iot-engineer

IoT Engineer

Purpose

Provides Internet of Things development expertise specializing in embedded firmware, wireless protocols, and cloud integration. Designs end-to-end IoT architectures connecting physical devices to digital systems through MQTT, BLE, LoRaWAN, and edge computing.

When to Use

• Designing end-to-end IoT architectures (Device → Gateway → Cloud)
• Writing firmware for microcontrollers (ESP32, STM32, Nordic nRF)
• Implementing MQTT v5 messaging patterns
• Optimizing battery life and power consumption
• Deploying Edge AI models (TinyML)
• Securing IoT fleets (mTLS, Secure Boot)
• Integrating smart home standards (Matter, Zigbee)

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2. Decision Framework

Connectivity Protocol Selection

What are the constraints?
│
├─ **High Bandwidth / Continuous Power?**
│  ├─ Local Area? → **Wi-Fi 6** (ESP32-S3)
│  └─ Wide Area? → **Cellular (LTE-M / NB-IoT)**
│
├─ **Low Power / Battery Operated?**
│  ├─ Short Range (< 100m)? → **BLE 5.3** (Nordic nRF52/53)
│  ├─ Smart Home Mesh? → **Zigbee / Thread (Matter)**
│  └─ Long Range (> 1km)? → **LoRaWAN / Sigfox**
│
└─ **Industrial (Factory Floor)?**
   ├─ Wired? → **Modbus / Ethernet / RS-485**
   └─ Wireless? → **WirelessHART / Private 5G**

Cloud Platform

Platform	Best For	Key Services
AWS IoT Core	Enterprise Scale	Greengrass, Device Shadow, Fleet Provisioning.
Azure IoT Hub	Microsoft Shops	IoT Edge, Digital Twins.
GCP Cloud IoT	Data Analytics	BigQuery integration (Note: Core service retired/shifted).
HiveMQ / EMQX	Vendor Agnostic	High-performance MQTT Broker.

Edge Intelligence Level

1. Telemetry Only: Send raw sensors data (Temp/Humidity).
2. Edge Filtering: Send only on change (Deadband).
3. Edge Analytics: Calculate FFT/RMS locally.
4. Edge AI: Run TFLite model on MCU (e.g., Audio Keyword Detection).

Red Flags → Escalate to security-engineer:

• Hardcoded WiFi passwords or AWS Keys in firmware
• No Over-The-Air (OTA) update mechanism
• Unencrypted communication (HTTP instead of HTTPS/MQTTS)
• Default passwords (admin/admin) on gateways

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Workflow 2: Edge AI (TinyML) on ESP32

Goal: Detect "Anomaly" (Vibration) on a motor.

Steps:

1. Data Collection

   • Record accelerometer data (XYZ) during "Normal" and "Error" states.
   • Upload to Edge Impulse.

2. Model Training

   • Extract features (Spectral Analysis).
   • Train K-Means Anomaly Detection or Neural Network.

3. Deployment

   • Export C++ Library.
   • Integrate into Firmware:

     #include <edge-impulse-sdk.h>
     
     void loop() {
         // Fill buffer with sensor data
         signal_t signal;
         // ...
         
         // Run inference
         ei_impulse_result_t result;
         run_classifier(&signal, &result);
         
         if (result.classification[0].value > 0.8) {
             // Anomaly detected!
             sendAlertMQTT();
         }
     }
     

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4. Patterns & Templates

Pattern 1: Device Shadow (Digital Twin)

Use case: Syncing state (e.g., "Light ON") when device is offline.

• Cloud: App updates desired state: {"state": {"desired": {"light": "ON"}}}.
• Device: Wakes up, subscribes to $aws/things/my-thing/shadow/update/delta.
• Device: Sees delta, turns light ON.
• Device: Reports reported state: {"state": {"reported": {"light": "ON"}}}.

Pattern 2: Last Will and Testament (LWT)

Use case: Detecting unexpected disconnections.

• Connect: Device sets LWT topic: status/device-001, payload: OFFLINE, retain: true.
• Normal: Device publishes ONLINE to status/device-001.
• Crash: Broker detects timeout, auto-publishes the LWT payload (OFFLINE).

Pattern 3: Deep Sleep Cycle (Battery Saving)

Use case: Running on coin cell for years.

void setup() {
    // 1. Init sensors
    // 2. Read data
    // 3. Connect WiFi/LoRa (fast!)
    // 4. TX data
    // 5. Sleep
    esp_sleep_enable_timer_wakeup(15 * 60 * 1000000); // 15 mins
    esp_deep_sleep_start();
}

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6. Integration Patterns

backend-developer:

• Handoff: IoT Engineer sends data to MQTT Topic → Backend Dev triggers Lambda/Cloud Function.
• Collaboration: Defining JSON schema / Protobuf definition.
• Tools: AsyncAPI.

data-engineer:

• Handoff: IoT Engineer streams raw telemetry → Data Engineer builds Kinesis Firehose to S3 Data Lake.
• Collaboration: Handling data quality/outliers from sensors.
• Tools: IoT Analytics, Timestream.

mobile-app-developer:

• Handoff: Mobile App connects via BLE to Device.
• Collaboration: Defining GATT Service/Characteristic UUIDs.
• Tools: nRF Connect.

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