187  Communication Models for IoT

187.1 Learning Objectives

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

• Compare Communication Patterns: Distinguish between request-response, publish-subscribe, and event-driven architectures
• Select Appropriate Models: Choose the right communication model based on IoT application requirements
• Implement Synchronous vs Asynchronous: Design systems using blocking and non-blocking communication patterns
• Apply Push and Pull Strategies: Determine when to push data to consumers versus when consumers should pull data
• Design Message Flows: Create efficient message routing patterns for multi-device IoT systems
• Evaluate Trade-offs: Analyze latency, bandwidth, reliability, and scalability implications of each model

187.2 Prerequisites

Before diving into this chapter, you should be familiar with:

• Networking Basics: Understanding TCP/IP, UDP, and basic network protocols provides foundation for communication models
• IoT Protocols Overview: Knowledge of MQTT, CoAP, HTTP, and WebSocket helps contextualize when each communication model is used
• Communication and Protocol Bridging: Understanding gateway functions and protocol translation complements communication model selection

TipFor Beginners: Communication Models Explained Simply

Think of communication models like different ways people can talk to each other:

Request-Response: Like asking a question and waiting for an answer. You call customer service, ask “What’s my balance?”, and they tell you. You can’t do anything else until they respond.

Publish-Subscribe: Like subscribing to a newsletter. Many people can subscribe, and whenever the newsletter is published, everyone gets a copy automatically. The publisher doesn’t need to know who’s reading.

Event-Driven: Like setting your phone to vibrate when you get a text. You don’t constantly check your phone - it notifies you when something happens.

Model	Real-World Analogy
Request-Response	Asking a librarian for a specific book
Publish-Subscribe	Subscribing to a YouTube channel
Push	Getting a text message notification
Pull	Refreshing your email to check for new messages

NoteKey Takeaway

In one sentence: The choice of communication model fundamentally shapes your IoT system’s latency, scalability, battery life, and resilience - there is no “best” model, only the best fit for your requirements.

Remember this rule: Use request-response for queries and commands needing confirmation, pub-sub for streaming sensor data to multiple consumers, and event-driven for battery-constrained devices that need to sleep between activities.

187.3 Request-Response Pattern

15 min

Intermediate

The request-response pattern is the most intuitive communication model, where a client sends a request and waits for a response from the server. This synchronous interaction ensures the client knows the outcome of each operation.

187.3.1 How Request-Response Works

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sequenceDiagram
    participant C as Client/Device
    participant S as Server/Cloud

    C->>S: Request (GET temperature)
    Note right of S: Process request
    S-->>C: Response (25.5 C)

    C->>S: Request (SET thermostat=22)
    Note right of S: Execute command
    S-->>C: Response (OK, applied)

    C->>S: Request (GET status)
    S-->>C: Response (Error: timeout)
    Note left of C: Handle error

187.3.2 Characteristics

Aspect	Request-Response Behavior
Timing	Synchronous - client blocks until response
Coupling	Tight - client must know server address
Delivery	Confirmed - response indicates success/failure
Scalability	Limited - server handles one request at a time per connection
Use Cases	Configuration, queries, commands requiring confirmation

187.3.3 IoT Applications

Best suited for: - Reading device configuration parameters - Sending commands that require acknowledgment (e.g., “turn off heater”) - Querying current sensor values on demand - REST API interactions with cloud platforms

Protocols using request-response: - HTTP/HTTPS (GET, POST, PUT, DELETE) - CoAP (with CON messages) - Modbus (master-slave polling)

