# A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
## 4 Implementation  
For the implementation, we used Amazon Web Services (AWS) as our cloud service provider. AWS helped us to set up and manage our system in a reliable, fast, and scalable manner. AWS provided us with various services and features that suited our system needs and use cases. For example, we used AWS Elastic Compute Cloud (EC2) ([[https://aws.amazon.com/ec2/instance-types/t3/](https://aws.amazon.com/ec2/instance-types/t3/)]([https://aws.amazon.com/ec2/instance-types/t3/](https://aws.amazon.com/ec2/instance-types/t3/)) (accessed 15 December 2023)) to launch virtual servers with different resources that we could adjust as needed. We also used AWS security and governance services to protect our data and applications from unauthorized access and threats. To set up and manage our system on AWS, we chose an EC2 instance, and the configuration parameters and values are detailed in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Cloud service provider & AWS \\ Instance type & t3.xlarge \\ Operating system & Windows Server 2022 \\ CPU & Intel Xeon Platinum 8259 CL \\ RAM & 16 GB \\ Disk size & 30 GB \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of the configuration parameters and values.  
After launching the EC2 instance, we installed Docker version 4.16.2 ([[https://docs.docker.com/desktop/release-notes/#4160](https://docs.docker.com/desktop/release-notes/#4160)]([https://docs.docker.com/desktop/release-notes/#4160](https://docs.docker.com/desktop/release-notes/#4160)) (accessed 17 December 2023)) on the virtual machine. We created two containers based on Arch Linux using Docker. The first container ran the code for the data acquisition layer and ambulance detection layer of our system. The data acquisition layer collected data from various sources, such as sensors, cameras, GPS devices, etc. The ambulance detection layer analyzed the data and detected any ambulances in the vicinity. This container also included a Python interpreter version 3.10.11 ([[https://www.python.org/downloads/release/python-31011/](https://www.python.org/downloads/release/python-31011/)]([https://www.python.org/downloads/release/python-31011/](https://www.python.org/downloads/release/python-31011/)) (accessed 18 December 2023)) and NodeJS version 18.16.0 ([[https://nodejs.org/en/blog/release/v18.16.0](https://nodejs.org/en/blog/release/v18.16.0)]([https://nodejs.org/en/blog/release/v18.16.0](https://nodejs.org/en/blog/release/v18.16.0)) (accessed 18 December 2023)). Python and NodeJS helped us write and run our code for the data acquisition layer and ambulance detection layer of our system. Python enabled us to write concise and readable code that could handle complex tasks and data structures. NodeJS allowed us to use JavaScript for both front-end and back-end development, simplifying our codebase and improving performance. The second container ran the code for the monitoring system for our system. The monitoring system enabled us to monitor the system's performance and status in real-time using a web-based dashboard. This container only included NodeJS version 18.16.0. This container was used for the monitoring system for our system.