AI-Guided System for Autonomous Deployment of Underwater Sensor Networks
Status: Patent Application Filed
Project Overview
As a principal inventor, I designed and filed a patent for a groundbreaking autonomous system that intelligently deploys and manages underwater surveillance networks. This invention leverages an AI-guided Autonomous Underwater Vehicle (AUV) to create a persistent, adaptive, and secure sensor field for maritime monitoring.
The Blind Spots in Underwater Surveillance
Effective underwater surveillance is a complex engineering challenge. We identified critical flaws in existing methods:
Static Sensor Arrays: Manually deployed sensor grids are incredibly expensive, inflexible, and cannot be adapted to new threats.
AUV Patrols: While mobile, AUVs provide only temporary, "snapshot" surveillance.
Lack of Intelligence: Prior art systems rely on pre-programmed paths and "dumb" sensors.
Our Innovative Solution
Our invention is a holistic, intelligent system that functions as a "network architect," not just a deployment tool.
AI-Driven Adaptive Deployment
The core of our innovation is the AUV's AI Decision Engine.
Reinforcement Learning: We use a reinforcement learning model (e.g., PPO) for real-time analysis.
Intelligent Placement: The AI autonomously identifies strategically valuable locations.
Edge-Computing Smart Sensor Nodes
We designed "smart" sensor nodes that think for themselves.
Onboard AI Accelerators: Each node has a low-power AI accelerator for local processing.
Local Threat Detection: Nodes analyze their own sensor data for genuine threats.
Secure, Self-Healing Underwater Mesh Network
Our system creates a resilient communication fabric.
Decentralized Communication: Nodes form a secure, ad-hoc mesh network.
Resilience and Security: Network can self-heal and ensures secure communications.
Technology Stack & Core Components
Hardware
NVIDIA Jetson Nano for AI processing, Robot Operating System (ROS), sonar/hydrophone suite, and a custom deployment actuator.
Software & AI
Reinforcement Learning (PPO) for deployment decisions, TensorFlow Lite for on-node classification, B.A.T.M.A.N. protocol for mesh networking.