1334 Edge Review: Architecture Patterns and Decision Frameworks

1334.1 Learning Objectives

Time: ~15 min | Level: Intermediate | Unit: P10.C10.U02

By the end of this chapter, you will be able to:

Identify Architecture Patterns: Distinguish between edge, fog, and cloud computing tiers and their roles
Apply Decision Frameworks: Use structured decision trees to select optimal processing locations
Evaluate Trade-offs: Assess latency, bandwidth, privacy, and cost trade-offs for different architectures
Match Use Cases: Map IoT application requirements to appropriate architectural patterns
Compare Massive vs Critical IoT: Understand the distinct requirements of high-volume delay-tolerant versus low-latency mission-critical deployments

1334.2 Prerequisites

Required Reading: - Edge Compute Patterns - Edge computing basics - Edge Comprehensive Review - Full review

Related Chapters: - Edge Review: Calculations - Formulas and practice problems - Edge Review: Deployments - Real-world patterns

For Beginners: Why Architecture Matters

Think of IoT architecture like choosing where to cook a meal:

Edge (your kitchen): Fast, private, but limited equipment
Fog (neighborhood restaurant): More equipment, serves multiple homes
Cloud (factory kitchen): Massive scale, can serve thousands, but delivery takes time

The right choice depends on what you are cooking (your data), how quickly you need it (latency), and how many people you are serving (scale). This chapter helps you make that choice systematically.

1334.3 Core Concepts Summary

1334.3.1 Edge Computing Terminology

Concept	Definition	Key Characteristics
Edge Computing	Processing data near its source (IoT devices)	Low latency (<10ms), reduced bandwidth, local autonomy
Fog Computing	Intermediate layer between edge and cloud	Aggregation, preprocessing, hierarchical architecture
Cloudlet	Small-scale data center at network edge	VM-based edge services, mobile edge computing
MEC (Multi-access Edge Computing)	Telco edge infrastructure at cell towers	5G integration, ultra-low latency, operator-managed
Edge Gateway	IoT Reference Model Level 3	Evaluate, format, reduce data before cloud
Data in Motion	Streaming data from devices (Levels 1-3)	Event-driven, real-time processing
Data at Rest	Stored data in databases (Level 4+)	Batch processing, historical analysis

1334.3.2 IoT Reference Model Four Levels

Level	Name	Function	Processing Type
Level 1	Physical Devices & Controllers	Sensors, actuators, embedded systems	Local sensing, basic control
Level 2	Connectivity	Communication protocols, gateways	Reliable data transport
Level 3	Edge Computing	Data reduction, filtering, formatting	Real-time preprocessing
Level 4	Data Accumulation	Storage systems, databases	Long-term storage, batch analytics

1334.3.3 Edge Gateway Three Functions (EFR Model)

Function	Purpose	Examples
Evaluate	Filter low-quality or invalid data	Discard out-of-range values, check sensor health
Format	Standardize data for cloud ingestion	Convert units, normalize timestamps, add metadata
Reduce (Distill)	Minimize data volume before transmission	Downsample, aggregate, compute statistics

1334.4 Edge vs Cloud Decision Matrix

1334.4.1 Architectural Trade-offs

Factor	Edge	Cloud	Hybrid
Latency	<10ms (local)	50-200ms (network delay)	Variable (critical=edge, batch=cloud)
Bandwidth Cost	Low (minimal WAN traffic)	High (continuous uploads)	Medium (selective sync)
Processing Power	Limited (constrained devices)	Unlimited (elastic scaling)	Flexible (offload when needed)
Data Privacy	Local (stays on-premise)	Concerns (third-party servers)	Selective (sensitive=edge)
Maintenance	Distributed (physical access)	Centralized (remote updates)	Complex (dual management)
Storage Capacity	Limited (local disks)	Petabyte-scale	Tiered (hot=edge, cold=cloud)
Reliability	Single point of failure	Redundant infrastructure	Failover capability
Cost Model	CapEx (upfront hardware)	OpEx (pay-as-you-go)	Mixed

1334.4.2 Use Case Suitability

Application	Best Architecture	Justification
Autonomous Vehicles	Edge-heavy	<10ms latency required for safety, no cloud dependency
Predictive Maintenance	Hybrid	Edge for anomaly detection, cloud for fleet-wide trends
Smart Home	Edge-heavy	Privacy, works during internet outages
Video Surveillance	Hybrid	Edge for motion detection, cloud for long-term storage
Agricultural Monitoring	Hybrid	Edge for irrigation control, cloud for historical analysis
Industrial Safety	Edge-heavy	Real-time shutdowns, no network dependency
Customer Analytics	Cloud-heavy	Aggregate across locations, complex ML models
Energy Optimization	Hybrid	Edge for load balancing, cloud for demand forecasting

