| Layer | Recommended Tech | |-------|------------------| | Edge OS | Yocto Linux (ARM‑Cortex‑A53) | | Container Runtime | Docker‑Slim + container‑d | | Signal Processing | C++/Eigen for low‑latency; optional Rust bindings | | ML Inference | TensorFlow‑Lite Micro (int8 quantisation) | | Incremental Learning | TinyML‑compatible online LSTM (Edge Impulse SDK) | | XAI | SHAP‑Lite (custom C++ port) | | Messaging | MQTT‑5 (QoS 2) + AMQP 1.0 fallback | | Web UI | React 18 + TypeScript + Recharts + Material‑UI | | API | FastAPI (Python) + GraphQL (Ariadne) | | Cloud | Kubernetes (EKS/GKE), PostgreSQL, TimescaleDB for time‑series, Grafana for visualisation | | CI/CD | GitHub Actions → Buildx (multi‑arch), Trivy security scan, ArgoCD for deployment | | Security | mTLS (cert‑manager), OAuth2 + OIDC, Audit Log in Elastic Stack |
| Sensor | Measurement Range | Accuracy | Typical Use Cases | |--------|-------------------|----------|-------------------| | Temperature (Thermistor) | –50 °C to +150 °C | ±0.1 °C (±0.5 °C @ –40 °C) | Climate stations, HVAC, cold‑chain | | Relative Humidity (Capacitive) | 0 %–100 % RH | ±1.5 % RH (±3 % @ 0 %/100 %) | Greenhouses, museums | | Barometric Pressure (MEMS) | 300 hPa–1100 hPa | ±0.3 hPa | Weather forecasting, altitude tracking | | CO₂ (NDIR) | 0 – 5000 ppm | ±30 ppm + 3 % of reading | Indoor air quality, labs | | Particulate Matter (Laser Scattering) | PM₁.₀, PM₂.₅, PM₁₀ (0 – 1000 µg m⁻³) | ±10 % | Urban pollution, mine ventilation | | VOC (Metal‑oxide) | 0 – 1000 ppb | ±15 % | Industrial safety, building health | | Light (Lux) (Photodiode) | 0 – 200 000 lx | ±5 % | Solar irradiance, plant research | | Wind Speed & Direction (Ultrasonic) (optional add‑on) | 0 – 60 m s⁻¹, 0°–360° | ±0.2 m s⁻¹, ±3° | Meteorology, wind‑farm siting |
Note: The MEYD‑675 can be ordered with any combination of the above sensors. Additional specialized probes (e.g., soil moisture, water level, radiation) are available as plug‑and‑play modules. meyd675
(A comprehensive overview for engineers, researchers, and field technicians)
| Question | Answer | |----------|--------| | Can I add or remove sensors after purchase? | Yes. The MEYD‑675 uses a standardized M‑Connector (8‑pin). Swapping sensors is a tool‑free operation; the firmware automatically detects the new configuration on reboot. | | What is the warranty? | 3‑year limited warranty covering manufacturing defects. Optional extended warranty (up to 5 years) available. | | Is the data encrypted during transmission? | All LTE and LoRaWAN packets are encrypted with AES‑128 (LoRa) or TLS 1.3 (LTE). The on‑device storage can be encrypted with a user‑defined key. | | How do I update the firmware? | Over‑the‑air (OTA) via LTE or LoRaWAN (partial updates) or via USB‑C for full releases. The GUI shows the current version and any pending patches. | | Can the unit operate underwater? | The base model is IP67 (water‑resistant, not submersible). For underwater applications, the MEYD‑675‑U variant with a sealed pressure‑compensated housing (IP68) is available. | | Is there a developer kit? | Yes. The MEYD‑675‑DK includes a breakout board, API examples in Python/Node‑JS, and a simulated sensor suite for rapid prototyping. | | What is the typical latency for an alarm sent via LTE? | Under normal cellular conditions, latency is 150–300 ms from sensor trigger to cloud receipt. | | Layer | Recommended Tech | |-------|------------------| |
| Benchmark | Model | Throughput | Power (W) | Efficiency (TOPS/W) | |-----------|-------|------------|-----------|----------------------| | ImageNet‑V2 (ResNet‑50) | INT8 | 3.1 TOPS | 1.5 W | 2.07 | | Object Detection (YOLO‑v5s) | FP16 | 1.2 TOPS | 0.8 W | 1.5 | | Speech Recognition (RNNT) | INT8 | 0.9 TOPS | 0.6 W | 1.5 | | DSP FIR Filter (64‑tap) | 32‑bit | 0.5 TOPS | 0.2 W | 2.5 |
All numbers are measured on a reference board with ambient temperature 25 °C, using the latest SDK optimizations. | Sensor | Measurement Range | Accuracy |
| Domain | Typical Use‑Case | Value Proposition | |--------|------------------|-------------------| | Industrial IoT | Predictive maintenance on CNC machines | Real‑time anomaly detection without cloud latency | | Automotive | Driver‑monitoring, lane‑keeping assistance | Low‑power safety‑critical inference inside the vehicle | | Smart Cameras | Edge‑vision for retail analytics | On‑device person/gesture detection, privacy‑preserving | | Robotics | SLAM & obstacle avoidance | High‑throughput sensor fusion in a compact form factor | | Healthcare Wearables | Continuous ECG/EEG classification | Secure, on‑device diagnosis, long battery life |
| Q | Milestone |
|---|-----------|
| Q1 | • Proof‑of‑concept (PoC) – ingest 2 sensors, run static anomaly model.
• Containerised edge runtime baseline.
• Basic health‑bar UI mockup. |
| Q2 | • Implement feature extraction & TinyML inference.
• XAI “Why?” prototype.
• Alert dispatcher to SCADA (OPC‑UA). |
| Q3 | • Self‑learning loop (nightly incremental training).
• Full dashboard (KPIs, RUL gauge).
• Mobile PWA push notifications. |
| Q4 | • Multi‑tenant cloud SaaS layer (model versioning, RBAC).
• Stress test (10 kHz × 200 sensors).
• Documentation, training material, beta rollout to 2 pilot plants. |
| Post‑Launch | • Continuous model improvement (data‑driven).
• Integration with CMMS (e.g., IBM Maximo).
• Expand to energy‑optimisation use‑case. |