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This Guidance demonstrates how you can enhance your airport’s operational efficiency and improve the passenger experience using the Internet of Things (IoT). Connect critical airport assets—like heating, ventilating, and air-conditioning (HVAC) systems; baggage handling, security, and access control systems; runways, taxiways, and passenger boarding bridges; and equipment and aircraft. Then, you can build an asset-monitoring system that provides insights and helps you streamline operations. You’ll be able to enact predictive maintenance, respond rapidly to downtime and disruptions, manage employee badges and tags, reduce passenger wait times, and make more-informed decisions.
Please note: [Disclaimer]
Architecture Diagram
[Architecture diagram description]
Step 1
Use AWS IoT Greengrass core device to connect to, publish to, and subscribe to data from airport assets on the edge using the open standard Message Queuing Telemetry Transport (MQTT) protocol.
Step 2
Use AWS IoT Core to maintain shadows of airport assets, connect to AWS, and manage messages from Internet of Things (IoT) sensors for further processing.
Step 3
Create a detector model in AWS IoT Events with AWS IoT Core as the input source. Configure Amazon Simple Notification Service (Amazon SNS) in the detector model to send notifications by SMS or email when an unusual event occurs or when a sensor reaches your set thresholds.
Step 4
Use AWS IoT Analytics to aggregate, transform, and analyze IoT messages from AWS IoT Core. Build an IoT analysis dashboard and visualize on Amazon QuickSight.
Step 5
Configure an IoT rule to send messages from AWS IoT Core to Amazon Kinesis Data Streams for downstream processing.
Step 6
Use an AWS Lambda function to process messages from Kinesis Data Streams, and store them on Amazon DynamoDB.
Step 7
Amazon Kinesis Data Firehose reads data from Kinesis Data Streams and stores it in an Amazon Simple Storage Service (Amazon S3) data lake. Use AWS Glue to transform data and store it back on Amazon S3.
Step 8
Use Amazon SageMaker to build, train, and validate machine learning (ML) models for predictive maintenance and anomaly detection of airport assets. Optionally, use this ML model inference with an AWS IoT Greengrass core device on the edge.
Step 9
Use a Lambda function to process all IoT data stored on a DynamoDB table and fetch the ML model inference endpoint for predictions. Create a REST API with a Lambda function as a backend on Amazon API Gateway.
Step 10
Develop an airport operations web application to centralize asset monitoring and predictive maintenance capabilities. Also, integrate a QuickSight dashboard using QuickSight embeddings.
Step 1
Use AWS IoT Greengrass core device to connect to, publish to, and subscribe to data from airport assets on the edge using the open standard Message Queuing Telemetry Transport (MQTT) protocol.
Step 2
Use AWS IoT Core to maintain shadows of airport assets, connect to AWS, and manage messages from Internet of Things (IoT) sensors for further processing.
Step 3
Create a detector model in AWS IoT Events with AWS IoT Core as the input source. Configure Amazon Simple Notification Service (Amazon SNS) in the detector model to send notifications by SMS or email when an unusual event occurs or when a sensor reaches your set thresholds.
Step 4
Use AWS IoT Analytics to aggregate, transform, and analyze IoT messages from AWS IoT Core. Build an IoT analysis dashboard and visualize on Amazon QuickSight.
Step 5
Configure an IoT rule to send messages from AWS IoT Core to Amazon Kinesis Data Streams for downstream processing.
Step 6
Use an AWS Lambda function to process messages from Kinesis Data Streams, and store them on Amazon DynamoDB.
Step 7
Amazon Kinesis Data Firehose reads data from Kinesis Data Streams and stores it in an Amazon Simple Storage Service (Amazon S3) data lake. Use AWS Glue to transform data and store it back on Amazon S3.
Step 8
Use Amazon SageMaker to build, train, and validate machine learning (ML) models for predictive maintenance and anomaly detection of airport assets. Optionally, use this ML model inference with an AWS IoT Greengrass core device on the edge.
Step 9
Use a Lambda function to process all IoT data stored on a DynamoDB table and fetch the ML model inference endpoint for predictions. Create a REST API with a Lambda function as a backend on Amazon API Gateway.
Step 10
Develop an airport operations web application to centralize asset monitoring and predictive maintenance capabilities. Also, integrate a QuickSight dashboard using QuickSight embeddings.
Well-Architected Pillars
The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
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Operational ExcellenceRead the Operational Excellence whitepaper
Amazon CloudWatch provides near real-time visibility into infrastructure and application performance through detailed metrics and logs. For example, it gives you insights into the performance of Lambda functions, enabling you to identify and resolve issues quickly.
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SecurityRead the Security whitepaper
AWS Identity and Access Management (IAM) lets you grant granular and least-privilege permissions so that users only have access to the specific resources they need. API Gateway enhances security for backend services and data by providing authentication and access control through API keys, helping you restrict access to your APIs and limit API rates using throttling. It also provides access logs and implementation logs to give you visibility into API usage and help you identify security issues.
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ReliabilityRead the Reliability whitepaper
AWS IoT Core enables reliable bidirectional communication between IoT-connected airport assets and AWS services. It can handle a high volume of messages from many assets and reliably route those messages to AWS for downstream processing and connecting. It also helps you reliably collect and process telemetry IoT data from your airport assets. AWS IoT Core scales to support any number of devices without compromising on reliability, and the built-in retries facilitate reliable communication at scale. Additionally, AWS IoT Greengrass core devices can continue to operate locally if disconnected from the AWS Cloud.
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Performance EfficiencyRead the Performance Efficiency whitepaper
DynamoDB provides fast, predictable performance by spreading data across multiple Availability Zones. It offers single-digit latency at scale, so you can build highly responsive applications with predictable performance at any scale, and provision the throughput capacity you need without having to overprovision for peak usage.
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Cost OptimizationRead the Cost Optimization whitepaper
Amazon S3 provides cost-effective storage for any amount of data at scale. Its object life-cycle management and storage tiering reduce costs by automatically transitioning less frequently accessed data to more affordable tiers, such as Amazon S3 Standard-Infrequent Access (S3 Standard-IA) or Amazon S3 Glacier storage classes. This minimizes the overall storage expenses of your architecture at scale.
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SustainabilityRead the Sustainability whitepaper
AWS IoT Greengrass enables local compute, messaging, device shadow, and ML inference capabilities on edge devices. Performing compute and inference locally is more energy efficient than sending large amounts of data back and forth between local devices and the AWS Cloud. By reducing the need to transmit data to AWS for analysis, you can save network bandwidth and energy consumption and reduce the overall carbon footprint of your workloads.
Implementation Resources
A detailed guide is provided to experiment and use within your AWS account. Each stage of building the Guidance, including deployment, usage, and cleanup, is examined to prepare it for deployment.
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
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Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.