AI in the Montserrat Rainforest
# AI in the Montserrat Rainforest: Server Configuration
This article details the server configuration supporting the "AI in the Montserrat Rainforest" project. This project utilizes artificial intelligence to analyze data collected from remote sensors deployed throughout the Montserrat rainforest, focusing on biodiversity monitoring and early detection of environmental changes. This guide is intended for newcomers to the MediaWiki site and provides a technical overview of the hardware and software infrastructure.
Project Overview
The "AI in the Montserrat Rainforest" project aims to create a real-time monitoring system using a network of sensors collecting data on temperature, humidity, soundscapes (for animal identification), and images (for plant species recognition). This data is transmitted to a central server for processing by machine learning algorithms. The processed data is then visualized on a web interface for researchers and conservationists. See also Data Acquisition Process and Machine Learning Models Used.
Server Hardware
The central server is a dedicated machine located in a secure, climate-controlled facility. It is built for high throughput and reliability. Below are the detailed specifications:
| Component | Specification |
|---|---|
| CPU | Dual Intel Xeon Gold 6248R (24 cores/48 threads per CPU) |
| RAM | 256GB DDR4 ECC Registered RAM @ 3200MHz |
| Storage (OS) | 1TB NVMe SSD (Samsung 980 Pro) |
| Storage (Data) | 16TB RAID 6 Array (Seagate Exos X16) |
| Network Interface | Dual 10 Gigabit Ethernet |
| Power Supply | Redundant 1600W Platinum Power Supplies |
This hardware configuration provides ample processing power, memory, and storage to handle the demanding workload of the AI algorithms and the large volume of data generated by the sensors. Refer to Server Room Security Protocol for security details.
Software Stack
The server runs a Linux-based operating system and utilizes a variety of software tools for data processing, machine learning, and web serving.
Operating System
- Operating System: Ubuntu Server 22.04 LTS
- Kernel Version: 5.15.0-76-generic
- Database: PostgreSQL 14
- Database Extensions: PostGIS (for geospatial data), TimescaleDB (for time-series data)
- Python 3.10
- TensorFlow 2.12
- PyTorch 2.0
- NumPy
- Pandas
- Scikit-learn
- Web Server: Nginx 1.23
- Application Framework: Flask (Python)
- Monitoring: Prometheus and Grafana. See Monitoring Dashboard Configuration for details.
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Database
Programming Languages & Libraries
Web Server
Monitoring System
Network Configuration
The server is connected to the internet via a dedicated 10 Gigabit Ethernet connection. A firewall is implemented to protect the server from unauthorized access. Detailed network diagrams can be found at Network Topology Diagram.
| Parameter | Value |
|---|---|
| IP Address | 192.168.1.100 |
| Subnet Mask | 255.255.255.0 |
| Gateway | 192.168.1.1 |
| DNS Servers | 8.8.8.8, 8.8.4.4 |
| Firewall | UFW (Uncomplicated Firewall) |
The firewall rules are configured to allow only necessary traffic, such as SSH (port 22), HTTP (port 80), and HTTPS (port 443). See Firewall Rule Set for a complete list of configured rules.
Data Flow
The following table outlines the data flow within the system:
| Step | Description |
|---|---|
| 1. Data Acquisition | Sensors collect environmental data (temperature, humidity, sound, images). |
| 2. Data Transmission | Data is transmitted wirelessly (LoRaWAN) to a gateway. |
| 3. Data Reception | Gateway forwards data to the central server. |
| 4. Data Storage | Data is stored in the PostgreSQL database. |
| 5. Data Processing | Machine learning algorithms analyze the data. |
| 6. Data Visualization | Processed data is displayed on the web interface. |
This ensures a streamlined process from data collection to actionable insights. For more details, consult Data Pipeline Architecture.
Future Considerations
Future upgrades may include the addition of a GPU for accelerated machine learning and an increase in storage capacity to accommodate growing data volumes. We are also exploring the use of containerization technologies (Docker, Kubernetes) for improved scalability and deployment efficiency. See Scalability Roadmap for details. Further investigation into Edge Computing possibilities is also planned.
Server Maintenance Schedule details routine maintenance procedures.
Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️