A suitable sampling technology to identify species and to estimate population dynamics based on individual counts at different temporal levels in relation to habitat variations is increasingly important for fishery management and biodiversity studies. In the past two decades, as interest in exploring the oceans for valuable resources and in protecting these resources from overexploitation have grown, the number of cabled (permanent) submarine multiparametric platforms with video stations has increased. Prior to the development of seafloor observatories, the majority of autonomous stations were battery powered and stored data locally. The recently installed low-cost, multiparametric, expandable, cabled coastal Seafloor Observatory (OBSEA), located 4 km off of Vilanova i la Gertrú, Barcelona, at a depth of 20 m, is directly connected to a ground station by a telecommunication cable; thus, it is not affected by the limitations associated with previous observation technologies. OBSEA is part of the European Multidisciplinary Seafloor Observatory (EMSO) infrastructure, and its activities are included among the Network of Excellence of the European Seas Observatory NETwork (ESONET). OBSEA enables remote, long-term, and continuous surveys of the local ecosystem by acquiring synchronous multiparametric habitat data and bio-data with the following sensors: Conductivity-Temperature-Depth (CTD) sensors for salinity, temperature, and pressure; Acoustic Doppler Current Profilers (ADCP) for current speed and direction, including a turbidity meter and a fluorometer (for the determination of chlorophyll concentration); a hydrophone; a seismometer; and finally, a video camera for automated image analysis in relation to species classification and tracking. Images can be monitored in real time, and all data can be stored for future studies. In this article, the various components of OBSEA are described, including its hardware (the sensors and the network of marine and land nodes), software (data acquisition, transmission, processing, and storage), and multiparametric measurement (habitat and bio-data time series) capabilities. A one-month multiparametric survey of habitat parameters was conducted during 2009 and 2010 to demonstrate these functions. An automated video image analysis protocol was also developed for fish counting in the water column, a method that can be used with cabled coastal observatories working with still images. Finally, bio-data time series were coupled with data from other oceanographic sensors to demonstrate the utility of OBSEA in studies of ecosystem dynamics.

A novel Kinetic Energy Harvester (KEH) has been developed for powering oceanic undrogued drifters. It consists on a double pendulum system capable of transforming the wave oscillations into rotation on a flywheel. This rotation is converted into DC current by an electrical generator and further processed by a power management unit (PMU). The PMU includes a "maximum power point tracking" system to maximize energy production by the generator. An oceanic drifter has also been designed to embed the KEH and a custom-made measurement system to perform real sea tests. It counts on an Inertial Measurement Unit to study the motion of the drifter and an embedded measurement system to estimate the rotation speed of the generator and the power at both the input and output of the PMU. A Wi-Fi connection is also included for data transfer at short distances. The generator was firstly characterized at the laboratory; the drifter was then placed on a linear shaker to assess its performance. Finally, the drifter was deployed in a controlled sea area with average values of wave height and frequency of 1.43 m and 0.29 Hz, respectively. In these conditions, the drifter showed horizontal and vertical oscillations with peak-to-peak accelerations of 0.8 g and power spectra centered around 1.5 Hz and 1 Hz, respectively. As a result, the KEH generated a mean output power of 0.18 mW, with peaks of 2.5 mW.

The study of the effects of climate change on the marine environment requires the existence of sufficiently long time series of key parameters. The study of these series allows both to characterize the range of variability in each particular region and to detect trends or changes that could be attributed to anthropogenic causes. For this reason, networks of permanent cabled observation systems are being deployed in the ocean. This paper presents a balance of a decade of activity at the OBSEA cabled observatory, as an example of ocean monitoring success and drawbacks. It is not the objective of this article to analyze the scientific and technical aspects already presented by the authors in different publications (Table 4). We will evaluate the overall experience by retracing the different steps of infrastructure deployment and maintenance, focusing on routines for in situ control, damages experienced, breakdowns and administrative constraints by local administrations. We will conclude by providing a set of guidelines to improve cabled observatories scientific outreach, societal projection, and economic efficiency. As a result of this work, a 10-years dataset has been published in Pangaea that is available for the community. INDEX TERMS Cabled observatories, multidisciplinary observation, coastal ocean monitoring, underwater imaging, european multidisciplinary seafloor and water column observatory (EMSO), JERICO-RI.

Effective ocean and coastal data management are needed to manage marine ecosystem health. Past ocean and coastal data management systems were often very specific to a particular application and region, but this focused approach often lacks real-time data and sharing/interoperating capability. The challenge for the ocean observing community is to devise standards and practices that enable integration of data from sensors across devices, manufacturers, users, and domains to enable new types of applications and services that facilitate much more comprehensive understanding and analyses of marine ecosystem. A given kind of sensor may be deployed on various platforms such as floats, gliders or moorings, and thus must be integrated with different operation, and data management systems. Simplifying the integration process in existing or newly established observing systems would benefit system operators and is important for the broader application of diverse sensors. This paper describes a geospatial "sensor web" architecture developed by the "NeXOS" project for ocean and coastal data management, based on the concepts of spatial data infrastructure and the Sensor Web Enablement framework of the Open Geospatial Consortium. This approach reduces the effort to propagate data from deployed sensors to users. To support the realization of the proposed Next generation Ocean Sensors (NeXOS) architecture, hardware and software specifications for a Smart Electronic Interface for Sensors and Instruments (SEISI) are described. SEISI specifies small lower-power electronics, minimal operating system, and standards-basedresearch software to enable web-based Manuscript

The study of global phenomena requires the combination of a considerable amount of data coming from different sources, acquired by different observation platforms and managed by institutions working in different scientific fields. Merging this data to provide extensive and complete data sets to monitor the long-term, global changes of our oceans is a major challenge. The data acquisition and data archival procedures usually vary significantly depending on the acquisition platform. This lack of standardization ultimately leads to information silos, preventing the data to be effectively shared across different scientific communities. In the past years, important steps have been taken in order to improve both standardization and interoperability, such as the Open Geospatial Consortium’s Sensor Web Enablement (SWE) framework. Within this framework, standardized models and interfaces to archive, access and visualize the data from heterogeneous sensor resources have been proposed. However, due to the wide variety of software and hardware architectures presented by marine sensors and marine observation platforms, there is still a lack of uniform procedures to integrate sensors into existing SWE-based data infrastructures. In this work, a framework aimed to enable sensor plug and play integration into existing SWE-based data infrastructures is presented. First, an analysis of the operations required to automatically identify, configure and operate a sensor are analysed. Then, the metadata required for these operations is structured in a standard way. Afterwards, a modular, plug and play, SWE-based acquisition chain is proposed. Finally different use cases for this framework are presented.