A Conceptual Framework for Predictive Maintenance of Underwater Sensors Using Named Data Networking and Machine Learning

Underwater Wireless Sensor Networks (UWSNs) are essential for gathering data in diverse marine applications, including oceanographic research, environmental monitoring, and marine resource management. However, maintaining under-water sensors is challenging due to the harsh and inaccessible environment. This paper proposes a novel conceptual framework for predictive maintenance of UWSNs, leveraging the strengths of Named Data Networking (NDN) for data management and machine learning for sensor fault prediction. The framework integrates these technologies to enhance sensor network reliability and lifespan while minimizing maintenance costs. We discuss the design principles, key components, and potential benefits and challenges of this framework, along with a detailed analysis of its potential benefits and challenges. Additionally, we explore specific case studies to illustrate the applicability of the framework to real-world scenarios. This research highlights the potential of integrating NDN and AI for proactive maintenance in UWSNs, paving the way for future implementation and validation in real-world scenarios.