Understanding the ocean environment is possible through the development of underwater acoustic sensor networks. These networks can monitor and gather vital information that has been previously unattainable and consist of a variety of sensors, including pressure and sonar sensors that are utilized over several fields of application.
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However, due to the extensive challenges in the underwater environment, there are constraints when using the network. Improvements to the network have been researched to implement modified algorithms and protocols to ensure successful and accurate data acquisition.
Exploring the Ocean
Ocean exploration technology has increasingly been developed in recent years. As the ocean takes up roughly 71% total landmass of the earth, there is significant value and importance in exploring underwater. Knowledge of the ocean can determine better practices, such as detecting underwater oil fields or determining routes for sea cables.
Several fields of application can utilize this rapidly expanding technology, for example, gathering scientific data, early detection warning systems, ecosystem monitoring, navigation, national safety, and defense and detecting valuable minerals. However, ocean exploration does contribute to water pollution due to the rise of various countries utilizing the ocean unsustainably.
Underwater Acoustic Sensor Networks Development
One technology used in ocean exploration is an underwater acoustic sensor network (UASN) consisting of a varied number of sensors placed over a specific area.
There are various types of sensors used in this network which are sometimes inexpensive and easy to use. The inexpensive sensors include pressure sensors that approximate the depths of a specific area and can also measure pressure along fault lines of tectonic plates.
Thermistors and photo-diodes measure the temperature and ambient light to help understand how the ocean interacts with the atmosphere and determine water depths.
More expensive specialized sensors such as fluorometers approximate the concentration of chlorophyll. A high level indicates the distribution of phytoplankton which helps to predict and track Algae blooms. Other devices can measure turbidity or CO2 concentration. Sonar sensors detect the movement of objects underwater and generally are utilized in modern naval warfare and particularly submarines are dependent on sonar technology.
The traditional oceanography and biology sensors depend on the samples being taken from the environment and transferred to a laboratory for analysis, which makes standard sensors very expensive.
The entire network is acoustically connected through sensors located on the seafloor, autonomous underwater vehicles, and a surface station. The individual components are linked to a control center located onshore, and several collaborative monitoring tasks are performed. For the network to achieve this goal, the sensors have to self-organize autonomously and constantly adapt to the characteristics of the ocean environment.
Challenges in the Underwater Environment
While ground-based sensor networks are static, underwater sensor networks are not and are rather free-moving due to aspects of the underwater environment. For this reason, there are several challenges associated with this technology.
UASN has no real-time monitoring, as the recorded data can only be obtained after the sensors are recovered. The process of recovery may occur months after initial monitoring has started.
Another challenge is that the bandwidth underwater is limited, and there are temporary losses in connectivity as the radio frequency is hugely variable.
The UASN system is composed of two different types of sensors or nodes; anchor or beacon nodes know their location, and the data can either be obtained via GPS or its own memory, whereas ordinary or unknown nodes do not. This causes further challenges in the localization of the node. Data that is meaningful requires the location of the node to be known.
Localization Research with Comprehensive Sensing Technology
The research by Wang, S., et al. (2019) further developed on the localization of the entire network. The focus of the piece was to determine the location of all the nodes in the network through a small number of anchor nodes.
Where other localization developments purely focus on localization accuracy of already developed UASN, this piece also considered power consumption, data processing energy, and cost. At present, the most practical and viable applications still use range-based algorithms, which have high requirements for power consumption and cost.
The study, however, applied the method of comprehensive sensing in conjunction with the hop correction algorithm and found localization to be significantly improved compared with the traditional range-free algorithm.
Further studies are likely to focus on improving the algorithm for more accurate results and consider the use of mobile node localization of UASNs.
Prediction Routing for Freely Floating Networks
When comparing the underwater environment to the ground environment, the most significant difference is the mobility aspect of the network. Freely moving sensors in the aquatic environment makes it difficult for a successful data forwarding process.
Research has proposed a mobility prediction optimal data forwarding (MPODF) protocol for UASN’s as most other protocols neglect the dynamic topology of the environment. The study presents the MPODF based on mobility prediction.
The results of simulations found this particular protocol achieves high energy efficiency and a reduction in data delay. However, the MPODF performance still has to be tested in various real environments to be thoroughly evaluated.
The designing and development of UASN technology should constantly be evolving and adapting to reflect the knowledge gained from ocean exploration. The aim is to develop UASN technology to increase the performance and quality of data. However, developments are in the early stages and will require further advancements in the future.
Continue reading: The Role of Portable Sensors in Aquatic Nutrient Analysis.
References and Further Reading
Alqahtani, G. and Bouabdallah, F. (2021) Energy-Efficient Mobility Prediction Routing Protocol for Freely Floating Underwater Acoustic Sensor Networks, Frontiers in Communications and Networks, 2. Available at: https://www.frontiersin.org/articles/10.3389/frcmn.2021.692002/full
Wang, S. et al. (2019) Underwater Acoustic Sensor Networks Node Localization Based on Compressive Sensing in Water Hydrology, Sensors, 19(20), p. 4552. Available at: https://doi.org/10.3390/s19204552
Pan, Y., Diamant, R. and Liu, J., (2016) Underwater acoustic sensor networks. International Journal of Distributed Sensor Networks, 12(8), p.155014771666549. Available at: https://doi.org/10.1177%2F1550147716665499
Theo, S. (2019)Sensors For Deep-Sea Applications - Electronics For You [online] Available at: https://www.electronicsforu.com/technology-trends/tech-focus/sensors-deep-sea-applications
Heidemann, J., Stojanovic, M. and Zorzi, M., 2012. Underwater sensor networks: applications, advances and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 370(1958), pp.158-175. Available at: https://doi.org/10.1098/rsta.2011.0214