Hardware Guide

ESP32 for Anomaly Detection with Edge Impulse

Espressif's ESP32 excels at anomaly detection via Edge Impulse. The 2-core xtensa-lx6 at 240 MHz with 520 KB SRAM handles 15 KB quantized models with 16.3x RAM headroom. Built-in Wi-Fi 802.11 b/g/n enables wireless result reporting.

Compatibility: Excellent

Memory-wise, the ESP32 offers 520 KB SRAM, which provides 16.3x the 32 KB minimum for anomaly detection. This generous headroom means the 15 KB model tensor arena, sensor input buffers, and application logic (vibration/current/temperature polling, Wi-Fi 802.11 b/g/n stack, state management) all fit without contention. An additional 4 MB PSRAM is available for larger buffers or data logging. The ESP32 provides 16 MB of flash memory, which comfortably houses the Edge Impulse runtime, the 15 KB model binary, application firmware, and OTA update partitions for field upgrades. Flash usage is well within budget for this configuration. The ESP32's dual-core Xtensa LX6 allows dedicating one core to inference while the other handles Wi-Fi/BLE communication and application logic. The ULP co-processor can handle simple sensor reads during deep sleep, reducing average power consumption in duty-cycled deployments. For anomaly detection, connect a vibration sensor (e.g., ADXL345 accelerometer via I2C/SPI) via SPI and a current sensor (e.g., ACS712 via ADC) via ADC and a temperature sensor (e.g., DS18B20 or TMP36 via ADC) via ADC to the ESP32. Sample at 50-200 Hz and collect windows of 64-256 samples as model input. Compute FFT or statistical features in firmware before inference. Edge Impulse provides an end-to-end workflow: data collection from the ESP32 via serial or WiFi, cloud-based training with auto-quantization, and deployment via C++ library export or Arduino library. The platform estimates on-device RAM and flash usage before deployment, reducing trial-and-error. Wi-Fi-connected boards can use the Edge Impulse daemon for direct data ingestion. At $2-5 per chip ($5-15 for dev boards), the ESP32 offers strong value for anomaly detection deployments. With 136 PlatformIO-listed boards, hardware availability is excellent. Key ESP32 features for this workload: Hardware crypto acceleration, Ultra-low-power co-processor (ULP).

Alternatives

FAQ

How does ESP32 report anomaly detection results wirelessly?

The ESP32's Wi-Fi transmits inference results via MQTT (lightweight, pub/sub), HTTP REST (simple integration), or WebSocket (real-time streaming). Send only classification results and confidence scores — not raw sensor data — to minimize bandwidth. The Wi-Fi stack requires a significant portion of RAM — consult the ESP-IDF documentation for exact memory requirements and account for this in your budget alongside the 15 KB model. ESP-IDF's esp_mqtt and esp_http_client libraries handle reconnection and TLS automatically.

What vibration sampling rate does ESP32 support for anomaly detection?

The ESP32 can sample accelerometers at 10+ kHz via SPI (faster) or ADC. For anomaly detection, 50-200 Hz is typically sufficient. Collect windows of 64-256 samples for gesture/motion classification. Use a software FFT library (e.g., CMSIS-DSP arm_rfft_q15) for frequency-domain features.

What is the power consumption for anomaly detection on ESP32?

Power consumption during inference depends on clock configuration, active peripherals, and duty cycle. Consult the ESP32 datasheet for detailed power profiles at 240 MHz. Wi-Fi transmission significantly increases peak current — transmit inference results only, not raw data. For battery-powered anomaly detection, use duty cycling: run inference at intervals and enter deep sleep between cycles. Profile your specific workload to estimate battery life accurately.