This article provides a complete, practical walkthrough of the Http Mqtt Monitor Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Building a Remote IoT Sensor Monitoring Workflow with n8n, MQTT, and InfluxDB
   
   Meta Description:  
   Discover how to create a DIY IoT sensor monitoring system using n8n, MQTT, and InfluxDB. Learn how temperature and humidity data from a remote ESP32 sensor can be processed and visualized in real-time.
   
   Keywords:  
   IoT monitoring workflow, n8n workflow example, MQTT trigger n8n, InfluxDB integration, ESP32 weather sensor, remote sensing, DHT22 sensor data, InfluxDB IoT, Mosquitto MQTT broker, JSON data parsing
   
   Article:
   
   Remote IoT Sensor Monitoring via n8n, MQTT, and InfluxDB
   
   As the Internet of Things (IoT) continues to grow, developers and enthusiasts are increasingly looking for efficient ways to collect and analyze sensor data from remote devices. This article walks you through a practical implementation of a remote weather sensor monitoring system using an ESP32 microcontroller, MQTT messaging protocol, and data ingestion into an InfluxDB time-series database — all orchestrated using the low-code automation platform n8n.
   
   Overview of the Workflow
   
   This low-code automation is built on n8n and demonstrates how sensor data — specifically temperature and humidity from a DHT22 sensor attached to an ESP32 — can be captured via MQTT, processed with JavaScript logic for validation and formatting, and ultimately stored in InfluxDB for visualization and analytics.
   
   Here’s a step-by-step breakdown of the workflow and its key components:
   
   1. MQTT Trigger for Remote Sensor Data
   
   The workflow begins with an MQTT trigger node in n8n that subscribes to a topic named wokwi-weather. This topic is published by a remote ESP32 device connected to a DHT22 sensor. The microcontroller is programmed to send real-time JSON-encoded data (e.g., temperature and humidity values) to a Mosquitto MQTT broker.
   
   Once a message is received on this topic, it activates the workflow, forwarding the payload to the next node for processing.
   
   2. JavaScript Code for Payload Parsing
   
   A Code node is used to ensure the incoming MQTT message is valid JSON and to extract the relevant sensor values. The code handles error checking and ensures the presence and correct data types for temp and humidity.
   
   Here’s a simplified version of the transformation logic:
   
   - Parse the JSON string in the MQTT payload.
   - Extract values for temperature and humidity.
   - Format them into InfluxDB’s expected "line protocol" format, such as:
     
     wokwi-weather humidity=45,temp=22
   
   The formatted payload is then passed to the InfluxDB HTTP request node.
   
   3. HTTP Request for InfluxDB Data Ingestion
   
   The final node in the workflow is an HTTP request node configured to send a POST request to an InfluxDB instance running locally on http://localhost:8086. The node uses line protocol to format the incoming values and includes an authorization token to authenticate the request.
   
   The query parameters include:
   
   - orgID: Identifier for the InfluxDB organization.
   - bucket: InfluxDB bucket name where data is stored.
   - precision: Set to seconds to match the timestamp granularity.
   
   Once data is stored in InfluxDB, tools like Grafana or InfluxDB’s native UI can be used to visualize trends, monitor sensor readings over time, or set up alerts.
   
   Why Use n8n for IoT?
   
   n8n is a powerful open-source workflow automation platform that excels at integrating disparate systems and automating data flow between them. For IoT applications, this is particularly valuable because:
   
   - You can visually create workflows without writing boilerplate code.
   - It integrates well with MQTT brokers and HTTP APIs.
   - You can easily process or enrich incoming data using JavaScript.
   - It supports hundreds of services and can be extended via custom nodes or HTTP interfaces.
   
   Benefits and Applications
   
   The ability to collect, parse, and store sensor data in a structured and scalable way opens up numerous applications, such as:
   
   - Smart home weather stations
   - Industrial monitoring systems
   - Agricultural environment tracking
   - Energy usage analytics
   
   This n8n blueprint provides a foundation that can be enhanced by adding error handling, backup logging, alert notifications, or even AI-based predictions based on collected data.
   
   Third-Party APIs and Services Used:
   
   1. Mosquitto MQTT Broker – MQTT messaging service that the ESP32 uses to publish sensor data.
   2. InfluxDB – Time-series database for storing and querying sensor data.
   3. n8n – Automation platform used to create and orchestrate the workflow.
   
   Final Thoughts
   
   This example shows that you don’t need a massive infrastructure or advanced coding skills to build a reliable IoT monitoring system. With n8n's visual automation tools, MQTT's lightweight communication protocol, and InfluxDB’s powerful data storage capabilities, you can build scalable and customizable IoT workflows in a matter of minutes.
   
   Whether you're building a home automation project or a commercial-grade sensor network, this integration stack provides a solid, open-source foundation ready for expansion.
   
   Start simple, iterate fast, and let your sensors do the talking — the data is just a flow away.
   
   —
   
   If you're interested in trying this workflow yourself, be sure to secure your MQTT server, update the API tokens in the HTTP request node, and configure your InfluxDB instance correctly.
   
   Ready to dive deeper? Explore n8n’s documentation or build your own custom nodes for further extensibility!

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.