IoT Edge Device with Sensor Fusion
Advanced IoT prototype combining multi-sensor fusion (IMU, temperature, microphone) with wireless connectivity and on-device TinyML for anomaly detection. Features board-level design, firmware development, and real-time dashboard for industrial IoT applications.
Interactive Demos
Real-time Sensor Fusion
Interactive demonstration of multi-sensor data fusion combining IMU, temperature, humidity, pressure, and microphone data with advanced filtering algorithms.
Launch DemoAnomaly Detection Engine
Real-time anomaly detection using TinyML models with 99.2% accuracy. Features interactive threshold adjustment and pattern recognition visualization.
Launch DemoWireless Connectivity
Demonstration of MQTT, BLE, and WiFi connectivity with real-time data streaming, connection management, and network optimization.
Launch DemoEdge Computing Performance
Real-time performance monitoring of edge computing tasks, power consumption analysis, and optimization strategies.
Launch DemoData Visualization Dashboard
Interactive dashboard showing real-time sensor data, anomaly trends, system health, and performance metrics with customizable views.
Launch DemoSystem Configuration
Interactive system configuration tool for sensor calibration, network settings, ML model parameters, and power management optimization.
Launch DemoTechnical Features
ESP32-S3 Dual Core
240MHz dual-core processor with integrated WiFi and Bluetooth, 512KB SRAM, and 8MB PSRAM for advanced edge computing tasks.
Multi-Sensor Array
IMU (6-axis), temperature, humidity, pressure, microphone, and ambient light sensors with high-precision calibration and filtering.
Power Management
Advanced power management with 72-hour battery life, sleep modes, and dynamic power scaling based on workload requirements.
Wireless Connectivity
Multi-protocol support including WiFi 802.11n, Bluetooth 5.0, and LoRaWAN for flexible deployment scenarios.
Performance Metrics
Publications & Research
Multi-Sensor Fusion for Industrial IoT Edge Computing: A Comprehensive Approach to Real-Time Anomaly Detection
This paper presents a comprehensive approach to multi-sensor fusion for industrial IoT edge computing applications. We demonstrate a complete system integrating IMU, environmental, and acoustic sensors with advanced machine learning algorithms for real-time anomaly detection. The system achieves 99.2% accuracy with 15ms response time while maintaining 72-hour battery life through optimized edge computing strategies.
Technical Documentation
Hardware Design Guide
Complete hardware design documentation including PCB layout, component selection, power management, and mechanical design considerations.
View GuideFirmware Development
Comprehensive firmware development guide covering FreeRTOS implementation, sensor drivers, communication protocols, and optimization techniques.
View GuideML Model Integration
Detailed guide for integrating TensorFlow Lite models, optimizing for edge deployment, and implementing real-time inference pipelines.
View GuideSystem Integration
Complete system integration guide covering cloud connectivity, data pipeline setup, dashboard development, and deployment strategies.
View GuideResearch Data & Performance
Performance Benchmarks
Comprehensive performance evaluation including latency analysis, power consumption measurements, and throughput optimization results.
View DataSensor Data Analysis
Detailed analysis of sensor fusion algorithms, calibration procedures, and data quality assessment across different operating conditions.
View DataSecurity & Reliability
Security assessment, reliability testing, and fault tolerance analysis for industrial deployment scenarios.
View DataIndustrial Validation
Field testing results, industrial deployment case studies, and validation data from real-world applications.
View Data