# Internet of Things in Marine Environment Monitoring: A Review
## 4 New Technologies for Marine Environment Monitoring and Protection  
### Data Analysis  
Recent fast development and deployment of IoT technologies in marine environment monitoring created huge amounts of data, while the recent advancement of Big Data analytics facilitated the analysis of these marine environment data.  
As in many other IoT-based data collection systems, dealing with marine environment data also faces some major challenges, particularly the large amount of data and significant bad data. Researchers around the word have been trying to address these challenges. [PERSON] et al. [71] proposed a method to quickly describe the contour of data collected over the Internet of Things (IoT). The distribution of contour lines can be calculated accurately in a short time.  
[PERSON] and [PERSON] [72] proposed an automatic model selection algorithm (AMSA) to determine the best model for a given matchup dataset. It can automatically choose between regression models to estimate the parameter of interest. It also finds out the number and combination of features to be used for obtaining the best model. They used four Machine Learning feature ranking methods and three Machine Learning regression models to estimate oceanic chlorophyll-a in the global and optically complex waters.  
[PERSON] et al. [73] developed a fast fuzzy C-means clustering algorithm to analyze water environment monitoring data of the Three Gorges Reservoir Area. The hard cluster center can be treated as the initial value of the fuzzy cluster center to accelerate the speed of convergence and reduce the number of iterations.  
[PERSON] et al. [74] summarized the challenges in marine environment data management and interpretation caused by the implications of Big Data. They suggested a solution for the management of Big Data, which requires new collaborations between marine practitioners and data scientists with expertise in programming languages and packages like R and Python.  
[PERSON] et al. (2017) proposed a deep learning-based approach in a marine Big Data setting that enables to classify these diverse acoustic sounds not only considering marine mammals signals.  
[PERSON] et al. (2017) presented a support vector regression architecture with smoothness priority for marine sensor data prediction to handle the abruptly fluctuating, multi-noise, non-stationary and abnormal data. The smoother plays the role of preprocessing to handle the outliers and noises in marine sensor data, providing stable initialization values for the next nonlinear approximation based on support vector machines.  
Since the existing current correlation analysis method for ocean monitoring big data is time consuming and stability is poor, [PERSON] et al. (2017) developed a new method by sending the collected data to the cloud storage system. Based on the global and local Moran index calculations, the ocean big data of relatively high correlation were saved to the adjacent data center, which reduces the ocean monitoring data correlation analysis time.  
[PERSON] et al. (2018) developed a low-cost passive acoustic monitoring (PAM) system for nautical citizen science and real-time acoustic augmentation of whale-watching experiences. This paper used machine learning identify vocal acoustic samples of common cetaceans like whales and dolphins with acoustic features (clicks, moans or whistles).  
It is clear that research and development efforts on the application of Big Data analytics in marine environment monitoring have been growing recently.