Monitoring of the marine environment has come to be a field of scientific interest in the last ten years. The instruments used in this work have ranged from small-scale sensor networks to complex observation systems. Among small-scale networks, Wireless Sensor Networks (WSNs) are a highly attractive solution in that they are easy to deploy, operate and dismantle and are relatively inexpensive. The aim of this paper is to identify, appraise, select and synthesize all high quality research evidence relevant to the use of WSNs in oceanographic monitoring. The literature is systematically reviewed to offer an overview of the present state of this field of study and identify the principal resources that have been used to implement networks of this kind. Finally, this article details the challenges and difficulties that have to be overcome if these networks are to be successfully deployed.

R. (2016). A decision support system for managing irrigation in agriculture.

This paper focuses on detecting freezing of gait in Parkinson’s patients using body-worn accelerometers. In this study, we analyzed the robustness of four feature sets, two of which are new features adapted from speech processing: mel frequency cepstral coefficients and quality assessment metrics. For classification based on these features, we compared random forest, multilayer perceptron, hidden Markov models, and deep neural networks. These algorithms were evaluated using a leave-one-subject-out (LOSO) cross validation to match the situation where a system is being constructed for patients for whom there is no training data. This evaluation was performed using the Daphnet dataset, which includes recordings from ten patients using three accelerometers situated on the ankle, knee, and lower back. We obtained a reduction from 17.3% to 12.5% of the equal error rate compared to the previous best results using this dataset and LOSO testing. For high levels of sensitivity (such as 0.95), the specificity increased from 0.63 to 0.75. The biggest improvement across all of the feature sets and algorithms tested in this study was obtained by integrating information from longer periods of time in a deep neural network with convolutional layers.

Monitoring of marine ecosystems is essential to identify the parameters that determine their condition. The data derived from the sensors used to monitor them are a fundamental source for the development of mathematical models with which to predict the behaviour of conditions of the water, the sea bed and the living creatures inhabiting it. This paper is intended to explain and illustrate a design and implementation for a new multisensor monitoring buoy system. The system design is based on a number of fundamental requirements that set it apart from other recent proposals: low cost of implementation, the possibility of application in coastal shallow-water marine environments, suitable dimensions for deployment and stability of the sensor system in a shifting environment like the sea bed, and total autonomy of power supply and data recording. The buoy system has successfully performed remote monitoring of temperature and marine pressure (SBE 39 sensor), temperature (MCP9700 sensor) and atmospheric pressure (YOUNG 61302L sensor). The above requirements have been satisfactorily validated by operational trials in a marine environment. The proposed buoy sensor system thus seems to offer a broad range of applications.

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