Value: Reduced downtime, optimized maintenance, extended equipment life
Show code
{const container =document.getElementById('kc-edge-8');if (container &&typeof InlineKnowledgeCheck !=='undefined') { container.innerHTML=''; container.appendChild(InlineKnowledgeCheck.create({question:"A factory has vibration sensors sampling at 10,000 Hz on 100 machines. Each sample is 4 bytes. If the factory's internet connection is 10 Mbps, can raw data be transmitted to the cloud in real-time?",options: [ {text:"Yes - 10 Mbps is more than enough for sensor data",correct:false,feedback:"Let's calculate: 100 machines x 10,000 samples/s x 4 bytes = 4,000,000 bytes/s = 32 Mbps. This exceeds the 10 Mbps connection by 3.2x."}, {text:"No - data rate is 32 Mbps, exceeding the 10 Mbps connection",correct:true,feedback:"Correct! 100 machines x 10,000 Hz x 4 bytes = 4 MB/s = 32 Mbps. The factory would need 3.2x more bandwidth, or must use edge/fog processing to filter and aggregate data locally."}, {text:"Yes - compression will reduce the data to fit",correct:false,feedback:"Even with 50% compression (16 Mbps), data still exceeds available bandwidth. High-frequency sensor data often doesn't compress well due to noise."}, {text:"Cannot determine without knowing the sensor type",correct:false,feedback:"We have all the information needed: number of sensors, sampling rate, and sample size. The calculation is straightforward."} ],difficulty:"hard",topic:"edge-fog-bandwidth" })); }}
336.3 Use Case 2: Autonomous Vehicle Edge Computing
336.3.1 Scenario
Connected autonomous vehicles requiring instant decision-making with sensing, communication, and coordination.
Fog Tier: Roadside Units (RSUs) - Deployed along roads at intersections - Coordinate multiple vehicles - Provide local traffic information - Extend sensor range (communicate what’s around corner) - Handle V2V message relay
Fog Tier: Mobile Edge Computing (MEC) at Base Stations - Cellular network edge - Regional traffic management - HD map updates - Software updates - Non-critical cloud services
Cloud Tier: Central Data Centers - Fleet management - Route optimization - Long-term learning - Software development - Regulatory compliance
Cooperative Perception: 1. RSU combines sensor data from multiple vehicles 2. Shares augmented awareness (blind spot information) 3. Vehicles receive enhanced situational awareness 4. Better decisions through cooperation
336.3.5 Benefits
Safety: Life-critical response times achieved Bandwidth: Terabytes/day of sensor data processed locally Reliability: Critical functions independent of cloud connectivity Scalability: Millions of vehicles supported through distributed architecture
Show code
{const container =document.getElementById('kc-edge-9');if (container &&typeof InlineKnowledgeCheck !=='undefined') { container.innerHTML=''; container.appendChild(InlineKnowledgeCheck.create({question:"In an autonomous vehicle system, which processing task should happen at the roadside fog unit (RSU) rather than on the vehicle (edge) or in the cloud?",options: [ {text:"Collision avoidance braking decisions",correct:false,feedback:"Collision avoidance requires <10ms latency. Even nearby RSUs add 20-50ms network latency. This must happen on-vehicle (edge) where latency is 3-8ms."}, {text:"Multi-vehicle coordination at intersections",correct:true,feedback:"Correct! RSUs coordinate multiple vehicles simultaneously, sharing 'what's around the corner' information, optimizing traffic flow, and preventing conflicts. This requires aggregating data from many vehicles - impossible for a single vehicle to do alone."}, {text:"Training improved AI models from driving data",correct:false,feedback:"AI model training requires massive compute (GPU clusters) and is not time-sensitive. This is a cloud task, done overnight or weekly."}, {text:"Compressing video for storage",correct:false,feedback:"Video compression happens on-vehicle (edge) before any transmission. Sending raw video to RSUs would overwhelm the network."} ],difficulty:"medium",topic:"edge-fog-architecture" })); }}
336.4 Use Case 3: Privacy-Preserving Architecture
Fog computing enables privacy-preserving architectures that process sensitive data locally while still providing useful insights and services.
