About This Architecture

AquaSentinel deploys a five-layer edge-cloud AI architecture on AWS for real-time aquaculture monitoring across tilapia, carp, sturgeon, and Nile perch species. Kinesis Data Streams ingests sensor telemetry (DO, pH, temperature, ammonia) while S3 Data Lake stores batch feed logs and mortality records; LSTM/Transformer models process time-series sequences, Random Forest handles tabular inference, and a multi-task shared backbone neural network with species-specific heads predicts growth, feed efficiency, and health metrics. YOLOv8 object detection runs on Jetson Orin edge nodes for fish counting and biomass estimation via underwater camera feeds, with DeepSORT tracking and autoencoder-based behavior anomaly detection; Bayesian MC Dropout provides confidence intervals, Mahalanobis distance triggers out-of-distribution abstention, and ADWIN monitors concept drift in live streams. SHAP/LIME explainers surface top contributing factors, rule-based safety overrides enforce dissolved oxygen thresholds, and a digital twin bioenergetics model validates predictions before actuation; SageMaker endpoints serve TensorFlow models, IoT Greengrass manages edge runtime, and CodePipeline orchestrates MLOps retraining with OTA updates via IoT Core. Fork this diagram on Diagrams.so to customize sensor pipelines, swap edge accelerators (Coral TPU alternatives), or adapt the multi-task architecture for other species or industrial IoT domains.

People also ask

How do I build an edge-cloud ML pipeline on AWS with real-time sensor ingestion, multi-task neural networks, and OTA model updates for IoT devices?

Use Kinesis Data Streams for real-time sensor ingestion, train multi-task models with TensorFlow and deploy to SageMaker endpoints, run YOLOv8 inference on Jetson Orin edge nodes with IoT Greengrass, and orchestrate OTA updates via IoT Core. This AquaSentinel diagram shows the full five-layer architecture with LSTM time-series models, SHAP explainability, and digital twin validation.