IoT Domain Expert

IoT Domain Expert

Enterprise IoT Architecture: From Sensors to Cloud

Focus: MQTT, Edge Computing, Industrial IoT, Security
Stack: Python, Mosquitto, Docker, TensorFlow Lite

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Overview

Production-ready patterns for scalable, secure IoT systems.

Core Architecture

• Device Layer: Sensors/​Actuators (ESP32, Arduino)
• Edge Layer: Local processing & buffering (Raspberry Pi, Jetson)
• Network Layer: MQTT, CoAP, LoRaWAN, NB-IoT
• Cloud Layer: Analytics, Storage, Device Management

Protocol Selection

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Implementation Patterns

1. Robust MQTT Client (Python)

Production-grade client with auto-reconnection and buffering.

import json
import logging
import paho.mqtt.client as mqtt
from datetime import datetime

class IoTClient:
    def __init__(self, client_id, broker, port=1883, username=None, password=None):
        self.client = mqtt.Client(client_id=client_id, protocol=mqtt.MQTTv5)
        if username:
            self.client.username_pw_set(username, password)

        self.client.on_connect = self.on_connect
        self.client.on_message = self.on_message
        self.client.on_disconnect = self.on_disconnect

        self.connected = False
        self.topics = []

    def connect(self):
        try:
            self.client.connect(self.broker, self.port, keepalive=60)
            self.client.loop_start()
        except Exception as e:
            logging.error(f"Connection failed: {e}")

    def on_connect(self, client, userdata, flags, rc, properties=None):
        if rc == 0:
            self.connected = True
            logging.info("Connected to broker")
            for topic, qos in self.topics:
                client.subscribe(topic, qos)
        else:
            logging.error(f"Connection failed with code {rc}")

    def publish_telemetry(self, device_id, data):
        if not self.connected:
            return False

        payload = {
            "device_id": device_id,
            "ts": datetime.utcnow().isoformat(),
            "data": data
        }

        info = self.client.publish(
            f"devices/​{device_id}/​telemetry",
            json.dumps(payload),
            qos=1
        )
        return info.rc == mqtt.MQTT_ERR_SUCCESS

    def subscribe_command(self, device_id, callback):
        topic = f"devices/​{device_id}/​commands/#"
        self.topics.append((topic, 1))
        self.client.message_callback_add(topic, callback)
        if self.connected:
            self.client.subscribe(topic, 1)

2. Edge Data Processing

Local buffering and aggregation before cloud upload.

import asyncio
from collections import deque

class EdgeProcessor:
    def __init__(self, buffer_size=100):
        self.buffer = deque(maxlen=buffer_size)
        self.cloud_client = IoTClient("edge-gateway", "cloud.broker.com")

    async def process_reading(self, sensor_id, value):
        # 1. Local Anomaly Detection
        if value > 100:  # Simple threshold
            await self.trigger_local_alert(sensor_id, value)

        # 2. Buffer for Aggregation
        self.buffer.append((sensor_id, value))

        # 3. Batch Upload
        if len(self.buffer) >= 50:
            await self.flush_to_cloud()

    async def flush_to_cloud(self):
        batch = list(self.buffer)
        self.buffer.clear()

        # Calculate aggregates
        avg_val = sum(v for _, v in batch) / len(batch)

        payload = {
            "count": len(batch),
            "avg": avg_val,
            "raw": batch
        }

        self.cloud_client.publish_telemetry("edge-01", payload)

3. Edge ML Inference (TFLite)

Running lightweight models on edge devices.

import tensorflow as tf
import numpy as np

class EdgeModel:
    def __init__(self, model_path):
        self.interpreter = tf.lite.Interpreter(model_path=model_path)
        self.interpreter.allocate_tensors()

        self.input_details = self.interpreter.get_input_details()
        self.output_details = self.interpreter.get_output_details()

    def predict(self, input_data):
        # Preprocess
        input_data = np.array(input_data, dtype=np.float32)
        input_data = np.expand_dims(input_data, 0)

        # Set input
        self.interpreter.set_tensor(self.input_details[0]['index'], input_data)

        # Run inference
        self.interpreter.invoke()

        # Get output
        output = self.interpreter.get_tensor(self.output_details[0]['index'])
        return output[0]

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Infrastructure & Deployment

Docker Compose (Edge Gateway)

version: "3.8"
services:
  mosquitto:
    image: eclipse-mosquitto:2.0
    ports:
      - "1883:1883"
      - "8883:8883"
    volumes:
      - ./​mosquitto/​config:/​mosquitto/​config
      - ./​mosquitto/​data:/​mosquitto/​data

  edge-service:
    build: .
    restart: always
    depends_on:
      - mosquitto
    environment:
      - MQTT_BROKER=mosquitto
      - DEVICE_ID=gateway-01
    devices:
      - "/​dev/​gpiomem:/​dev/​gpiomem" # GPIO access

  influxdb:
    image: influxdb:2.7
    ports:
      - "8086:8086"
    volumes:
      - influxdb-data:/​var/​lib/​influxdb2

Mosquitto Configuration

# mosquitto.conf
listener 1883
protocol mqtt

listener 8883
protocol mqtt
cafile /​mosquitto/​config/​certs/​ca.crt
certfile /​mosquitto/​config/​certs/​broker.crt
keyfile /​mosquitto/​config/​certs/​broker.key

persistence true
persistence_location /​mosquitto/​data/

# Security
allow_anonymous false
password_file /​mosquitto/​config/​passwd
acl_file /​mosquitto/​config/​acl

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Security Best Practices

1. Transport Security: Always use TLS (MQTTS) for cloud communication.
2. Authentication: Use X.509 client certificates or strong unique credentials per device.
3. Authorization: Implement strict ACLs (e.g., sensors can only write to their own topics).
4. Firmware Updates: Secure OTA (Over-The-Air) updates with code signing.

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Validation Checklist

Connectivity:

• MQTT broker reachable
• TLS handshake successful
• Auto-reconnection working

Data Flow:

• Telemetry publishing (QoS 1)
• Command subscription working
• Edge buffering functioning

Security:

• Credentials not hardcoded
• ACLs enforced
• Data encrypted at rest/​transit

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Related Skills

• moai-domain-ml: Edge ML models
• moai-security-devsecops: Secure deployment
• moai-cloud-aws-advanced: AWS IoT Core integration

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Last Updated: 2025-11-20