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Edge Computing with AWS Outposts: Architecting Low-Latency Data Processing for Industrial IoT

In this article, we explore a production-grade architecture for low-latency data processing using AWS Outposts, AWS IoT Core, and AWS Lambda for edge analytics.

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Ryan Tan
Published Feb 23, 2025

Introduction

Industrial IoT environments—such as manufacturing plants, refineries, or energy facilities—require processing of sensor data in near real-time. By deploying AWS Outposts, organizations can run AWS services locally, ensuring that critical workloads are not affected by cloud latency or network interruptions. This solution leverages:
  • AWS Outposts: To host AWS services locally.
  • AWS IoT Core and Greengrass (optional): To manage and route IoT device data.
  • AWS Lambda: To process IoT data at the edge.
  • Cloud connectivity: For further analytics, long-term storage, and dashboarding.

Architectural Overview

The solution architecture consists of the following key components:
  • Industrial IoT Devices: Sensors and machines that generate data.
  • Local Edge Gateway: Aggregates and preprocesses sensor data.
  • AWS Outposts: Hosts core AWS services in the local data center.
  • AWS IoT Core: Receives IoT messages from the local gateway.
  • Edge Data Processing: A Lambda function deployed on Outposts performs real-time data analytics.
  • Cloud Analytics & Storage: Processed data is forwarded to the cloud for further analysis and long-term storage.
Diagram Explanation:
  • Industrial IoT Devices: Produce real-time data such as sensor readings.
  • Local Edge Gateway: Aggregates data and routes it securely to the local AWS Outposts.
  • AWS Outposts: Hosts critical services (e.g., IoT Core, Lambda) near the devices to ensure low latency.
  • AWS IoT Core (Local Endpoint): Receives and routes IoT messages within the local network.
  • AWS Lambda (Edge Processing): Processes and transforms data for real-time decision making.
  • Cloud Analytics & Storage: The processed data is sent to the cloud for further insights and long-term retention.

Sample Edge Processing Lambda Function

Below is a sample Python Lambda function intended to be deployed on AWS Outposts. This function processes IoT sensor data—converting temperature readings from Celsius to Fahrenheit as an example—and logs the results for further action.
Explanation:
  • Input Event:
    The function expects an event containing a data object with a temperature field (in Celsius).
  • Processing Logic:
    It converts the temperature to Fahrenheit and prints/logs the result.
  • Error Handling:
    The function raises appropriate errors if expected data is missing.

Best Practices

  1. Network Connectivity:
    • Ensure robust connectivity between edge devices, Outposts, and the AWS Region.
    • Use VPC peering or VPNs for secure integration.
  2. Security and Access Control:
    • Implement IAM roles with least privilege for Lambda, IoT Core, and S3.
    • Employ encryption in transit (TLS) for sensor data and at rest for stored data.
  3. Monitoring and Logging:
    • Configure Amazon CloudWatch (local logs) on Outposts for Lambda logging and IoT Core metrics.
    • Forward logs to the AWS Region for centralized analysis.
  4. Reliability and High Availability:
    • Use AWS Outposts capacity planning to ensure sufficient compute and storage resources on-prem.
    • Consider multi-AZ or multi-Region strategies for mission-critical workloads.
  5. Data Lifecycle Management:
    • Retain critical data on-prem if required by compliance or operational constraints.
    • Archive historical data in the cloud for cost-effective retention.

Conclusion

Edge computing with AWS Outposts offers a powerful solution for industrial IoT use cases requiring low-latency data processing and near real-time analytics. By running AWS services on-premises, organizations can maintain continuous operations even when network connectivity is limited. The architecture outlined in this article shows how to process data locally on Outposts before streaming it to the AWS Region for deeper analytics and long-term storage.
Key Takeaways:
  • Low Latency: Critical decisions and processing happen on-premises to minimize round-trip delays.
  • Seamless Cloud Integration: Outposts simplifies hybrid architectures by offering consistent APIs and services across edge and cloud.
  • Scalability & Flexibility: Easily incorporate additional AWS services (e.g., machine learning, big data) in the cloud when needed.
By following best practices for network connectivity, security, and monitoring, enterprises can build robust, highly performant edge solutions tailored to industrial IoT demands.
 

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