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      question: "A smart thermostat needs to change the setpoint from 20C to 22C and confirm the change was applied. The cloud platform offers both MQTT (pub/sub) and REST API (request-response). Which approach is MORE appropriate for this command?",
      options: [
        {text: "MQTT publish to 'thermostat/setpoint' topic", correct: false, feedback: "MQTT pub/sub is fire-and-forget by default. While QoS levels can ensure delivery, you don't get confirmation that the thermostat actually applied the change - only that the message was received."},
        {text: "REST API POST /thermostat/setpoint with response confirmation", correct: true, feedback: "Correct! Request-response patterns like REST ensure the command was received AND processed. The response can include the new state, confirming the thermostat actually changed to 22C, not just that a message was delivered."},
        {text: "Both are equally appropriate for commands", correct: false, feedback: "They're not equal for commands requiring confirmation. Request-response provides synchronous confirmation of command execution, which is critical for control operations."},
        {text: "Neither - use raw TCP sockets instead", correct: false, feedback: "Raw sockets add complexity without benefit. The choice between request-response and pub/sub patterns matters more than the underlying transport."}
      ],
      difficulty: "medium",
      topic: "communication-models"
    }));
  }
}

187.3.4 Limitations in IoT

While intuitive, request-response has drawbacks for IoT:

1. Polling overhead: To detect changes, clients must repeatedly poll, wasting bandwidth
2. Latency: Real-time updates require frequent polling, increasing load
3. Scalability: Each client connection consumes server resources
4. Battery drain: Constant polling keeps radios active, draining batteries

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      question: "A factory has 500 temperature sensors reporting to a central monitoring system. Using HTTP request-response, each sensor is polled every second. What is the PRIMARY scalability concern?",
      options: [
        {text: "HTTP headers are too large", correct: false, feedback: "While HTTP headers add overhead, the primary concern is connection and processing load, not header size."},
        {text: "Server must handle 500 concurrent connections and 500 requests/second", correct: true, feedback: "Correct! With request-response polling, the server must accept connections from all 500 sensors and process 500 requests per second continuously. This creates massive connection overhead and processing load compared to pub/sub where sensors push only when values change."},
        {text: "TCP three-way handshake is too slow", correct: false, feedback: "Connection setup is a factor, but persistent connections can mitigate this. The core issue is handling 500 simultaneous polling operations every second."},
        {text: "JSON parsing is computationally expensive", correct: false, feedback: "Payload parsing is minimal compared to connection handling. The scalability bottleneck is managing hundreds of concurrent polling clients."}
      ],
      difficulty: "medium",
      topic: "communication-models"
    }));
  }
}

187.4 Publish-Subscribe Pattern

20 min

Intermediate

The publish-subscribe (pub/sub) pattern decouples message producers from consumers through an intermediary broker. Publishers send messages to topics without knowing who will receive them, and subscribers receive messages from topics they’re interested in.

187.4.1 How Pub/Sub Works

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graph TB
    subgraph Publishers
        P1[Temp Sensor]
        P2[Humidity Sensor]
        P3[Motion Sensor]
    end

    subgraph Broker["Message Broker"]
        T1[sensors/temp]
        T2[sensors/humidity]
        T3[sensors/motion]
    end

    subgraph Subscribers
        S1[Dashboard]
        S2[Alert System]
        S3[Data Logger]
    end

    P1 -->|publish| T1
    P2 -->|publish| T2
    P3 -->|publish| T3

    T1 -->|subscribe| S1
    T1 -->|subscribe| S3
    T2 -->|subscribe| S1
    T2 -->|subscribe| S3
    T3 -->|subscribe| S2

    style Broker fill:#E67E22,stroke:#2C3E50,color:#fff
    style Publishers fill:#16A085,stroke:#2C3E50,color:#fff
    style Subscribers fill:#2C3E50,stroke:#16A085,color:#fff

187.4.2 Key Benefits for IoT

Benefit	Explanation
Decoupling	Publishers and subscribers don’t need to know about each other
Scalability	Adding new subscribers doesn’t affect publishers
Real-time	Messages delivered immediately upon publication
Efficiency	No polling - data sent only when values change
Fan-out	One message can reach multiple subscribers

187.4.3 Topic Hierarchies

MQTT and similar protocols support hierarchical topics with wildcards:

sensors/building1/floor2/room203/temperature
sensors/building1/floor2/+/temperature     # + matches single level
sensors/building1/#                         # # matches all remaining levels

