ESP32-CAM

Extend further - - Analytics - Automated pipelines - Dashboards for real-time monitoring

Image Capture and Storage: AWS S3 / Azure Blob Storage

Objective: Capture images from the ESP32-CAM and upload them to cloud storage for later analysis or viewing.

Steps: - ESP32-CAM: Capture images periodically or based on a trigger (like motion detection). - AWS: Use the AWS SDK for Arduino to upload the captured images to an S3 bucket. - Use AWS IoT Core to securely transmit the image data to an AWS Lambda function, which then stores the image in an S3 bucket. - Azure: Use Azure Blob Storage to store the captured images. - Use Azure IoT Hub to send data from the ESP32-CAM, then use an Azure Function to save the images to Blob Storage.

Learning Outcomes: - Understand how to integrate IoT devices with cloud storage. - Learn about secure data transmission and cloud storage using IoT services.

Required AWS Services: - S3 (Amazon Simple Storage Service) – Free Tier includes 5 GB storage. - AWS IoT Core – 250,000 messages per month.

Required Azure Services: - Azure Blob Storage – Free Tier includes 5 GB of storage. - Azure IoT Hub – Free tier allows 8,000 messages per day.

Objective: Set up motion detection (using basic image analysis or an external PIR sensor) with the ESP32-CAM and send notifications via email or SMS using AWS SNS (Simple Notification Service) or Azure Notification Hubs.

Steps: - ESP32-CAM: Use the camera’s built-in motion detection (or use a PIR sensor) to trigger an event. - AWS: Send a message to AWS IoT Core, and trigger an AWS Lambda function that sends an email/SMS through Amazon SNS. - Azure: Use Azure IoT Hub to send a message to Azure Functions, which triggers Azure Notification Hubs to send a push notification or email. - Possible Extensions: - Include an AI-based object recognition feature to only send alerts for specific objects (e.g., people or vehicles).

Learning Outcomes: - Explore the integration between edge IoT devices and cloud services. - Understand event-driven architectures in the cloud.

Required AWS Services: - AWS SNS – Free tier includes 1 million notifications per month. - AWS Lambda – 1 million requests and 400,000 GB-seconds of compute time.

Required Azure Services: - Azure Notification Hubs – Free tier allows 1 million push notifications. - Azure IoT Hub – Free tier with 8,000 messages per day.

Live Streaming: AWS Kinesis Video Streams / Azure Media Services

Objective: Stream live video from the ESP32-CAM to the cloud using AWS Kinesis Video Streams or Azure Media Services for real-time monitoring or video analytics.

Steps: - ESP32-CAM: Set up the camera to capture and stream video. - AWS: Use Kinesis Video Streams to stream live video to the cloud. You can also use Amazon Rekognition for basic video analytics (e.g., object detection, face recognition). - Azure: Use Azure Media Services to stream the video and perform tasks like live video encoding, storage, and analysis.

Learning Outcomes: - Understand how to transmit real-time video from an IoT device to the cloud. - Learn about video processing and analytics services in the cloud.

Required AWS Services: - Kinesis Video Streams – Free Tier includes 1,000,000 PUT requests and 5 GB of storage. - Amazon Rekognition (Optional) – Free tier for video includes 60 minutes per month.

Required Azure Services: - Azure Media Services – Free tier offers 20 encoding minutes and 5 GB of data transfer.

Live Streaming: via MQTT

Objective: Stream live video from the ESP32-CAM using MQTT (AWS IoT Core or Azure IoT Hub) and display it on a dashboard.
Tools Needed:
- ESP32-CAM
- AWS IoT Core or Azure IoT Hub (free tier)
- MQTT broker (AWS/Azure)
- Dashboard service (AWS IoT Core, Azure IoT Hub, or custom app)
Steps:
1. ESP32-CAM: Set up the camera to capture a continuous video stream.
2. MQTT: Use MQTT to transmit images or video data to AWS IoT Core or Azure IoT Hub.
3. AWS/Azure: Set up an AWS IoT or Azure IoT rule to forward the video stream to a dashboard.
4. Display the video feed in real time on a dashboard or custom application.
Learning Outcomes:
- Understand how to transmit real-time video from an IoT device to the cloud using MQTT.
- Learn about setting up and using AWS IoT Core or Azure IoT Hub for video streaming.
Required AWS Services:
- AWS IoT Core – Free tier includes 250,000 messages per month.
- AWS Lambda (Optional) – Free tier includes 1,000,000 requests and 400,000 GB-seconds of compute time per month for processing video streams.
Required Azure Services:
- Azure IoT Hub – Free tier includes 8,000 messages per day.
- Azure Functions (Optional) – Free tier includes 1,000,000 requests and 400,000 GB-seconds of compute time per month for processing video streams.
Possible Extensions:
- Integrate with AWS Lambda/Azure Functions to process the video stream in real-time for object detection or face recognition.

Face Recognition: AWS Rekognition / Azure Cognitive Services

Objective: Perform face recognition with the ESP32-CAM by integrating it with cloud-based AI services like AWS Rekognition or Azure Cognitive Services.

Steps: - ESP32-CAM: Capture images and send them to the cloud for processing. - AWS: Use AWS Rekognition to detect and recognize faces from the images sent by the ESP32-CAM. You can also implement facial comparisons or label detection. - Azure: Use Azure Cognitive Services (specifically the Face API) to perform face detection and recognition on the images sent from the ESP32-CAM.

Possible Extensions:
- Add a logging system where each recognized face is logged with a timestamp in AWS DynamoDB or Azure Cosmos DB.
- Set up notifications when a specific face is recognized.
- Learning Outcomes:
Gain exposure to cloud-based AI and machine learning services.
Learn how to integrate computer vision with IoT devices.

