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Guidance for Connected Restaurants Using Internet of Things on AWS

Overview

This Guidance shows how you can enhance your restaurant’s operational efficiency and employee experience through Internet of Things (IoT)–connected kitchen devices. By integrating AWS services with smart kitchen equipment (such as thermostats, robots, dishwashers, refrigerators, ovens, and grills), you can enable predictive maintenance powered by machine learning (ML), near real-time equipment, and inventory monitoring, while also gaining visibility into functional robotic equipment. Through this Guidance, you can generate data-driven insights that minimize equipment downtime, streamline kitchen operations, and enable informed decision-making, ultimately helping you reduce waste and increase revenue.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram

Well-Architected Pillars

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.

Amazon CloudWatch provides visibility into infrastructure and application performance. This enables proactive monitoring, fast troubleshooting of errors and issues, and near real-time responses to events and incidents.

Read the Operational Excellence whitepaper

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. When used with API Gateway, AWS Identity and Access Management(IAM) provides fine-grained access controls to your APIs and SSL policies. IAM also provides data encryption help protect data while in transit and at rest. API Gateway provides access logs and implementation logs to give you visibility into API usage and help you identify security issues.

Read the Security whitepaper

AWS IoT Core enables reliable bi-directional communication between IoT-connected kitchen equipment and AWS services. It can handle a high volume of messages from many pieces of kitchen equipment and reliably routes those messages to AWS for downstream processing and connecting. AWS IoT Core scales to support any number of devices without compromising on reliability, and the built-in retries facilitate communication at scale. Additionally, AWS IoT Greengrass core devices can continue to operate locally if disconnected from the AWS Cloud.

Read the Reliability whitepaper

SageMaker enables users to efficiently train and deploy ML models at scale for their IoT telemetry data. Its distributed approach scales model training across multiple nodes to reduce training time. Additionally, its automatic model tuning finds the optimized hyperparameters, and its model inference provides responses with low latency.

Read the Performance Efficiency whitepaper

DynamoDB centrally stores all IoT data. The DynamoDB Time to Live (TTL) feature deletes this data from your tables based on configured thresholds. It does this without consuming any write throughput, so you do not need to pay for additional storage, and you can keep your storage costs optimized.

Read the Cost Optimization 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 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, reducing the overall carbon footprint of your workloads.

Read the Sustainability whitepaper

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.