## 3 State-of-the-Art Review
### Specific Techniques and Approaches  
To address the special needs and purposes of marine environment monitoring, a number of WSN-based techniques and approaches have been developed and reported in the literature.  
[PERSON] _et al._[67] presented a multi-modal event monitoring system based on WSNs and visual images for river and coastal marine detection. The system used a visual sensor to complement the use of a WSN in detecting and tracking features of a river or coastal marine location. A software tool was developed to analyze the relationship between the sensor readings and image features. It uses a support vector machine (SVM) approach for training or classification. A Matlab image processing toolbox was used for processing images and extracting various image features including color features, texture features, and edge features.  
[PERSON] _et al._[68] designed a WSN-based water environment monitoring system which can sense and monitor video information in key areas and various water quality parameters, including water temperature, turbidity, pH, dissolved oxygen and electric conductivity. This monitoring system has a data video base station, data monitoring nodes, and a remote monitoring center. This system used an ARM-DSP based double processor, combined ZigBee and CDMA wireless transmission networks, and used a CPLD sampling controller.  
A decentralized _ad-hoc_ wireless sensor network was proposed for ocean pollution detection by [PERSON] _et al._[69]. In order to prolong the network lifetime and to improve its Quality of Service (QoS), they focused on the deployment of sensors, protocol stacks, synchronization and the routing algorithms.  
A WSN-based wave monitoring technology was proposed by [PERSON] _et al._[70] to monitor various wave parameters. This system deployed dense wireless sensor nodes which are equipped with low-cost, low-power, MEMS-based inertial sensors of accelerometers and gyroscopes. They conducted experiments using a Ferris wheel contraption and the results showed an accuracy of approximately 10 cm for a wheel diameter of 100 cm.  
A robotic wireless sensor network was presented by [PERSON] _et al._[71] for monitoring common carp in Minnesota lakes. This project built a small, mobile, lightweight robotic raft which is deployed with searching and tracking algorithms. They conducted several field experiments in various lakes, and experimental results demonstrated that the robotic raft has great potential in environmental monitoring. They envisioned some system improvements including energy saving, localization accuracy, autonomous navigation and multi-raft systems.  
In order to enhance measurement precision and prolong the lifetime of marine environmental monitoring sensors, [PERSON] _et al._[72] analyzed the biofouling effects on marine sensors measurements, proposed some promising techniques for the biofouling protection of _in situ_ sensors.  
To explore the impact of the deep ocean increase in CO\({}_{2}\) levels and resulting pH changes on ocean biogeochemistry and ecology, [PERSON] _et al._[73] studied a Free Ocean CO\({}_{2}\) Enrichment (FOCE) system. The proposed algorithm can achieve the objectives of instrument-in-the-loop control, software reuse of infrastructure and instrument services, and rapid assembly of a scalable end-to-end sensor network system.  
An Android-based WSN application was developed by [PERSON] [74]. This WSN app can be installed and operate on any Android-based smartphone, get synchronized with the centralized database server, and monitor the various nodes of the wireless sensor network.