AI in the Northern Mariana Islands Rainforest
AI in the Northern Mariana Islands Rainforest: Server Configuration
This article details the server configuration deployed to support Artificial Intelligence (AI) research within the rainforest ecosystem of the Northern Mariana Islands. This deployment focuses on real-time data analysis from a network of remote sensors and cameras, requiring a robust and scalable server infrastructure. This guide is intended for new contributors to the wiki and those looking to understand the system architecture.
Overview
The core objective is to process sensor data (temperature, humidity, acoustic readings, visual data) to identify and track species, monitor environmental changes, and detect potential threats like invasive species or wildfires. The server infrastructure is designed for high availability and utilizes a distributed architecture to handle the large data volumes generated. We utilize a combination of on-site and cloud-based resources to optimize performance and redundancy. This involves data pre-processing locally before transmitting summarized data to a central cloud server farm for advanced analysis. This system builds upon previous work documented in Rainforest Monitoring Systems and Sensor Network Deployment.
Hardware Specifications
The on-site server cluster consists of three primary nodes, each with identical specifications for redundancy. These nodes are housed in a hardened, environmentally-controlled shelter to protect against the humid rainforest conditions.
| Component | Specification |
|---|---|
| CPU | Intel Xeon Gold 6248R (24 cores, 3.0 GHz) |
| RAM | 256 GB DDR4 ECC Registered (3200 MHz) |
| Storage (OS & Applications) | 2 x 1 TB NVMe PCIe Gen4 SSD (RAID 1) |
| Storage (Data Buffer) | 8 x 8 TB SAS HDD (RAID 6) |
| Network Interface | Dual 10 Gigabit Ethernet |
| Power Supply | Redundant 1600W Platinum PSU |
The cloud-based component utilizes virtual machines hosted on a dedicated cluster within Amazon Web Services.
Software Stack
The software stack is built around open-source technologies to minimize costs and maximize flexibility. We leverage Linux (Ubuntu Server 22.04) as the operating system for both on-site and cloud servers.
- On-Site Servers:
* Operating System: Ubuntu Server 22.04 LTS * Data Acquisition: Node-RED for sensor data ingestion and pre-processing. * Database: PostgreSQL with PostGIS extension for geospatial data management. * Message Queue: RabbitMQ for asynchronous communication between components. * Data Compression: zstd for efficient data storage and transmission.
- Cloud Servers:
* Operating System: Amazon Linux 2 * AI Framework: TensorFlow and PyTorch for model training and inference. * Data Storage: Amazon S3 for long-term data archival. * Data Analytics: Apache Spark for large-scale data processing. * API Gateway: Amazon API Gateway for secure access to AI models. * Monitoring: Prometheus and Grafana for system monitoring and alerting.
Network Configuration
The on-site network is a dedicated VLAN with a 10 Gigabit Ethernet backbone. Connectivity to the cloud is established via a high-bandwidth, low-latency internet connection provided by Marianas Cable Corporation. A VPN tunnel secures the communication between the on-site servers and the cloud infrastructure. Firewall rules are configured using iptables to restrict access to essential services. Detailed network diagrams can be found in Network Topology Documentation.
| Network Segment | IP Range | Subnet Mask | Gateway |
|---|---|---|---|
| On-Site Servers | 192.168.1.0/24 | 255.255.255.0 | 192.168.1.1 |
| Sensor Network | 192.168.2.0/24 | 255.255.255.0 | 192.168.2.1 |
| Management Network | 192.168.3.0/24 | 255.255.255.0 | 192.168.3.1 |
Data Flow
The data flow follows a multi-stage process:
1. Sensor Data Acquisition: Sensors collect data and transmit it to the on-site servers via LoRaWAN. 2. Pre-processing: Node-RED processes the data, filtering noise and performing initial analysis. 3. Database Storage: Processed data is stored in the PostgreSQL database. 4. Data Transmission: Summarized data is transmitted to the cloud via the VPN tunnel. 5. AI Analysis: TensorFlow and PyTorch models analyze the data to identify species, detect anomalies, and predict future trends. 6. Visualization & Reporting: Results are visualized using Grafana and presented through a web interface accessible via Web Application Access.
Security Considerations
Security is paramount, given the sensitive nature of the data and the remote location. Key security measures include:
- Firewall Rules: Strict firewall rules are enforced to limit network access.
- VPN Tunnel: A secure VPN tunnel encrypts all communication between the on-site servers and the cloud.
- Access Control: Role-based access control (RBAC) is implemented to restrict access to sensitive data and systems.
- Regular Security Audits: Regular security audits are conducted to identify and address vulnerabilities. See Security Audit Reports.
- Data Encryption: Data is encrypted both in transit and at rest.
| Security Measure | Description | Implementation |
|---|---|---|
| Firewall | Restricts network access | iptables |
| VPN | Encrypts communication | OpenVPN |
| RBAC | Controls access to resources | Linux user groups and permissions |
| Encryption | Protects data confidentiality | TLS/SSL for transit, AES-256 for at rest |
Future Enhancements
Planned enhancements include:
- Implementing edge computing capabilities to reduce latency and bandwidth usage. Details are available in Edge Computing Strategy.
- Integrating machine learning models for real-time species identification using camera feeds.
- Developing a mobile application for field researchers to access data and insights.
- Expanding the sensor network to cover a wider area of the rainforest.
Main Page Rainforest Ecology Data Analysis Techniques Sensor Calibration Procedures System Monitoring Tools Troubleshooting Guide Software Updates Hardware Maintenance Power Management Data Backup Strategy Disaster Recovery Plan Security Protocols Network Administration API Documentation Cloud Services Overview Onsite Server Maintenance Remote Access Configuration Documentation Index
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.* ⚠️