1334.5 Architecture Patterns

1334.5.1 Hierarchical Edge-Fog-Cloud Architecture

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flowchart TB
    subgraph Device["Device Layer (Edge)"]
        D1[IoT Device 1<br/>Sensor Data] --> EG1[Edge Gateway]
        D2[IoT Device 2<br/>Sensor Data] --> EG1
        D3[IoT Device 3<br/>Sensor Data] --> EG2[Edge Gateway]
        D4[IoT Device 4<br/>Sensor Data] --> EG2
    end

    subgraph Fog["Fog Layer (Intermediate)"]
        EG1 --> FN1[Fog Node<br/>Aggregation]
        EG2 --> FN1
        EG1 --> FN2[Fog Node<br/>Processing]
        FN1 --> FN2
    end

    subgraph Cloud["Cloud Layer (Centralized)"]
        FN2 --> CS[Cloud Storage]
        CS --> CA[Cloud Analytics]
        CA --> BD[Big Data<br/>Processing]
    end

    style EG1 fill:#2C3E50,stroke:#16A085,color:#fff
    style EG2 fill:#2C3E50,stroke:#16A085,color:#fff
    style FN1 fill:#16A085,stroke:#2C3E50,color:#fff
    style FN2 fill:#16A085,stroke:#2C3E50,color:#fff
    style CS fill:#E67E22,stroke:#2C3E50,color:#fff
    style CA fill:#E67E22,stroke:#2C3E50,color:#fff
    style BD fill:#E67E22,stroke:#2C3E50,color:#fff

Figure 1334.1: Three-tier edge-fog-cloud architecture showing data flow from devices through edge gateways, fog aggregation nodes, to cloud storage and analytics

1334.5.2 Alternative View: Processing Decision Tree

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flowchart TD
    Start[IoT Data<br/>Processing Decision] --> Q1{Latency<br/>Requirement?}

    Q1 -->|< 10ms Critical| Edge[Process at Edge]
    Q1 -->|10-100ms| Fog[Process at Fog]
    Q1 -->|> 100ms OK| Cloud[Process in Cloud]

    Edge --> E1[Local control loops<br/>Safety shutdowns<br/>Real-time response]
    Fog --> F1[Regional aggregation<br/>Floor-level analytics<br/>Moderate compute]
    Cloud --> C1[Historical analytics<br/>ML model training<br/>Fleet-wide trends]

    E1 --> Cost1[CapEx: Hardware<br/>Latency: 1-5ms<br/>Bandwidth: Minimal]
    F1 --> Cost2[Mixed: Hardware + Cloud<br/>Latency: 10-50ms<br/>Bandwidth: Medium]
    C1 --> Cost3[OpEx: Pay-per-use<br/>Latency: 50-200ms<br/>Bandwidth: High]

    style Edge fill:#16A085,stroke:#2C3E50,color:#fff
    style Fog fill:#E67E22,stroke:#2C3E50,color:#fff
    style Cloud fill:#2C3E50,stroke:#16A085,color:#fff
    style E1 fill:#16A085,stroke:#2C3E50,color:#fff
    style F1 fill:#E67E22,stroke:#2C3E50,color:#fff
    style C1 fill:#2C3E50,stroke:#16A085,color:#fff

Figure 1334.2: Decision tree for selecting processing location based on latency requirements, showing trade-offs between edge (fast, hardware costs), fog (balanced), and cloud (flexible, bandwidth costs) processing

1334.5.3 Data Reduction Pipeline

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flowchart LR
    Raw[Raw Sensor Data<br/>10,000 samples/hour<br/>4 bytes each<br/>40 KB/hour] --> Eval[Evaluate<br/>Remove invalid<br/>5% filtered<br/>38 KB/hour]

    Eval --> Format[Format<br/>Standardize units<br/>Add metadata<br/>38 KB/hour]

    Format --> Reduce[Reduce/Distill<br/>Aggregate to<br/>min/max/avg<br/>10 samples/hour]

    Reduce --> Output[Output to Cloud<br/>10 samples/hour<br/>40 bytes/hour<br/>1000x reduction]

    style Raw fill:#7F8C8D,stroke:#2C3E50,color:#fff
    style Eval fill:#2C3E50,stroke:#16A085,color:#fff
    style Format fill:#16A085,stroke:#2C3E50,color:#fff
    style Reduce fill:#E67E22,stroke:#2C3E50,color:#fff
    style Output fill:#27AE60,stroke:#2C3E50,color:#fff

Figure 1334.3: Edge gateway data reduction pipeline demonstrating 1000x volume reduction through evaluation, formatting, and aggregation stages

