Real-Time IoT Monitoring with ESP32 and Grafana Dashboards

Real-time data visualization is critical for monitoring IoT deployments, debugging systems, and making data-driven decisions. Grafana, an open-source analytics platform, excels at turning raw ESP32 sensor data into actionable insights through customizable dashboards. This guide combines hardware setup, data pipelines, visualization techniques, and optimization strategiesCost Analysis: Total Ownership for ESP32 Connectivity SolutionsUnlock cost savings with ESP32 IoT solutions. This guide reveals how to balance hardware, connectivity, power, and maintenance costs to master TCO. to help you build scalable, secure, and intuitive monitoring solutions.

Table of Contents🔗

Prerequisites: ESP32, Data Pipeline, and Grafana Setup🔗

Hardware/Software Requirements:

ESP32Setting Up ESP32 as a Wi-Fi Access PointMaster ESP32 AP configuration with our step-by-step guide. Set up a secure, local IoT network using practical code examples and optimization tips. with sensor (e.g., BME280 for temperature/humidity)
Mosquitto MQTT brokerConnecting ESP32 to Cloud Services via Wi-FiDiscover how to connect your ESP32 to AWS, Azure, and Google Cloud using secure Wi-Fi. This guide covers setup, error handling, and low power strategies. (local or cloud-based)
InfluxDB v2.4+ (time-series database)
Grafana v9.0+ (hosted or local instance)

Libraries:

ESP32Setting Up ESP32 as a Wi-Fi Access PointMaster ESP32 AP configuration with our step-by-step guide. Set up a secure, local IoT network using practical code examples and optimization tips.: PubSubClient for MQTTConnecting ESP32 to Cloud Services via Wi-FiDiscover how to connect your ESP32 to AWS, Azure, and Google Cloud using secure Wi-Fi. This guide covers setup, error handling, and low power strategies., ArduinoJson for payloads
InfluxDB: influxdb-client-cpp for C++ integration

Data Flow Architecture: ESP32 → MQTT → InfluxDB → Grafana🔗

ESP32 Sensors → (MQTT over Wi-Fi) → Mosquitto Broker → (Telegraf) → InfluxDB → (Grafana Data Source) → Dashboard

Key Considerations:

Latency: Use MQTTConnecting ESP32 to Cloud Services via Wi-FiDiscover how to connect your ESP32 to AWS, Azure, and Google Cloud using secure Wi-Fi. This guide covers setup, error handling, and low power strategies. QoS 1 to balance speed and reliability.
Scalability: Implement InfluxDB retention policies to manage storage.
Security: Enable TLS encryptionConnecting ESP32 to Cloud Services via Wi-FiDiscover how to connect your ESP32 to AWS, Azure, and Google Cloud using secure Wi-Fi. This guide covers setup, error handling, and low power strategies. for MQTT and token-based authentication for InfluxDB.

Sending Sensor Data from ESP32 via MQTT with TLS🔗

#include <WiFi.h>
#include <PubSubClient.h>
#include <ArduinoJson.h>
const char* ssid = "YOUR_SSID";
const char* password = "YOUR_PASS";
const char* mqtt_server = "mqtt.example.com";
const int mqtt_port = 8883;
WiFiClientSecure espClient;
PubSubClient client(espClient);
void sendData() {
  StaticJsonDocument<200> doc;
  doc["temp"] = readTemperature(); // Custom sensor function
  doc["humidity"] = readHumidity();
  char payload[200];
  serializeJson(doc, payload);
  client.publish("esp32/sensors", payload);
}
void setup() {
  espClient.setCACert(root_ca); // Add CA certificate for TLS
  client.setServer(mqtt_server, mqtt_port);
  // Connect to Wi-Fi and MQTT...
}

Best PracticesZigbee Green Power: Ultra-Low-Power Energy Harvesting SolutionsDiscover how ZGP enables battery-free IoT devices through energy harvesting with ESP32 integrations, supporting smart home and industrial applications.:

Use dynamic topic naming (e.g., esp32Setting Up ESP32 as a Wi-Fi Access PointMaster ESP32 AP configuration with our step-by-step guide. Set up a secure, local IoT network using practical code examples and optimization tips./{device_id}/sensors).
Enable TLS with X.509 certificatesAWS IoT Core with ESP32: X.509 Certificates and Shadow UpdatesLearn to securely connect ESP32 to AWS IoT Core using X.509 certificates and device shadows, with step-by-step instructions and best practices. or pre-shared keys.

Storing ESP32 Data in InfluxDB🔗

1. Create a Bucket:

influx bucket create -n esp32_data --retention 30d

2. Configure Telegraf (MQTTConnecting ESP32 to Cloud Services via Wi-FiDiscover how to connect your ESP32 to AWS, Azure, and Google Cloud using secure Wi-Fi. This guide covers setup, error handling, and low power strategies.-to-InfluxDB bridge):

[[inputs.mqtt_consumer]]
  servers = ["tcp://mqtt.example.com:8883"]
  topics = ["esp32/#"]
  data_format = "json"
  tls_ca = "/etc/telegraf/ca.pem"
[[outputs.influxdb_v2]]
  urls = ["http://localhost:8086"]
  token = "$INFLUX_TOKEN"
  organization = "iot_team"
  bucket = "esp32_data"

Configuring Grafana for Real-Time Visualization🔗

1. Add InfluxDB Data Source:

URL: httpUsing Quectel BC66/BG96 Modules with ESP32 for NB-IoT ConnectivityExplore our detailed tutorial on integrating Quectel BC66/BG96 with ESP32 for low-power, reliable NB-IoT connectivity. Learn hardware setup and AT commands.://localhost:8086
Authentication: Use a token with read access to esp32_data.

2. Enable Alerting: Configure notification channels (e.g., Slack, email).

Building a Grafana Dashboard: Panels, Queries, and Templating🔗

1. Time-Series Panel Example:

from(bucket: "esp32_data")
  |> range(start: -15m)
  |> filter(fn: (r) => r["_measurement"] == "sensors")
  |> filter(fn: (r) => r["_field"] == "temp")
  |> aggregateWindow(every: 10s, fn: mean)

2. Templating for Dynamic Dashboards:

Create a variable $device using SHOW TAG VALUES WITH KEY = "device_id".

Advanced Visualizations: Heatmaps, Histograms, and Annotations🔗

Heatmap:

from(bucket: "esp32_data")
  |> range(start: -1h)
  |> histogram(bins: [0, 25, 30, 35]) // Temperature bins

Annotations: Trigger events (e.g., maintenance logs) via InfluxDB alerts.

Setting Up Alerts for Threshold Crossings🔗

1. Create Alert Rule:

Condition: WHEN last() OF query(A, 1m, now()) > 35

2. Notifications: Send alerts to Slack/email when thresholds are breached.

Optimizing Grafana for High-Data-Rate Deployments🔗

Downsampling: Use InfluxDB tasks to reduce data granularity:

option task = {name: "downsample_1h", every: 1h}
from(bucket: "esp32_data")
  |> aggregateWindow(every: 1h, fn: mean)
  |> to(bucket: "esp32_downsampled")