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      question: "A smart building has 1,000 sensors publishing temperature data every minute. Three applications need this data: a real-time dashboard, an hourly report generator, and an anomaly detection system. Using pub/sub with MQTT, how many message deliveries occur per minute?",
      options: [
        {text: "1,000 (one per sensor)", correct: false, feedback: "This would be true if there was only one subscriber. With pub/sub, each subscriber receives a copy of each message."},
        {text: "3,000 (each message delivered to all 3 subscribers)", correct: true, feedback: "Correct! In pub/sub, the broker delivers each message to ALL matching subscribers. 1,000 sensors x 3 subscribers = 3,000 message deliveries per minute. This fan-out capability is a key strength of pub/sub."},
        {text: "1,003 (sensors plus subscribers)", correct: false, feedback: "Message count isn't additive. Each sensor message is replicated to each subscriber, resulting in multiplication, not addition."},
        {text: "Depends on network bandwidth", correct: false, feedback: "Bandwidth affects whether messages succeed, but doesn't change the logical delivery count. The broker attempts 3,000 deliveries regardless of network conditions."}
      ],
      difficulty: "easy",
      topic: "communication-models"
    }));
  }
}

187.4.4 Quality of Service (QoS) Levels

MQTT defines three QoS levels that trade reliability for overhead:

QoS	Name	Guarantee	Overhead	Use Case
0	At-most-once	May lose messages	Lowest	Frequent sensor telemetry
1	At-least-once	Delivered, may duplicate	Medium	Commands, alerts
2	Exactly-once	One and only one delivery	Highest	Financial transactions, critical commands

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      question: "An IoT door lock receives 'unlock' commands via MQTT. The system must ensure the command is executed exactly once - not zero times (door stays locked) and not twice (audit log shows duplicate unlock). Which QoS level is required?",
      options: [
        {text: "QoS 0 - fastest delivery for real-time response", correct: false, feedback: "QoS 0 provides no delivery guarantee. The unlock command could be lost in transit, leaving the door locked when access was granted."},
        {text: "QoS 1 - guaranteed delivery is sufficient", correct: false, feedback: "QoS 1 ensures delivery but may duplicate. If the network acks slowly, the command could be retried, causing the lock to log two unlock events from one authorization."},
        {text: "QoS 2 - exactly-once delivery prevents both loss and duplication", correct: true, feedback: "Correct! QoS 2 uses a four-step handshake (PUBLISH, PUBREC, PUBREL, PUBCOMP) to guarantee the message is delivered exactly once. This prevents both missing unlocks and duplicate audit entries."},
        {text: "Any QoS works - the lock is idempotent", correct: false, feedback: "Even if the physical lock action is idempotent, the audit log and billing systems may not be. Exactly-once semantics matter for accountability."}
      ],
      difficulty: "medium",
      topic: "communication-models"
    }));
  }
}

187.5 Push vs Pull Communication

15 min

Foundational

The distinction between push and pull communication determines who initiates data transfer and when data flows through the system.

187.5.1 Push Model

In push communication, the data source initiates transmission when new data is available:

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sequenceDiagram
    participant S as Sensor
    participant C as Cloud

    Note over S: Temperature changes
    S->>C: Push: temp=26.5C

    Note over S: 5 minutes later...
    Note over S: Temperature stable
    Note over S: No transmission needed

    Note over S: Temperature changes
    S->>C: Push: temp=27.1C

Characteristics: - Source decides when to send (event-driven) - Efficient - no data sent when unchanged - Real-time - immediate notification - Source must maintain connection or wake periodically

187.5.2 Pull Model

In pull communication, the consumer requests data when needed:

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sequenceDiagram
    participant C as Consumer/App
    participant S as Sensor/Server

