Enhancing Search and Rescue Operations Using AWS for FIND-911



  • 20 October 2023
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AWS Funding Secured by Cloud303
  • Well-Architected
  • Migration Acceleration Program

About the Customer

FIND-911 deploys advanced technological solutions in the form of drone fleets and ground robots across diverse terrains. Their primary objective is to facilitate search and rescue or search and recovery missions, harnessing multiple media types and various equipment for precise and timely data collection. The ultimate goal of FIND-911 is to supercharge its existing capabilities, ushering in an era of rapid, responsive, and more versatile mission capabilities, powered by the cutting-edge combination of AI/ML models and cloud-based processing.

Executive Summary

FIND-911 aimed to harness the unparalleled flexibility, elasticity, and availability offered by AWS to meet the ever-evolving demands of search and rescue operations. Through this partnership, FIND-911 envisioned an infrastructure robust enough to cater to its present needs and scale seamlessly for future requirements. Central to this vision was the deployment of an MVP during Phase 2, built atop the AWS setup from Phase 1, encapsulating the core features of the existing system and laying the foundation for future AI/ML model integrations.

The Challenge

Search and rescue missions demand speed, accuracy, and adaptability. With the sheer volume of data collected through drones and ground robots, processing this data in real-time to make mission-critical decisions becomes a daunting task. Furthermore, the vast variety of data, ranging from images to videos, requires a streamlined and organized approach for efficient processing and analysis. Achieving real-time data feed from the sensors and effectively utilizing AI/ML for refined data analysis posed significant technical and operational challenges.

Why Cloud303?

  • Expertise in AI/ML Solutions Cloud303 possesses in-depth knowledge and expertise in a wide range of machine learning algorithms and artificial intelligence models. Whether it's natural language processing, computer vision, or predictive analytics, Cloud303 is equipped to design, train, and deploy models that deliver actionable insights and drive business value.
  • Ethical and Responsible AI Ethical considerations in AI/ML are crucial, ranging from bias mitigation to data privacy. Cloud303 adheres to ethical guidelines and best practices in AI, ensuring that models are not only efficient but also fair, transparent, and responsible.
  • Scalable Data Processing Managing the massive datasets that feed AI/ML models is a significant challenge. Cloud303 provides scalable data processing solutions, optimizing both storage and computational capabilities. This ensures that your AI/ML models are trained efficiently and can scale seamlessly with your data requirements.
  • Proven Track Record Whether it's navigating complex data migrations, implementing scalable AI/ML models, or setting up robust DevOps pipelines, Cloud303 has consistently demonstrated its ability to deliver, making it a go-to partner for businesses with complex technical needs.

Engagement Overview

Cloud303's engagements follow a streamlined five-phase lifecycle: Requirements, Design, Implementation, Testing, and Maintenance. Initially, a comprehensive assessment is conducted through a Well-Architected Review to identify client needs. This is followed by a scoping call to fine-tune the architectural design, upon which a Statement of Work (SoW) is agreed and signed.

The implementation phase kicks in next, closely adhering to the approved designs. Rigorous testing ensures that all components meet the client's specifications and industry standards. Finally, clients have the option to either manage the deployed solutions themselves or to enroll in Cloud303's Managed Services for ongoing maintenance, an option many choose due to their high satisfaction with the services provided.

The Solution

Introduction to a Paradigm Shift in Rescue Operations

FIND-911's mission to revolutionize search and rescue operations commenced with a strategic partnership with Cloud303, aiming to leverage AWS's cloud infrastructure. This effort aimed to leverage the cloud's capability to distribute processing for near real-time analysis of data collected by drones and autonomous robots, marking a significant shift from the traditional method of sequentially analyzing one image at a time on local systems. This approach establishes a new benchmark for speed and precision in vital operations, emphasizing the transformational impact of cloud computing on data analysis and mission outcomes.

Constructing a Sophisticated Digital Ecosystem

The first phase of the solution involved constructing a sophisticated digital ecosystem capable of assimilating and processing the voluminous data from aerial drones and ground robots. The AWS backbone facilitated a seamless flow of information and provided agility and responsiveness in high-stakes rescue scenarios.

