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AI in the Gibraltar Rainforest

# AI in the Gibraltar Rainforest: Server Configuration

This document details the server configuration powering the "AI in the Gibraltar Rainforest" project, a research initiative utilizing artificial intelligence for ecological monitoring and species identification within the Upper Rock Nature Reserve. This guide is intended for new system administrators and developers contributing to the project.

Project Overview

The "AI in the Gibraltar Rainforest" project employs a network of sensor nodes (described in Sensor Network Deployment) collecting data on environmental conditions, audio recordings, and visual imagery. This data is processed by a centralized server cluster to identify species, detect anomalies, and provide real-time insights into the rainforest ecosystem. The project relies heavily on Machine Learning Models and Data Storage Solutions. The initial phase focuses on bird song identification using Audio Analysis Techniques.

Server Infrastructure

The server infrastructure consists of three primary server roles: Data Ingestion, Processing, and Web Interface. These roles are distributed across a cluster of physical servers housed within a secure, climate-controlled data center at the University of Gibraltar. Redundancy is built in at each layer to ensure high availability. Details on Network Topology are available in a separate document.

Data Ingestion Servers

These servers are responsible for receiving data streams from the sensor network. They perform initial data validation and buffering before passing the data to the processing servers.

Server Name Role Operating System CPU RAM Storage
gib-di-01 Data Ingestion (Primary) Ubuntu Server 22.04 LTS Intel Xeon Silver 4310 (12 cores) 64 GB DDR4 ECC 4 TB NVMe SSD (RAID 1)
gib-di-02 Data Ingestion (Secondary/Failover) Ubuntu Server 22.04 LTS Intel Xeon Silver 4310 (12 cores) 64 GB DDR4 ECC 4 TB NVMe SSD (RAID 1)

The ingestion servers utilize Message Queueing Protocol (MQTT) for receiving data from the sensors. Security Protocols are implemented to ensure data integrity and prevent unauthorized access.

Processing Servers

These servers perform the core AI processing tasks, including data analysis, model training, and species identification. They leverage GPU acceleration for faster processing.

Server Name Role Operating System CPU RAM GPU Storage
gib-ps-01 AI Processing (Primary) Ubuntu Server 22.04 LTS Intel Xeon Gold 6338 (32 cores) 128 GB DDR4 ECC NVIDIA RTX A6000 (48 GB VRAM) 8 TB NVMe SSD (RAID 10)
gib-ps-02 AI Processing (Secondary) Ubuntu Server 22.04 LTS Intel Xeon Gold 6338 (32 cores) 128 GB DDR4 ECC NVIDIA RTX A6000 (48 GB VRAM) 8 TB NVMe SSD (RAID 10)

These servers utilize Python Libraries such as TensorFlow and PyTorch. Model Version Control is crucial for managing and deploying updated AI models.

Web Interface Server

This server hosts the web interface for visualizing data and interacting with the AI system. It provides a user-friendly dashboard for researchers and stakeholders.

Server Name Role Operating System CPU RAM Web Server Storage
gib-wi-01 Web Interface Ubuntu Server 22.04 LTS Intel Xeon E-2336 (8 cores) 32 GB DDR4 ECC Nginx 2 TB SSD

The web interface is developed using Web Development Frameworks and utilizes a RESTful API to communicate with the processing servers. User Authentication is implemented to control access to sensitive data.

Software Stack

The following software components are essential to the server configuration:

⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️

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