IoT & Embedded Systems (Rust & Edge AI)

• Rust memory ownership, borrowing, and no_std bare-metal fundamentals
• ESP32 hardware architecture, GPIO, UART, SPI, I2C driver writing
• FreeRTOS task scheduling, queues, mutexes, and interrupt handlers (ISR)

• Data collection, feature extraction (FFT spectral analysis), and model training
• Quantizing models to INT8 using TensorFlow Lite for Microcontrollers (TFLM)
• Deploying anomaly detection on ESP32-S3 vector instructions

• CAN Bus 2.0B / CAN-FD physical layer, transceivers, and DBC file parsing
• ISO 26262 functional safety, watchdog timers, and fail-safe state machines
• Cellular / MQTT telemetry streaming to AWS IoT Core with cryptographic OTA updates

Q1: Why is Rust gaining massive adoption over traditional C in embedded and automotive systems?

Over 70% of high-severity vulnerabilities in C/C++ embedded systems stem from memory safety bugs (buffer overflows, dangling pointers, race conditions). Rust guarantees memory safety and thread safety at compile-time with zero runtime garbage-collection overhead, dramatically reducing safety-critical recalls in automotive and medical hardware.

Q2: How does INT8 quantization work in TinyML edge models?

INT8 quantization maps 32-bit floating-point weights and activation values into 8-bit signed integers through scaling and zero-point offsets. This reduces model memory footprint by 75% and enables 2x-4x faster integer SIMD execution on microcontrollers with minimal accuracy degradation.