AWS IoT Greengrass at Synadia

AWS IoT Greengrass enables Synadia to execute secure, deterministic and production grade logic directly at the industrial edge.

Our deployments implement AWS IoT Greengrass Core to support local data collection, real time analysis and autonomous machine level action, while synchronizing securely with AWS IoT Core using certificate based authentication and device shadow mechanisms.

By combining Lambda execution on edge devices, machine learning inference and secure over the air updates, Synadia delivers mission critical systems that continue operating even during cloud connectivity interruptions.

AWS IoT Greengrass enables execution of Lambda functions directly on edge devices, allowing systems to sense, analyze and act locally. At Synadia, Greengrass Core is deployed as a secure runtime environment that integrates with AWS IoT Core through device shadow synchronization, MQTT over TLS and certificate based identity management.

Our Greengrass implementations are production proven and aligned with AWS best practices across cost optimization, reliability, performance efficiency, security and operational excellence.

Industrial edge systems must do more than forward data to the cloud.

They must operate as autonomous execution layers capable of sensing local events, analyzing conditions and acting deterministically at machine level.

At Synadia, AWS IoT Greengrass is positioned at the center of this operational spectrum. It enables secure local execution, synchronized cloud integration and controlled device lifecycle management within mission critical environments.

The spectrum below outlines how AWS IoT Greengrass is applied across each architectural layer.

AWS IoT Greengrass Core devices ingest telemetry directly from PLCs, sensors and industrial controllers. Data is normalized and validated locally before any cloud synchronization occurs.

Payload size and message frequency are carefully optimized. Event driven publishing is preferred over constant polling to reduce bandwidth consumption and improve cost efficiency.

All communication between edge and AWS IoT Core occurs over MQTT with TLS encryption using certificate based authentication.

AWS Lambda functions are deployed directly onto AWS IoT Greengrass Core devices. These functions execute locally to:

This eliminates unnecessary cloud round trips and ensures deterministic processing close to the machine.

Greengrass Core subscribes to device shadow delta topics and translates desired state updates into local machine commands.

When predefined thresholds are exceeded, local Lambda functions trigger immediate responses such as:

These actions occur without dependency on active AWS connectivity, ensuring operational continuity.

AWS IoT Greengrass integrates tightly with AWS IoT Core device shadows to synchronize desired and reported state between cloud and edge. Greengrass Core devices subscribe to shadow delta updates and translate configuration changes into deterministic local execution.

Desired state changes initiated in the cloud are applied locally without exposing the device to inbound connections. Reported state updates are published back to AWS IoT Core in a controlled manner, ensuring traceability while maintaining strict limits on shadow document size and update frequency.

This structured command and control mechanism ensures secure bidirectional communication while preserving tenant isolation and operational stability.

Operational excellence at the edge requires more than secure connectivity. Greengrass Core devices must be provisioned, monitored and updated throughout their lifecycle.

Synadia provisions Greengrass Core devices using certificate based identity management and scoped IAM roles that enforce least privilege. Certificate rotation strategies and controlled onboarding flows reduce long term security risk.

Application components, Lambda functions and machine learning models are deployed using secure over the air mechanisms. Updates are version controlled and validated before activation to prevent operational disruption. Monitoring mechanisms track device health and message integrity, enabling controlled recovery and device replacement when required.

Machine learning models are trained in Amazon SageMaker and stored as versioned artifacts in Amazon S3. These models are packaged and deployed to AWS IoT Greengrass Core devices where inference engines execute locally.

Running inference at the edge enables real time anomaly detection, predictive quality control and immediate operational feedback without requiring a cloud round trip. Edge devices access local hardware resources such as CPU and memory to execute inference workloads efficiently.

Model updates follow controlled deployment pipelines and are distributed securely using over the air mechanisms, ensuring consistent behavior across distributed industrial environments.

AWS IoT Greengrass enables Synadia to extend secure cloud architecture directly into industrial environments where deterministic execution is required. By combining local Lambda execution, structured device shadow synchronization and autonomous machine level control, our edge systems continue to sense, analyze and act regardless of cloud connectivity status.

Greengrass Core is deployed as a controlled execution layer, not as a message forwarder. It performs local data normalization, threshold evaluation and machine learning inference before synchronizing structured events with AWS IoT Core and downstream serverless services.

Through certificate based provisioning, secure over the air updates and disciplined payload management, Synadia ensures that edge devices remain secure, resilient and operational throughout their lifecycle. This architecture transforms isolated industrial assets into intelligent, self managing systems fully integrated with the AWS Cloud.

Read more about IoT GreenGrass