336.4.1 Privacy Challenges in IoT
Personal Data Exposure: - Video surveillance - Health monitoring - Location tracking - Behavioral patterns
Cloud Privacy Risks: - Data breaches - Unauthorized access - Third-party sharing - Government surveillance
336.4.2 Fog-Based Privacy Preservation
Local Processing Principle: “Process data where it’s collected; send only necessary insights”
Techniques:
Data Minimization: - Extract only required features - Discard raw sensitive data - Aggregate individual data
Example: Smart home: Count people in room (1 number) instead of sending video stream
Anonymization: - Remove personally identifiable information - Blur faces in video - Generalize location (area vs. precise GPS)
Differential Privacy: - Add noise to data before transmission - Provide statistical guarantees on privacy - Enable aggregate analytics while protecting individuals
Encryption: - End-to-end encryption for necessary transmissions - Homomorphic encryption for cloud processing of encrypted data - Secure multi-party computation
336.4.3 Architecture Pattern
Edge Devices: Collect raw sensitive data
Fog Nodes:
Extract privacy-safe features
Anonymize or aggregate
Encrypt if transmission needed
Cloud:
Receives only privacy-preserved data
Performs authorized analytics
Returns results to fog/devices
Example: Healthcare Monitoring - Wearable: Collects heart rate, location, activity - Fog (smartphone): Detects anomalies, triggers alerts - Cloud: Receives only: “Anomaly detected at approximate location X” - Privacy preserved: Raw health data never leaves personal fog node
Show code
{const container =document.getElementById('kc-edge-7');if (container &&typeof InlineKnowledgeCheck !=='undefined') { container.innerHTML=''; container.appendChild(InlineKnowledgeCheck.create({question:"A European company deploys smart cameras in stores to count customers. GDPR requires that personally identifiable information (faces, license plates) cannot leave the EU. How should the system be architected?",options: [ {text:"Cloud-only in US data center - lower costs, better ML capabilities",correct:false,feedback:"This violates GDPR! Sending raw video with faces to a US data center exposes PII outside the EU and could result in massive fines (up to 4% of global revenue)."}, {text:"Cloud-only in EU data center - keeps data in EU",correct:false,feedback:"While this keeps data in the EU, transmitting raw video to any cloud still exposes PII. A data breach could still violate GDPR's data minimization principle."}, {text:"Edge processing with face blur before transmission - only anonymized counts reach cloud",correct:true,feedback:"Correct! Edge/fog processing anonymizes data at the source: blur faces, count people, send only aggregated statistics. Raw video never leaves the store, satisfying GDPR's data minimization and purpose limitation principles."}, {text:"Store video locally only - never connect to any network",correct:false,feedback:"While maximally private, this loses the business value of aggregated analytics across stores. Edge processing provides privacy compliance while enabling cloud analytics on anonymized data."} ],difficulty:"medium",topic:"edge-fog-privacy" })); }}
336.5 Worked Example: Compute Offloading Decision for Agricultural Drone
Scenario: A fleet of agricultural drones surveys 1,000-acre farms for crop disease detection. Each drone carries cameras, processes images to detect diseased plants, and triggers precision pesticide spraying. The question: should image processing happen on the drone (edge), at a ground station (fog), or in the cloud?
Deferred (ground station buffer -> cloud overnight): Full-resolution image archival, detailed analysis for treatment planning, model retraining data collection
Calculate costs:
Option A (Cloud - if possible): $3/hour x 2 hours/day x 20 drones x 365 = $43,800/year + bandwidth: 34.6 TB/day x $0.09/GB x 365 = $1,136,340/year = $1,180,140/year (IMPOSSIBLE anyway)
Option C (Edge + deferred cloud): Drone GPUs: $400 x 20 = $8,000 (one-time), Ground station: $15,000 (one-time), Cloud analysis: $3/hour x 4 hours/day x 365 = $4,380/year, Overnight bandwidth: ~100 GB/day x $0.09 x 365 = $3,285/year
Result: Edge processing is the only viable option for real-time crop disease detection. The hybrid architecture uses on-drone inference for immediate decisions (32ms latency) while deferring full-resolution uploads for overnight cloud analysis.
Key Insight: When bandwidth is the bottleneck (which it almost always is for high-resolution imagery), edge processing becomes mandatory regardless of cost. The compute offloading decision is often determined by physics (data size vs link capacity) rather than economics. In this case, even unlimited budget couldn’t make cloud processing work in real-time. Design for edge-first when dealing with high-bandwidth sensors (cameras, lidar, radar) and use fog/cloud for deferred analytics.
336.6 Summary
Edge and fog computing enable use cases that cloud-only architectures cannot support. From factory floors requiring sub-100ms anomaly detection to autonomous vehicles needing 10ms collision avoidance, local processing is often a fundamental requirement rather than an optimization.
Key takeaways:
Smart factory: 99.9% bandwidth reduction with real-time anomaly detection
Autonomous vehicles: Edge processing mandatory for safety-critical decisions
Privacy: Process sensitive data locally to comply with regulations
Agricultural drones: Bandwidth physics make edge processing the only option
336.7 What’s Next?
Learn about common mistakes and how to avoid them in edge/fog implementations.