    C->>S: Request: get temperature
    S-->>C: Response: 26.5C

    Note over C: Wait 1 minute

    C->>S: Request: get temperature
    S-->>C: Response: 26.5C (unchanged)

    Note over C: Wait 1 minute

    C->>S: Request: get temperature
    S-->>C: Response: 27.1C

Characteristics: - Consumer decides when to request - May miss events between polls - Polling overhead even when data unchanged - Source can sleep between requests (for battery devices)

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      question: "A wildlife tracking collar runs on a small battery and needs to last 2 years. It should report location whenever the animal moves to a new territory (roughly once per week). Which communication model is MORE battery-efficient?",
      options: [
        {text: "Pull model - server polls collar for location every hour", correct: false, feedback: "Hourly polling means the collar's radio must wake 24 times per day (8,760 per year) even though location only changes ~52 times per year. This wastes 99.4% of radio power on redundant responses."},
        {text: "Push model - collar transmits only when territory change detected", correct: true, feedback: "Correct! Push is far more efficient here. The collar's radio only activates ~52 times per year (on territory changes) versus 8,760+ times with hourly polling. This can extend battery life by 100x for sparse event scenarios."},
        {text: "Both are equal - radio energy is the same per transmission", correct: false, feedback: "Energy per transmission may be similar, but the NUMBER of transmissions differs dramatically. Push: ~52/year. Pull (hourly): 8,760/year."},
        {text: "Pull model - it lets the collar sleep between polls", correct: false, feedback: "The collar must still wake to RESPOND to polls. With push, it truly sleeps until it detects an event worth reporting."}
      ],
      difficulty: "easy",
      topic: "communication-models"
    }));
  }
}

187.5.3 Comparison Table

Factor	Push	Pull
Latency	Low (immediate)	Variable (depends on poll interval)
Bandwidth	Efficient (only when changed)	Wasteful if data rarely changes
Battery	Radio active on source events	Radio wakes on poll schedule
Missed Events	None (if connection maintained)	Possible between polls
Server Load	Varies with event rate	Predictable (poll rate)
Best For	Streaming, alerts, sparse events	On-demand queries, batch processing

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      question: "A fire alarm system monitors 200 smoke detectors. Detectors should report immediately when smoke is detected, but the central station also needs to verify all detectors are online daily. What hybrid approach is BEST?",
      options: [
        {text: "Pure push - detectors push smoke events and daily heartbeats", correct: false, feedback: "This works but doesn't verify connectivity from the station's perspective. If a detector fails silently, the station won't know until it should have received a heartbeat."},
        {text: "Pure pull - station polls all detectors every 5 minutes", correct: false, feedback: "Polling every 5 minutes means up to 5-minute delay for smoke detection - unacceptable for fire safety where seconds matter."},
        {text: "Push for smoke alerts (immediate), pull for daily health checks (verification)", correct: true, feedback: "Correct! Smoke alerts use push for immediate notification (life safety). Daily pull-based health checks let the station actively verify each detector responds - catching silent failures that push-only would miss."},
        {text: "Push for everything with QoS 2 guaranteed delivery", correct: false, feedback: "QoS 2 ensures delivery but doesn't verify detector health. A detector with a failed sensor but working radio would appear fine. Active polling verifies the entire device responds."}
      ],
      difficulty: "hard",
      topic: "communication-models"
    }));
  }
}

187.6 Event-Driven Architecture

20 min

Advanced

Event-driven architecture (EDA) structures systems around the production, detection, and reaction to events. Unlike request-response where the caller waits for completion, event-driven systems decouple producers from consumers through asynchronous event channels.