Required AWS Services: - AWS Rekognition – Free tier includes 5,000 images per month for face analysis.

Required Azure Services: - Azure Cognitive Services – Face API – Free tier includes 30,000 transactions per month.

Temperature Monitoring: AWS IoT Analytics / Azure Time Series Insights

Objective: Use the ESP32-CAM along with a temperature sensor (e.g., DHT11 or DHT22) to monitor environmental conditions and visualize data in the cloud.

Steps: - ESP32-CAM: Connect a DHT sensor to measure temperature and humidity. Send data to the cloud along with images captured periodically. - AWS: Use AWS IoT Analytics to analyze and visualize the temperature data. You can build time-series dashboards to monitor changes over time. - Azure: Use Azure Time Series Insights to create time-series graphs of temperature readings and explore the data interactively.

Possible Extensions: - Set up temperature thresholds to trigger alerts or activate devices like fans.

Learning Outcomes: - Understand how to collect and analyze time-series IoT data. - Learn how to build IoT data pipelines and visualize the results.

Required AWS Services: - AWS IoT Analytics – Free Tier includes 2,500 messages per month.

Required Azure Services: - Azure Time Series Insights – Free tier includes 30 days of data retention and 10,000 events.

Security System: AWS IoT & Lambda / Azure Logic Apps

Objective: Build a simple security system using the ESP32-CAM and cloud services to notify you whenever motion is detected, and store images for review.

Steps: - ESP32-CAM: Use motion detection or a PIR sensor to trigger image capture. - AWS: Send the captured image to AWS IoT Core, then use AWS Lambda to store the image in S3 and notify you through Amazon SNS. - Azure: Use Azure IoT Hub to send data to Azure Logic Apps, where you can trigger storage of the image in Blob Storage and send a notification.

Learning Outcomes: - Learn how to build a basic IoT security system. - Explore serverless functions to process IoT data and trigger events.

Required AWS Services: - AWS IoT Core and AWS Lambda – Free tier as mentioned in previous examples. - Amazon SNS – Free tier includes 1 million notifications.

Required Azure Services: - Azure IoT Hub and Azure Logic Apps – Free tier as mentioned in previous examples.

Security Surveillance with AWS DynamoDB / Azure Cosmos DB

Objective: Use the ESP32-CAM to monitor a location and store image or video capture events in AWS DynamoDB or Azure Cosmos DB.
Tools Needed:
- ESP32-CAM
- AWS DynamoDB or Azure Cosmos DB (free tier)
- AWS S3 or Azure Blob Storage (for storing images/videos)
- Cloud-based dashboard service (AWS or Azure)
Steps:
1. ESP32-CAM: Set up the camera to capture periodic images or detect motion.
2. Storage: Store captured images or videos in AWS S3 or Azure Blob Storage.
3. Database: Store metadata (like timestamps and camera ID) in DynamoDB or Cosmos DB, along with links to the captured images/videos.
4. Dashboard: Use a cloud-based dashboard to query and visualize historical data (like images captured over time).
Learning Outcomes:
- Understand how to capture and store surveillance data from an IoT device.
- Learn about using cloud databases and storage services for managing surveillance data.
- Develop skills in creating dashboards for visualizing historical data.
Required AWS Services:
- AWS DynamoDB – Free tier includes 25 GB of storage.
- AWS S3 – Free tier includes 5 GB of standard storage.
- AWS CloudWatch (Optional) – For monitoring and logging.
Required Azure Services:
- Azure Cosmos DB – Free tier includes 400 RU/s provisioned throughput and 5 GB of storage.
- Azure Blob Storage – Free tier includes 5 GB of standard storage.
- Azure Monitor (Optional) – For monitoring and logging.
Possible Extensions:
- Create a time-lapse video of all images captured over a period.
- Integrate with AWS Lambda or Azure Functions to trigger alerts based on specific events (e.g., motion detected).

Time-Lapse Video Creator with AWS S3 / Azure Blob Storage

Objective: Use the ESP32-CAM to capture images at regular intervals and upload them to AWS S3 or Azure Blob Storage to create a time-lapse video.
Tools Needed:
- ESP32-CAM
- AWS S3 or Azure Blob Storage (free tier)
- Optional: AWS Lambda or Azure Functions for automation
Steps:
1. ESP32-CAM: Set up the camera to capture images every X minutes or hours.
2. Storage: Upload the captured images to AWS S3 or Azure Blob Storage.
3. Processing: Use a script or service (AWS Lambda or Azure Functions) to stitch the images into a time-lapse video.
4. Output: Store the generated time-lapse video in a separate bucket or location for easy access.
Learning Outcomes:
- Understand how to capture and store images at regular intervals using an IoT device.
- Learn about using cloud storage services for managing and storing images.
- Develop skills in automating the creation of time-lapse videos using cloud functions.
Required AWS Services:
- AWS S3 – Free tier includes 5 GB of standard storage.
- AWS Lambda (Optional) – Free tier includes 1,000,000 requests and 400,000 GB-seconds of compute time per month for processing images.
Required Azure Services:
- Azure Blob Storage – Free tier includes 5 GB of standard storage.
- Azure Functions (Optional) – Free tier includes 1,000,000 requests and 400,000 GB-seconds of compute time per month for processing images.
Possible Extensions:
- Automate the video generation process, storing videos in a separate bucket or location for easy access.
- Integrate with notification services (AWS SNS or Azure Notification Hubs) to alert when a new time-lapse video is available.