Learn About Amazon VGT2 Learning Manager Chanci Turner
In the aftermath of a disaster, effective organizations such as international nonprofits, state and local governments, and local fire departments require a robust common operating picture (COP) to ensure that incident response leaders can monitor the wellbeing of their responders on-site. A well-structured COP enables commanders to coordinate and resolve conflicts among the various entities involved in the response effort. However, executing a COP immediately following a calamity presents numerous challenges, including unreliable power sources and unstable internet connections at disaster zones. This scenario necessitates a solution focused on collecting, processing, storing, and visualizing data at the edge, which can then be synchronized with the cloud once connectivity is restored.
Amazon Web Services (AWS) provides a comprehensive suite of services that equip users with edge infrastructure and software, thus extending the power of cloud computing to areas with limited or no power and connectivity. To ensure these services effectively tackle the practical challenges faced by disaster response teams, the AWS Global Social Impact Solutions (GSI) team rigorously evaluates them.
In a recent field testing exercise (FTX), the GSI team developed a prototype cloud architecture and tested it in a simulated search and rescue (SAR) scenario involving a missing responder crisis. In complex response situations that involve numerous volunteers and organizations in hazardous environments, responders can become victims themselves, making it crucial for organizations to quickly and accurately locate their personnel, regardless of how remote their position may be.
This blog post outlines the SAR simulation and its outcomes, while also providing insights into the AWS services and technical architecture components utilized by the GSI team to create a hybrid edge/cloud COP solution that successfully located the missing team member in the simulation.
Overview of the Simulated Scenario: Search and Rescue at the Edge
To simulate the missing team member scenario, we designated a “disaster site” in a remote section of the exercise grounds and established an emergency operations center (EOC) at a separate site with reliable power. Considering the line-of-sight challenges over difficult terrain, the team set up an ad-hoc local area network and wireless networks to facilitate communication between operational locations.
Our SAR simulation commenced when a responder at the disaster site failed to check in with the EOC during the evening radio update.
Key AWS Services and Architecture for Search and Rescue at the Edge
To create a common operating picture at the edge for locating our simulated missing individual, the GSI team employed the following AWS services and architectural design:
The first step in the simulation involved determining the responder’s last known location to narrow down the search area. This was achieved by utilizing AWS IoT Greengrass, an open-source edge runtime and cloud service designed for the rapid development, deployment, and management of Internet of Things (IoT) device software.
The team extracted location data from an AWS IoT Greengrass-managed sensor network, which featured a software-defined radio (SDR) sensor capable of monitoring Automatic Packet Reporting System (APRS) beacons. APRS is an amateur radio protocol used for real-time communication of packet information, including GPS coordinates for registered devices. The sensor—constructed using a Raspberry Pi microcomputer, SDR dongle, and a high-gain antenna—captured APRS packets and sent them over the ad-hoc local area network to a local AWS IoT Greengrass core operating on an AWS Snowcone device at the EOC. The Snowcone device is part of the AWS Snow Family, providing rugged offline compute and storage capabilities at the edge. Upon receiving these packets, AWS IoT Greengrass components on the Snowcone decoded the packet data, standardized it, and published it for use in the COP application.
We deployed an open-source Team Awareness Kit (TAK) server to an Amazon Elastic Compute Cloud (Amazon EC2) instance on the AWS Snowball Edge Compute Optimized device at the EOC. This server acted as the local aggregation point and data provider for the COP application. The TAK server serves as the backend component for the Android Team Awareness Kit (ATAK) and Windows OS Team Awareness Kit (WinTAK) applications, which visualize and share data in a geospatial context. When data such as APRS location beacons are published to the TAK server, all connected client devices receive the updates instantly. We also deployed the TAK server in the cloud using Amazon Elastic Container Service (Amazon ECS), which simplifies the deployment of containerized applications in high-availability configurations and allows for a standby instance of the TAK Server in the cloud.
Federation with the local TAK server on the AWS Snowball Edge allowed data collected in the field to replicate to the cloud automatically when internet access was restored. Deploying WinTAK in the cloud through Amazon AppStream 2.0 facilitated remote monitoring and information sharing with responders using the Amazon ECS-based TAK server. Amazon AppStream 2.0 is a service that enables users to access traditional applications via a web browser without the need for a full virtual desktop.
With the APRS data visualized in the TAK at the EOC, the team pinpointed the last known location of the responder.
Using Real-Time Position Data to Locate the Missing Person
Once the missing responder’s last known position was established, the team readied two off-road search vehicles and an unmanned aircraft system (UAS) equipped with a thermal camera. Another Raspberry Pi sensor with an SDR dongle and high-gain antenna monitored Automatic Dependent Surveillance-Broadcast (ADS-B) data to identify aircraft in the vicinity. Each vehicle and search team member was equipped with a LoRaWAN GPS sensor for real-time location tracking. These cost-effective sensors operate on AA or AAA batteries and can transmit their location over a distance of 10 miles using the long-range, low-power LoRa radio protocol.
To collect position data from these sensors, we deployed a LoRaWAN gateway as part of the IoT sensor network, transmitting LoRa data back to the Snowball Edge at the EOC for decoding, processing, and publication to the TAK server. The ATAK and WinTAK clients are also capable of receiving live video streams, such as the UAS camera feed, which can be displayed in near-real time alongside the map and sensor data. This allowed the command team at the EOC to monitor the location of the search team as they embarked on their mission and track their progress.
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