187.6.1 Event-Driven Components

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graph LR
    subgraph Producers["Event Producers"]
        S1[Motion Sensor]
        S2[Door Contact]
        S3[Temp Sensor]
    end

    subgraph Bus["Event Bus/Stream"]
        E1[MotionDetected]
        E2[DoorOpened]
        E3[TempThreshold]
    end

    subgraph Consumers["Event Consumers"]
        C1[Alert Service]
        C2[Logging Service]
        C3[Automation Engine]
    end

    S1 -->|emit| E1
    S2 -->|emit| E2
    S3 -->|emit| E3

    E1 --> C1
    E1 --> C2
    E2 --> C1
    E2 --> C3
    E3 --> C3

    style Bus fill:#E67E22,stroke:#2C3E50,color:#fff

187.6.2 Event Types in IoT

Event Type	Description	Example
State Change	Value crosses threshold	Temperature exceeds 30C
Condition	Complex multi-sensor state	“Room occupied AND lights off”
Command	Action request	“Activate sprinkler zone 3”
Notification	Informational alert	“Battery below 20%”
Audit	Historical record	“User John unlocked door at 14:32”

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      question: "A smart home automation rule says: 'When motion is detected in the living room AND it's after sunset, turn on the lights.' Three services are involved: motion sensor, time service, and light controller. In event-driven architecture, which component evaluates the AND condition?",
      options: [
        {text: "The motion sensor evaluates both conditions before emitting an event", correct: false, feedback: "Sensors should emit raw events (motion detected), not evaluate business logic. Embedding time checks in sensors creates tight coupling and requires sensor updates for rule changes."},
        {text: "The light controller polls both motion and time services", correct: false, feedback: "Polling is inefficient and not event-driven. The light controller should react to events, not constantly poll for conditions."},
        {text: "A rules engine or event processor subscribes to motion events and checks time, then commands lights", correct: true, feedback: "Correct! In EDA, a dedicated rules/automation engine subscribes to sensor events, evaluates conditions (AND, OR, time-based), and emits command events to actuators. This decouples sensors from actuators and centralizes business logic."},
        {text: "The event bus automatically combines events into compound triggers", correct: false, feedback: "Event buses route messages but don't evaluate business rules. A separate rules engine or complex event processor handles condition evaluation."}
      ],
      difficulty: "hard",
      topic: "communication-models"
    }));
  }
}

187.6.3 Complex Event Processing (CEP)

CEP systems analyze event streams to detect patterns across multiple events:

Pattern Types: - Sequence: Event A followed by Event B within 5 minutes - Aggregation: Average temperature across 10 sensors exceeds threshold - Absence: No heartbeat received for 60 seconds (device offline) - Correlation: Door opened without prior authentication

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      question: "An industrial IoT system monitors a production line. A 'quality issue' should be detected when: (1) temperature exceeds 80C, (2) followed by vibration spike within 30 seconds, (3) but only if conveyor speed was above 2 m/s. This pattern requires:",
      options: [
        {text: "Simple threshold monitoring on each sensor", correct: false, feedback: "Simple thresholds can't correlate events across sensors or detect sequences. Each sensor would alert independently without detecting the combined pattern."},
        {text: "Complex Event Processing with temporal correlation", correct: true, feedback: "Correct! CEP engines can define patterns like 'temp > 80 FOLLOWED BY vibration > threshold WITHIN 30s WHERE speed > 2'. This requires correlating events across time and sensors - exactly what CEP is designed for."},
        {text: "Polling all sensors simultaneously and comparing values", correct: false, feedback: "Polling captures snapshots, not sequences. You'd need continuous polling to catch the 30-second window, and still couldn't reliably detect 'followed by' patterns."},
        {text: "Machine learning to detect anomalies automatically", correct: false, feedback: "ML can help, but this is a deterministic rule-based pattern. CEP provides explicit, auditable detection. ML might complement CEP for unknown patterns."}
      ],
      difficulty: "hard",
      topic: "communication-models"
    }));
  }
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187.7 Synchronous vs Asynchronous Communication

15 min

Intermediate

The distinction between synchronous and asynchronous communication determines whether the sender waits for completion before proceeding.

