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.INDEX TERMS Medium access control, mobile sensor networks, reinforcement learning, Q-learning, underwater acoustic networks.
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.
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