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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.<\/jats:p>","DOI":"10.1177\/1088467x251339271","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T01:12:11Z","timestamp":1747012331000},"page":"162-181","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Adaptive marine intelligence and sensing architecture for autonomous underwater ecosystem monitoring using AI and IoT integration"],"prefix":"10.1177","volume":"30","author":[{"given":"M","family":"Ananthi","sequence":"first","affiliation":[{"name":"Department of Computer Science and Business Systems, KGiSL Institute of Technology, Coimbatore, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R","family":"Lakshmana Kumar","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Machine Learning, Tagore Institute of Engineering and Technology, Salem, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"BalaAnand","family":"Muthu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Tagore Institute of Engineering and Technology, Salem, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P","family":"Punitha","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Tagore Institute of Engineering and Technology, Salem, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,5,11]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10499-023-01192-7"},{"key":"e_1_3_2_3_2","first-page":"1975","article-title":"Integration of IoT-enabled technologies and artificial intelligence in diverse domains: recent advancements and future trends","volume":"102","author":"Gouiza N","year":"2024","unstructured":"Gouiza N, Jebari H, Reklaoui K. 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