Apart from their ecological value, the world’s oceans are among the planet’s most valuable resources, a rich source of food and wealth and in urgent need of protection. This article describes BUSCAMOS-RobObs, a robot-based observatory, consisting of an autonomous solar-powered marine robot with specialized sensing systems designed to carry out long-term observation missions in the inland sea of the Mar Menor in southeastern Spain. This highly specialised device is unique because it has the capacity to anchor itself to the seabed and become a “buoy”, either to take measurements at specific points or to recharge its batteries. It thus avoids drifting and possible accidents in the buoy mode, especially near the coast, and resumes monitoring tasks when the required energy levels are reached. The robot is equipped with a broad range of sensors, including side scan sonar, sub-bottom sonar, laser systems, ultrasound sonar, depth meters, a multi-parametric probe and a GPS, which can collect georeferenced oceanic data. Although various types of autonomous vehicles have been described in the literature, they all have limited autonomy (even in the long term) as regards operational time and covering the seabed. The article describes a permanent monitoring mission in the Mar Menor, with a combination of solar energy and a decision-making strategy as regards the optimum route to be followed. The energy and mission simulation results, as well as an account of actual monitoring missions are also included.

Autonomous underwater vehicles (AUVs) have increasingly played a key role in monitoring the marine environment, studying its physical-chemical parameters for the supervision of endangered species. AUVs now include a power source and an intelligent control system that allows them to autonomously carry out programmed tasks. Their navigation system is much more challenging than that of land-based applications, due to the lack of connected networks in the marine environment. On the other hand, due to the latest developments in neural networks, particularly deep learning (DL), the visual recognition systems can achieve impressive performance. Computer vision (CV) has especially improved the field of object detection. Although all the developed DL algorithms can be deployed in the cloud, the present cloud computing system is unable to manage and analyze the massive amount of computing power and data. Edge intelligence is expected to replace DL computation in the cloud, providing various distributed, low-latency and reliable intelligent services. This paper proposes an AUV model system designed to overcome latency challenges in the supervision and tracking process by using edge computing in an IoT gateway. The IoT gateway is used to connect the AUV control system to the internet. The proposed model successfully carried out a long-term monitoring mission in a predefined area of shallow water in the Mar Menor (Spain) to track the underwater Pinna nobilis (fan mussel) species. The obtained results clearly justify the proposed system’s design and highlight the cloud and edge architecture performances. They also indicate the need for a hybrid cloud/edge architecture to ensure a real-time control loop for better latency and accuracy to meet the system’s requirements.

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The world’s oceans are one of the most valuable sources of biodiversity and resources on the planet, although there are areas where the marine ecosystem is threatened by human activities. Marine protected areas (MPAs) are distinctive spaces protected by law due to their unique characteristics, such as being the habitat of endangered marine species. Even with this protection, there are still illegal activities such as poaching or anchoring that threaten the survival of different marine species. In this context, we propose an autonomous surface vehicle (ASV) model system for the surveillance of marine areas by detecting and recognizing vessels through artificial intelligence (AI)-based image recognition services, in search of those carrying out illegal activities. Cloud and edge AI computing technologies were used for computer vision. These technologies have proven to be accurate and reliable in detecting shapes and objects for which they have been trained. Azure edge and cloud vision services offer the best option in terms of accuracy for this task. Due to the lack of 4G and 5G coverage in offshore marine environments, it is necessary to use radio links with a coastal base station to ensure communications, which may result in a high response time due to the high latency involved. The analysis of on-board images may not be sufficiently accurate; therefore, we proposed a smart algorithm for autonomy optimization by selecting the proper AI technology according to the current scenario (SAAO) capable of selecting the best AI source for the current scenario in real time, according to the required recognition accuracy or low latency. The SAAO optimizes the execution, efficiency, risk reduction, and results of each stage of the surveillance mission, taking appropriate decisions by selecting either cloud or edge vision models without human intervention.

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