187.7.1 Synchronous Communication

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sequenceDiagram
    participant D as Device
    participant C as Cloud API

    D->>C: Send reading (blocking)
    Note over D: WAITING...<br/>Cannot do other work
    C-->>D: Response received
    Note over D: Continue execution
    D->>C: Next operation (blocking)
    Note over D: WAITING...
    C-->>D: Response received

Characteristics: - Caller blocks until operation completes - Simple control flow (sequential execution) - Timeout handling required - Latency adds up for multiple operations

187.7.2 Asynchronous Communication

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sequenceDiagram
    participant D as Device
    participant Q as Message Queue
    participant C as Cloud Processor

    D->>Q: Send reading (non-blocking)
    Note over D: Continue immediately
    D->>Q: Send another reading
    Note over D: Reading sensors...
    Q->>C: Deliver when ready
    C->>Q: Acknowledge
    Q->>C: Deliver second message

Characteristics: - Caller continues immediately after sending - Complex control flow (callbacks, promises, event loops) - Built-in buffering handles burst traffic - Decouples sender from receiver timing

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      question: "An ESP32 device reads 3 sensors and sends data to the cloud. Using synchronous HTTP calls with 200ms latency each, total time is 600ms. Using asynchronous MQTT publishes, total time is approximately:",
      options: [
        {text: "600ms - same total data must be transmitted", correct: false, feedback: "Asynchronous doesn't change transmission time, but it changes how the device spends that time. The device doesn't block waiting for each response."},
        {text: "200ms - all three complete when the slowest finishes", correct: false, feedback: "This assumes parallel execution with network as bottleneck. In async, the device returns immediately after queueing - it doesn't wait for network completion."},
        {text: "Near-instant - device queues messages and returns immediately", correct: true, feedback: "Correct! With async MQTT, the device queues all three messages to the local client buffer and returns in microseconds. The 600ms network latency happens in the background while the device continues other work (reading more sensors, sleeping, etc.)."},
        {text: "2ms - one round trip for batch transmission", correct: false, feedback: "Async doesn't batch into one transmission. Each message is sent separately, but the device doesn't wait for acknowledgments before proceeding."}
      ],
      difficulty: "medium",
      topic: "communication-models"
    }));
  }
}

187.7.3 Comparison for IoT

Aspect	Synchronous	Asynchronous
Code Complexity	Simple (linear flow)	Complex (callbacks/async-await)
Error Handling	Immediate (exception at call site)	Delayed (callback or later check)
Resource Usage	Thread blocked during wait	Thread free during wait
Latency Sensitivity	High impact (blocking time)	Low impact (background processing)
Reliability	Fail immediately if unavailable	Queue for retry when available
Debugging	Easy stack traces	Harder async trace correlation

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      question: "A remote agriculture sensor loses cellular connectivity for 2 hours during a storm. It must continue collecting soil moisture readings. Synchronous cloud API calls would fail during this outage. How does asynchronous messaging handle this scenario?",
      options: [
        {text: "Readings are lost - async doesn't help with offline scenarios", correct: false, feedback: "Async messaging with local queuing specifically addresses offline scenarios. Messages are stored locally and transmitted when connectivity returns."},
        {text: "Device queues readings locally and transmits backlog when connectivity returns", correct: true, feedback: "Correct! Asynchronous messaging with store-and-forward capability queues messages during outages. When connectivity resumes, the queued readings are transmitted. This is why async patterns with local persistence are essential for unreliable networks."},
        {text: "Device retries synchronously until the storm ends", correct: false, feedback: "Continuous retry attempts would drain the battery and still lose readings if the retry queue is in-memory only. Async with persistent queuing is more robust."},
        {text: "Cloud platform requests missed readings after reconnection", correct: false, feedback: "The cloud doesn't know what readings were missed. The device must buffer and push when able. This requires device-side async queuing, not cloud-side pull."}
      ],
      difficulty: "medium",
      topic: "communication-models"
    }));
  }
}

187.8 Message Patterns for IoT

20 min

Advanced

Different IoT scenarios require different message patterns. Understanding these patterns helps design efficient, scalable communication.