Securing the Data

AWS Cognito was deployed to create a robust authentication mechanism, ensuring secure access to data. Beyond authentication, the approach emphasizes comprehensive data management, from secure storage in S3 data lakes to the capability for evidentiary retrieval. This holistic security strategy ensures that all collected data is not only protected but also organized for potential legal and analytical uses, highlighting the system's preparedness for handling sensitive information in compliance with legal standards.

Designing an Intuitive Operational Interface

An intuitive operational interface was developed to empower operatives with control over the fleet of drones and robots, enabling them to define and adjust search parameters efficiently. This interface facilitated the fluid nature of rescue operations and allowed for a rapid response to emerging situations.

Advanced Data Processing and Analysis

Leveraging AWS EC2 and the serverless capabilities of AWS Lambda, FIND-911 implemented a comprehensive data analysis framework. The parallel processing capabilities of AWS ensured that high-resolution imagery and sensor data from drones and robots were efficiently transformed into strategic insights.

Machine Learning Model Development

Cloud303 tailored custom algorithms for enhanced object recognition in various visual spectra beyond standard RGB. While we harnessed AWS Rekognition for facial detection and label identification, our core innovation lied in crafting novel algorithms. These were specifically designed to analyze data from drones and robots in both visible and non-visible color spaces, improving the accuracy of object detection in search and rescue operations. The process remains iterative, with ongoing enhancements to refine these specialized algorithms."

Machine Learning Pipeline

The pipeline began with the preparation of data, where raw inputs were cleansed, labeled, and transformed into formats suitable for machine learning. Continuous Integration and Continuous Deployment (CI/CD): AWS CodePipeline was used to create a CI/CD pipeline that streamlined the model training, testing, and deployment processes. This ensured that new models and updates were consistently rolled out with minimal downtime.

Model Deployment

Deployed models were integrated into the operational workflow to provide real-time inference capabilities, enabling the system to interpret and act on data as it was received. For large datasets, batch processing was set up to efficiently handle the analysis, ensuring that insights were derived quickly to support ongoing rescue operations.

Serverless Architecture and Event-Driven Processing

The serverless architecture utilizing AWS Lambda enabled an event-driven processing model, with S3 event notifications triggering Lambda functions for immediate data processing. This approach streamlined operations and reduced the time from data capture to actionable insight.

Refining the MVP and Preparing for the Future

With the MVP successfully deployed, the focus shifted to refining the system's AI/ML model accuracy and enhancing its resilience. The user experience was improved, ensuring that the operational interface remained intuitive and powerful.

A Benchmark for Machine Learning in Search and Rescue

The collaboration between FIND-911 and Cloud303, bolstered by AWS technology, has set a new benchmark for the integration of machine learning in search and rescue operations. The deployment of sophisticated AI/ML models has positioned FIND-911 at the forefront of technological innovation, underpinning their application for the AWS Machine Learning Competency with a solid case of practical application and continuous innovation.

Engineer Quote

The combination of our expertise in computer vision, machine learning, software development, and cloud computing, in conjunction with AWS' serverless and elastic computing capabilities have propelled FIND-911's operations into a new era.

Robert Boyer Co-founder/VP of AI/ML, Cloud303


Scalability and Performance

The system scaled to process data faster than the legacy setup, with the ability to handle up to 100,000 images per hour, over 1000% increase compared to pre-AWS implementation. Auto-scaling capabilities were tested and proven to handle at least a threefold spike in data throughput, accommodating sudden surges during high-intensity operations.

Operational Efficiency

Real-time processing of drone and robot data streams led to a 40% reduction in the time taken to identify points of interest in search areas. The introduction of AI/ML models enhanced image analysis precision by 30%, reducing false positives in search patterns and improving the accuracy of rescue operations.

Cost Optimization

With the implementation of AWS Fargate and serverless architectures, the operational cost was reduced by 25% due to the elimination of over-provisioning and by leveraging pay-as-you-go pricing models.

Security and Compliance

The fortified security architecture ensured a 99.9% reduction in security incidents, with no successful breaches or data leaks post-implementation.

Innovation and Future-Proofing

The successful integration of AI/ML models laid the groundwork for future enhancements, including predictive analytics and autonomous search pattern generation. The architecture was designed to be future-proof, with the capacity to integrate next-generation AWS services and tools as they become available.

The system scaled to process data faster than the legacy setup, with the ability to handle up to 100,000 images per hour, over 1000% increase compared to pre-AWS implementation.