IoT and AI for Smart Rural Fishing: System Architecture, TEK Integration, and Economic Viability Analysis
DOI:
https://doi.org/10.56294/dm20261302Keywords:
Internet of Things (IoT), Edge computing, Traditional Ecological Knowledge (TEK), Economic viability, Human-AI collaboration, Smart fisheries, Chinese fishing netsAbstract
Introduction: The integration of IoT and AI technologies presents opportunities for modernizing traditional fishing practices while preserving ecological knowledge.
Objective: This research develops and evaluates an integrated IoT-AI system to enhance traditional Chinese fishing net operations in Kerala backwaters, India, with an emphasis on TEK integration and economic viability assessment.
Method: We deployed an edge-cloud computing architecture integrating 15 environmental sensors, automated winch systems, and cloud-based AI analytics at Chathedam fishing site (9,9674°N, 76,2816°E) over six months (January–June 2025), documenting 1,000 fishing operations and validating 20 TEK rules through statistical analysis.
Results: Nine high-confidence TEK rules (≥0,80) achieved 100 % validation success with 18–47 % catch improvements. AI-guided operations achieved 10 percentage point improvement in profit margin (70,4 % vs 60,4 %) primarily through cost reduction. System investment of $1,720 achieves payback in 1,8 months with 898 % ROI over 18 months.
Conclusions: The validated edge-cloud architecture, TEK-AI integration framework, and demonstrated economic viability provide a replicable model for technology-enabled enhancement of traditional small-scale fisheries.
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