Architectural Framework for Underwater IoT: Forecasting System for Analyzing Oceanographic Data and Observing the Environment

Abstract

With the passage of time, the exploitation of Internet of Things (IoT) sensors and devices has become more complicated. The Internet of Underwater Things (IoUT) is a subset of the IoT in which underwater sensors are used to continually collect data about ocean ecosystems. Predictive analytics can offer useful insights to the stakeholders associated with environmentalists, marine explorers, and oceanographers for decision-making and intelligence about the ocean, when applied to context-sensitive information, gathered from marine data. This study presents an architectural framework along with algorithms as a realistic solution to design and develop an IoUT system to excel in the data state of the practice. It also includes recommendations and forecasting for potential partners in the smart ocean, which assist in monitoring and environmental protection. A case study is implemented which addresses the solution’s usability and agility to efficiently exploit sensor data, executes the algorithms, and queries the output to assess performance. The number of trails is performed for data insights for the 60-day collection of sensor data. In the context of the smart ocean, the architectural design innovative ideas and viable approaches can be taken into consideration to develop and validate present and next-generation IoUTs and are simplified in this solution.