187.8.1 One-to-One (Point-to-Point)

Direct communication between two endpoints:

Sensor --> Gateway
Device --> Cloud API
Controller --> Actuator

Use cases: Direct commands, configuration updates, device-to-gateway links

187.8.2 One-to-Many (Broadcast/Multicast)

Single message delivered to multiple recipients:

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graph TD
    CMD[Firmware Update Command] --> D1[Device 1]
    CMD --> D2[Device 2]
    CMD --> D3[Device 3]
    CMD --> D4[Device 4]
    CMD --> D5[Device ...]

    style CMD fill:#E67E22,stroke:#2C3E50,color:#fff

Use cases: Firmware updates, configuration broadcasts, time synchronization

187.8.3 Many-to-One (Aggregation)

Multiple sources sending to single destination:

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graph TD
    S1[Sensor 1] --> GW[Gateway/Aggregator]
    S2[Sensor 2] --> GW
    S3[Sensor 3] --> GW
    S4[Sensor N] --> GW
    GW --> Cloud[Cloud Platform]

    style GW fill:#E67E22,stroke:#2C3E50,color:#fff

Use cases: Sensor data collection, log aggregation, fleet telemetry

{
  const container = document.getElementById('kc-comm-11');
  if (container && typeof InlineKnowledgeCheck !== 'undefined') {
    container.innerHTML = '';
    container.appendChild(InlineKnowledgeCheck.create({
      question: "A building management system needs to send an emergency evacuation alert to 500 display panels simultaneously. Each panel runs independently and may be temporarily offline. Which message pattern and mechanism ensures ALL panels eventually receive the alert?",
      options: [
        {text: "One-to-one messages sent sequentially to each panel", correct: false, feedback: "Sequential delivery is slow (500 individual transmissions) and doesn't help offline panels. By the time you reach panel 500, the emergency may be over."},
        {text: "Broadcast with MQTT retained message on 'emergency/evacuation' topic", correct: true, feedback: "Correct! Broadcasting via pub/sub reaches all subscribed panels simultaneously. The retained message flag ensures panels that were offline receive the alert immediately upon reconnecting - critical for safety systems where every device must eventually get the message."},
        {text: "Multicast UDP for fastest delivery to all panels", correct: false, feedback: "UDP multicast is fast but unreliable and doesn't help offline panels. There's no retry or store-and-forward. Panels that miss the packet never receive the alert."},
        {text: "Request-response where each panel polls for alerts every second", correct: false, feedback: "Polling every second works but wastes bandwidth during normal operation and may still miss panels that are offline. Retained messages are more efficient and reliable for infrequent critical alerts."}
      ],
      difficulty: "hard",
      topic: "communication-models"
    }));
  }
}

187.8.4 Request-Reply Pattern

For commands requiring responses, implement request-reply over pub/sub:

Device publishes: commands/device123/request  {id: "req-456", action: "reboot"}
Server publishes: commands/device123/response {id: "req-456", status: "accepted"}

187.8.5 Event Sourcing Pattern

Store all state changes as a sequence of events:

[Event 1] ThermostatCreated {id: "t1", setpoint: 20}
[Event 2] SetpointChanged {id: "t1", old: 20, new: 22}
[Event 3] ModeChanged {id: "t1", mode: "cooling"}
[Event 4] SetpointChanged {id: "t1", old: 22, new: 21}

Benefits: Complete audit trail, replay capability, debugging, time-travel queries

