Improved Large-Scale Ocean Wave Dynamics Remote Monitoring Based on Big Data Analytics and Reanalyzed Remote Sensing

Abstract

Oceans and large water bodies have the potential to generate a large amount of green and renewable energy by harvesting the ocean surface properties like wind waves and tidal waves using Wave Energy Converter (WEC) devices. Although the oceans have this potential, very little ocean energy is harvested because of improper planning and implementation challenges. Besides this, monitoring ocean waves is of immense importance as several ocean-related calamities could be prevented. Also, the ocean serves as the maritime transportation route. Therefore, a need exists for remote and continuous monitoring of ocean waves and preparing strategies for different situations. Remote sensing technology could be utilized for a large scale low-cost opportunity for monitoring entire ocean bodies and extracting several important ocean surface features like wave height, wave time period, and drift velocities that can be used to estimate the ideal locations for power generation and find locations for turbulent waters so that maritime transportation hazards could be prevented. To process this large volume of data, Big Data Analytics techniques have been used to distribute the workload to worker nodes, facilitating a fast calculation of the reanalyzed remote sensing data. The experiment was conducted on Indian Coastline. The findings from the experiment show that a total of 1.86 GWh energy can be harvested from the ocean waves of the Indian Coastline, and locations of turbulent waters can be predicted in real-time to optimize maritime transportation routes.

Keywords

• big data analytics, renewable energy, ocean energy, wave energy, remote sensing