With the rapid development of society and the economy, an increasing number of human activities have gradually destroyed the marine environment. Marine environment monitoring is a vital problem and has increasingly attracted a great deal of research and development attention. During the past decade, various marine environment monitoring systems have been developed. The traditional marine environment monitoring system using an oceanographic research vessel is expensive and time-consuming and has a low resolution both in time and space. Wireless Sensor Networks (WSNs) have recently been considered as potentially promising alternatives for monitoring marine environments since they have a number of advantages such as unmanned operation, easy deployment, real-time monitoring, and relatively low cost. This paper provides a comprehensive review of the state-of-the-art technologies in the field of marine environment monitoring using wireless sensor networks. It first describes application areas, a common architecture of WSN-based oceanographic monitoring systems, a general architecture of an oceanographic sensor node, sensing parameters and sensors, and wireless communication technologies. Then, it presents a detailed review of some related projects, systems, techniques, approaches and algorithms. It also discusses challenges and opportunities in the research, development, and deployment of wireless sensor networks for marine environment monitoring.

Marine environment monitoring has attracted more and more attention due to the growing concern about climate change. During the past couple of decades, advanced information and communication technologies have been applied to the development of various marine environment monitoring systems. Among others, the Internet of Things (IoT) has been playing an important role in this area. This paper presents a review of the application of the Internet of Things in the field of marine environment monitoring. New technologies including advanced Big Data analytics and their applications in this area are briefly reviewed. It also discusses key research challenges and opportunities in this area, including the potential application of IoT and Big Data in marine environment protection.

Considering the requirements of real time and robustness for the segmentation algorithms for mobile robots, an optimal threshold segmentation algorithm based on mathematical morphology was studied. Two improved measures, i.e., introducing an improved combination filter of median and multi-scale, and increasing morphological opening and closing operation, were proposed for the general optimal threshold segmentation algorithm. The experimental results indicated that the algorithm can not only accurately segment road and off-road regions within the unstructured road images that contain three common types of noise, but better overcome the influence of the road shadow, water marks, cracks and damage on the segmentation.

The main factors that affect segmentation of unstructured road images are shadows and water marks on the road surface. Taking advantage of the parallel image processing capability of cellular neural networks, a fast algorithm for road image segmentation based on cellular neural networks was proposed. In the algorithm, gray threshold segmentation, dilation and erosion, and edge detection using CNN are performed successively. Experimental results demonstrated that the algorithm has strong environmental adaptability, which can fast segment structured and unstructured roads. The proposed method can segment the lane area quickly, effectively and robustly, and can eliminate the influence of shadows and water marks on the segmentation of road images.