1334.6 Massive IoT vs Critical IoT

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flowchart TB
    subgraph Massive["Massive IoT (High Volume, Delay Tolerant)"]
        M1[Millions of devices<br/>Low data rate<br/>Long battery life]
        M2[Use Cases:<br/>Smart meters<br/>Asset tracking<br/>Agriculture]
        M3[Characteristics:<br/>Latency: seconds-minutes<br/>Reliability: 90-95%<br/>Cost: Ultra-low]
        M1 --> M2 --> M3
    end

    subgraph Critical["Critical IoT (Low Latency, High Reliability)"]
        C1[Thousands of devices<br/>High data rate<br/>Always powered]
        C2[Use Cases:<br/>Autonomous vehicles<br/>Industrial safety<br/>Remote surgery]
        C3[Characteristics:<br/>Latency: <10ms<br/>Reliability: 99.999%<br/>Cost: High]
        C1 --> C2 --> C3
    end

    style M1 fill:#16A085,stroke:#2C3E50,color:#fff
    style M2 fill:#16A085,stroke:#2C3E50,color:#fff
    style M3 fill:#16A085,stroke:#2C3E50,color:#fff
    style C1 fill:#E67E22,stroke:#2C3E50,color:#fff
    style C2 fill:#E67E22,stroke:#2C3E50,color:#fff
    style C3 fill:#E67E22,stroke:#2C3E50,color:#fff

Figure 1334.4: Comparison between Massive IoT (high volume, delay tolerant) and Critical IoT (low latency, high reliability) deployment patterns

1334.7 Edge vs Cloud Decision Framework

1334.7.1 Comprehensive Decision Tree

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flowchart TD
    Start{Latency requirement<br/><10ms?} -->|Yes| Edge[Edge Processing<br/>Required]
    Start -->|No| Safety{Safety-critical?<br/>Cannot depend<br/>on network?}

    Safety -->|Yes| Edge
    Safety -->|No| Volume{High data volume?<br/>>100 GB/day?}

    Volume -->|Yes| Hybrid1[Hybrid: Edge filter<br/>+ Cloud storage]
    Volume -->|No| Analytics{Need historical<br/>analytics across<br/>multiple sites?}

    Analytics -->|Yes| Hybrid2[Hybrid: Edge real-time<br/>+ Cloud analytics]
    Analytics -->|No| Privacy{Privacy concerns?<br/>Data must stay<br/>local?}

    Privacy -->|Yes| Edge
    Privacy -->|No| Cloud[Cloud Processing<br/>Sufficient]

    Edge --> EdgeBenefits[<10ms latency<br/>Network independent<br/>Local privacy<br/>Limited compute]

    Hybrid1 --> HybridBenefits[Bandwidth savings<br/>Local + Global<br/>Flexible<br/>Complex management]
    Hybrid2 --> HybridBenefits

    Cloud --> CloudBenefits[Unlimited compute<br/>Easy management<br/>Global access<br/>Network dependent]

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    style Safety fill:#2C3E50,stroke:#16A085,color:#fff
    style Volume fill:#2C3E50,stroke:#16A085,color:#fff
    style Analytics fill:#2C3E50,stroke:#16A085,color:#fff
    style Privacy fill:#2C3E50,stroke:#16A085,color:#fff
    style Edge fill:#E67E22,stroke:#2C3E50,color:#fff
    style Cloud fill:#16A085,stroke:#2C3E50,color:#fff
    style Hybrid1 fill:#7F8C8D,stroke:#2C3E50,color:#fff
    style Hybrid2 fill:#7F8C8D,stroke:#2C3E50,color:#fff

Figure 1334.5: Decision tree for selecting edge, cloud, or hybrid IoT architecture based on application requirements

1334.7.2 Workload Placement Guidelines

Workload Type	Best Location	Justification
Safety-critical control	Edge	Cannot depend on network
Immediate alerting	Edge	<10ms response needed
Data filtering/validation	Edge	Reduce bandwidth 10-100x
Local dashboards	Edge	Works during outage
Sensor fusion	Edge/Fog	Combine multiple inputs locally
Anomaly detection (rules)	Edge	Simple thresholds
Anomaly detection (ML)	Fog	GPU for inference
Historical analysis	Cloud	Access to all locations
Model training	Cloud	Massive datasets, compute
Regulatory reporting	Cloud	Centralized compliance
Cross-site optimization	Cloud	Global view needed

1334.8 Summary and Key Takeaways

1334.8.1 Core Architecture Principles

Process data as close to the source as possible to minimize latency and bandwidth
Edge gateways perform Evaluate-Format-Reduce (EFR) functions before cloud upload
IoT Reference Model has 4 levels: devices, connectivity, edge, cloud storage
Massive IoT (high volume, delay tolerant) vs Critical IoT (low latency, high reliability)
Hybrid architectures combine edge real-time processing with cloud analytics

1334.8.2 Architecture Selection Summary

Aspect	Edge Advantage	Cloud Advantage
When to use	Real-time, safety-critical, privacy-sensitive	Historical analysis, complex ML, cross-site aggregation
Data strategy	Filter and reduce locally	Store everything for long-term insights
Processing	Simple rules, lightweight ML	Deep learning, big data analytics
Failure mode	Local autonomy during outage	Redundant, always-available infrastructure

1334.9 What’s Next

Continue your edge computing review with:

Edge Review: Calculations - Formulas, bandwidth savings, power optimization, and practice problems
Edge Review: Deployments - Real-world deployment patterns, technology stack, and security considerations

Return to: Edge Topic Review - Main review index