The demand for ocean exploration and exploitation is rapidly increasing and this has led to rapid growth in the market of mobile vehicles. Given the mobility, the key challenge is to design a highly adaptive solution with minimal signalling (and the associated delays) which current techniques have not fully addressed. Therefore, the mobility and associated challenges in the underwater channel necessitates the design of a new approach to Medium Access Control (MAC) which provides the capability to adapt to rapidly changing conditions with no reliance on signalling which causes delays. This paper proposes the UW-ALOHA-QM protocol, which uses reinforcement learning to allow nodes to adapt to the time varying environment through trial-and-error interaction and thereby improve network resilience and adaptability. Simulations are carried out in four distinct scenarios in which node mobility patterns are significantly different. Simulation results demonstrate that UW-ALOHA-QM provides up to 300% improvement in channel utilisation with respect to existing protocols designed for mobile networks.

ALOHA-Q is a reinforcement learning medium access control protocol which is designed for terrestrial networks. The protocol is simple but is able to increase channel utilization using the learning scheme. Underwater acoustic networks have limited bandwidth which leads to low data rates and the low propagation speed of the networks brings about the significant idle time in the channel use. Therefore, achieving high channel utilization is a major challenge in underwater communications. This paper evaluates performance of the ALOHA-Q protocol for underwater networks and considers asynchronous operation and refining the number of slots to improve channel utilization. Simulation results show that ALOHA-Q can be adapted to achieve 30% better performance than the original protocol in the underwater environment.

Several cameras are mounted on navigation aid buoys and these cameras can be used for accident prevention systems by processing the images captured. The currently existing image processing algorithms were originally designed for accident prevention on land—for example, CCTV (closed-circuit television)—which are performance oriented. However, when it comes to ocean-based images, navigation aids are usually located at sea and the cameras must be battery operated, and consequently, the energy efficiency of image processing is a major concern. Therefore, this paper proposed a novel approach to the detection of images in an ocean environment with a significantly lower computation. The new algorithm clustered pixels to grids and dealt with grids using greyscale rather than the particular color values of each pixel. Simulation-based experiments demonstrated that the grid-based algorithm provided five-times faster image processing in order to detect an object and achieved an up to 2.5 higher detection rate when compared with existing algorithms using ocean images.

A number of accidents at sea are primarily caused by low visibility due to sea fog. Therefore, it is important to estimate sea fog intensity and visibility in real-time in the ocean environment. Visibility meters utilize optical sensors rather than visional information, so that the measured visibility data occasionally includes errors. Moreover, visibility meters have significant costs so that it is not viable to install them at various locations. Therefore, this paper proposes an algorithm called RDCP (Reduced Dark Channel Prior), which provides reliable estimation at a low cost by processing images captured from cameras, since they receive identical information as human eyes. For the estimation, the RDCP algorithm firstly acquires dark channels from an ocean image, then applies an optimized threshold value to the dark channels and crops out the sky region in the ocean image. For the estimation performance evaluation, 320 raw images captured from cameras at four different ports in Republic of Korea are used. Considering facilities in oceans, which are usually operated in power-limited environment, the processing time performance of PDCP is also examined. The experiments demonstrate that the RDCP algorithm provides reliable estimation performance in real-time.

A number of accidents at sea are primarily caused by low visibility due to sea fog therefore, it is important to estimate sea fog intensity and visibility in real-time in the ocean environment. However, the existing estimation systems have significant costs so that it is not viable to install them at various locations, such as small vessels, minor ports, and a number of buoys. Therefore, this paper proposes an algorithm called RDCP (Reduced Dark Channel Prior) which provides the estimation at a low complexity and a low cost. For the estimation, the RDCP algorithm firstly acquires dark channels from an ocean image, then applies an optimized threshold value to the dark channels, and crops out the sky region in the ocean image. For the estimation performance evaluation, 320 raw images captured from cameras at four different ports in South Korea are used rather than artificially composed images. Considering facilities in oceans which are usually operated in the power limited environment, the processing time performance of PDCP are also examined. The experiments demonstrate that the RDCP algorithm provides reliable estimation performance in real-time.