{
  const container = document.getElementById('kc-comm-12');
  if (container && typeof InlineKnowledgeCheck !== 'undefined') {
    container.innerHTML = '';
    container.appendChild(InlineKnowledgeCheck.create({
      question: "A fleet of 10,000 delivery trucks reports GPS location every 30 seconds. The backend must process real-time locations AND allow historical route replay for any truck. Which combination of patterns supports both requirements efficiently?",
      options: [
        {text: "Request-response API for both real-time and historical queries", correct: false, feedback: "Request-response works for historical queries but is inefficient for real-time streaming from 10,000 trucks (polling overhead). It also doesn't naturally support stream processing."},
        {text: "Pub/sub for real-time streaming plus event sourcing for historical replay", correct: true, feedback: "Correct! Pub/sub efficiently streams 10,000 GPS updates in real-time to dashboards and processing systems. Event sourcing stores every location event in an append-only log, enabling historical replay by reading events for any truck from any point in time."},
        {text: "Store latest position only - historical data isn't needed for delivery", correct: false, feedback: "Historical routes are valuable for driver performance analysis, dispute resolution, ETA prediction training, and compliance. Discarding history loses significant business value."},
        {text: "Batch upload every hour to reduce message volume", correct: false, feedback: "Hourly batches lose real-time visibility. You can't track a truck's current position or detect route deviations in real-time with hour-old data."}
      ],
      difficulty: "hard",
      topic: "communication-models"
    }));
  }
}

187.9 Communication Model Selection Guide

10 min

Intermediate

Use this decision framework to select appropriate communication models:

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flowchart TD
    START[Select Communication Model] --> Q1{Need immediate<br/>confirmation?}

    Q1 -->|Yes| Q2{Multiple consumers?}
    Q1 -->|No| Q3{Data streaming<br/>or events?}

    Q2 -->|No| RR[Request-Response<br/>HTTP/CoAP]
    Q2 -->|Yes| HYBRID[Pub-Sub + Request<br/>for critical ops]

    Q3 -->|Yes| Q4{Need historical<br/>replay?}
    Q3 -->|No| POLL[Poll-based<br/>Request-Response]

    Q4 -->|Yes| ES[Event Sourcing<br/>Kafka/EventStore]
    Q4 -->|No| PS[Publish-Subscribe<br/>MQTT/AMQP]

    style START fill:#2C3E50,stroke:#16A085,color:#fff
    style RR fill:#16A085,stroke:#2C3E50,color:#fff
    style PS fill:#16A085,stroke:#2C3E50,color:#fff
    style ES fill:#E67E22,stroke:#2C3E50,color:#fff
    style HYBRID fill:#E67E22,stroke:#2C3E50,color:#fff

187.9.1 Quick Reference Table

Scenario	Recommended Model	Protocol Examples
Device configuration	Request-Response	HTTP, CoAP
Sensor telemetry streaming	Pub-Sub (async)	MQTT, AMQP
Commands with confirmation	Request-Response	HTTP, CoAP CON
Alerts to multiple systems	Pub-Sub broadcast	MQTT retained
Battery-constrained events	Push (event-driven)	MQTT QoS 0/1
Audit trail requirements	Event Sourcing	Kafka, EventStore
Unreliable networks	Async with queue	MQTT + local buffer
Real-time dashboards	Pub-Sub + WebSocket	MQTT over WS

187.10 Summary and Key Takeaways

187.10.1 Core Concepts

1. Request-Response: Synchronous, confirmed delivery, good for commands and queries
2. Publish-Subscribe: Decoupled, scalable, efficient for streaming and multi-consumer scenarios
3. Push vs Pull: Push for efficiency and real-time; pull for control and on-demand access
4. Event-Driven: Reactive, loosely coupled, enables complex event processing
5. Sync vs Async: Sync for simplicity; async for resilience and efficiency

187.10.2 Design Principles

• Match communication model to data flow requirements
• Use async messaging for unreliable networks
• Implement retained messages for critical state
• Consider battery impact of polling vs push
• Design for eventual consistency in distributed systems

187.11 What’s Next

After understanding communication models, explore:

• MQTT Fundamentals: Deep dive into the most popular IoT pub-sub protocol
• CoAP Protocol: Request-response optimized for constrained devices
• Stream Processing: Message queuing and real-time data processing for IoT
• Communication and Protocol Bridging: Translating between different protocols at gateways