COLLABORATIVE EDGE-FOG SYSTEM FOR REAL-TIME ANOMALY DETECTION AND COMMUNICATION EFFICIENCY IN UNDERWATER IOT

Abstract

Background: In the underwater IoT systems, environmental constraints like bandwidth limitations and highlatency raise issues with real-time anomaly detection and communication. Data processing efficiently with timely anomaly detection is an essential step in enhancing system performance in underwater environments.

Objectives: This work proposes the design of a collaborative edge-fog system for underwater IoT networks that enhance the efficiency of real-time anomaly detection, communication, and data processing while optimizing response time and resource management.
Methods: The distributed anomaly detection using edge and fog computing, offloading noncritical tasks in case of fog nodes while still maintaining real-time detection capabilities at the edge, will reduce latency and enhance communication efficiency.
Empirical Results: The framework can reduce detection latency to 25%, enhance communication efficiency up to 30%, and performs better compared to the traditional centralized systems while being assured of a real-time performance in underwater IoT applications.
Conclusion: The collaborative edge-fog system would significantly improve the anomaly detection of underwater IoT systems and communication efficiency. Scalability and energy-efficient solutions are obtained from this design. Future development can be considered in terms of integrating advanced algorithms for anomaly detection and further optimized resource management.