Adaptive marine intelligence and sensing architecture for autonomous underwater ecosystem monitoring using AI and IoT integration

Abstract

Adaptive Marine Intelligence and Sensing Architecture (AMISA) is a new framework that enables the enhancement of Autonomous Underwater Vehicle (AUV) capabilities with Artificial Intelligence (AI) and the Internet of Things (IoT). In response to the increasing requirement for real-time monitoring of marine ecosystems and sustainable ocean management, the proposed work is built for autonomous prediction, observation, and analysis of marine ecosystems through real-time data acquisition and adaptive decision-making processes. It has some cutting-edge components, such as Predictive Environment Mapping (PEM), which mines both historical and real-time data to adaptively detect and selectively focus on regions that might undergo ecological changes, and Dynamic Sensor Orchestration (DSO) is an energy-saving mechanism that selectively activates sensors in ecologically critical areas. Multi-tier AI Processing (MTAP) introduces an efficient hierarchical model structure for preliminary event detection and high-level anomaly analysis, tailoring data processing to diverse underwater conditions. Here, Energy-Conscious Path Optimization (ECPO) uses reinforcement learning to adaptively manage the route planning of the AUV to conduct optimal energy usage and to cover high-priority areas. The Smart Cloud Connectivity Protocol (SCCP) allows efficient data transmission by prioritizing essential findings and supports real-time alerts. Lastly, the Continuous Adaptive Learning (CAL) module enables the AUV to autonomously evolve by incremental updates of AI models with new data.