Smart Query Answering for Marine Sensor Data

We review existing query answering systems for sensor data. We then propose an extended query answering approach termed smart query, specifically for marine sensor data. The smart query answering system integrates pattern queries and continuous queries. The proposed smart query system considers both streaming data and historical data from marine sensor networks. The smart query also uses query relaxation technique and semantics from domain knowledge as a recommender system. The proposed smart query benefits in building data and information systems for marine sensor networks

IEEE 1451 http server implementation for marine data

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Adaptive sampling in autonomous marine sensor networks

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2006In this thesis, an innovative architecture for real-time adaptive and cooperative control of autonomous sensor platforms in a marine sensor network is described in the context of the autonomous oceanographic network scenario. This architecture has three major components, an intelligent, logical sensor that provides high-level environmental state information to a behavior-based autonomous vehicle control system, a new approach to behavior-based control of autonomous vehicles using multiple objective functions that allows reactive control in complex environments with multiple constraints, and an approach to cooperative robotics that is a hybrid between the swarm cooperation and intentional cooperation approaches. The mobility of the sensor platforms is a key advantage of this strategy, allowing dynamic optimization of the sensor locations with respect to the classification or localization of a process of interest including processes which can be time varying, not spatially isotropic and for which action is required in real-time. Experimental results are presented for a 2-D target tracking application in which fully autonomous surface craft using simulated bearing sensors acquire and track a moving target in open water. In the first example, a single sensor vehicle adaptively tracks a target while simultaneously relaying the estimated track to a second vehicle acting as a classification platform. In the second example, two spatially distributed sensor vehicles adaptively track a moving target by fusing their sensor information to form a single target track estimate. In both cases the goal is to adapt the platform motion to minimize the uncertainty of the target track parameter estimates. The link between the sensor platform motion and the target track estimate uncertainty is fully derived and this information is used to develop the behaviors for the sensor platform control system. The experimental results clearly illustrate the significant processing gain that spatially distributed sensors can achieve over a single sensor when observing a dynamic phenomenon as well as the viability of behavior-based control for dealing with uncertainty in complex situations in marine sensor networks.Supported by the Office of Naval Research, with a 3-year National Defense Science and Engineering Grant Fellowship and research assistantships through the Generic Ocean Array Technology Sonar (GOATS) project, contract N00014-97-1-0202 and contract N00014-05-G-0106 Delivery Order 008, PLUSNET: Persistent Littoral Undersea Surveillance Network

Do-it-yourself instruments and data processing methods for developing marine citizen observatories

Water is the most important resource for living on planet Earth, covering more than 70% of its surface. The oceans represent more than 97% of the planet total water and they are where more than the 99.5% of the living beings are concentrated. A great number of ecosystems depend on the health of these oceans; their study and protection are necessary. Large datasets over long periods of time and over wide geographical areas can be required to assess the health of aquatic ecosystems. The funding needed for data collection is considerable and limited, so it is important to look at new cost-effective ways of obtaining and processing marine environmental data. The feasible solution at present is to develop observational infrastructures that may increase significantly the conventional sampling capabilities. In this study we promote to achieve this solution with the implementation of Citizen Observatories, based on volunteer participation. Citizen observatories are platforms that integrate the latest information technologies to digitally connect citizens, improving observation skills for developing a new type of research known as Citizen Science. Citizen science has the potential to increase the knowledge of the environment, and aquatic ecosystems in particular, through the use of people with no specific scientific training to collect and analyze large data sets. We believe that citizen science based tools -open source software coupled with low-cost do-it-yourself hardware- can help to close the gap between science and citizens in the oceanographic field. As the public is actively engaged in the analysis of data, the research also provides a strong avenue for public education. This is the objective of this thesis, to demonstrate how open source software and low-cost do-it-yourself hardware are effectively applied to oceanographic research and how can it develop into citizen science. We analyze four different scenarios where this idea is demonstrated: an example of using open source software for video analysis where lobsters were monitored; a demonstration of using similar video processing techniques on in-situ low-cost do-it-yourself hardware for submarine fauna monitoring; a study using open source machine learning software as a method to improve biological observations; and last but not least, some preliminar results, as proof of concept, of how manual water sampling could be replaced by low-cost do-it-yourself hardware with optical sensors.L’aigua és el recurs més important per la vida al planeta Terra, cobrint més del 70% de la seva superfície. Els oceans representen més del 70% de tota l'aigua del planeta, i és on estan concentrats més del 99.5% dels éssers vius. Un gran nombre d'ecosistemes depenen de la salut d'aquests oceans; el seu estudi i protecció són necessaris. Grans conjunts de dades durant llargs períodes de temps i al llarg d’amples àrees geogràfiques poden ser necessaris per avaluar la salut dels ecosistemes aquàtics. El finançament necessari per aquesta recol·lecció de dades és considerable però limitat, i per tant és important trobar noves formes més rendibles d’obtenir i processar dades mediambientals marines. La solució factible actualment és la de desenvolupar infraestructures observacionals que puguin incrementar significativament les capacitats de mostreig convencionals. En aquest estudi promovem que es pot assolir aquesta solució amb la implementació d’Observatoris Ciutadans, basats en la participació de voluntaris. Els observatoris ciutadans són plataformes que integren les últimes tecnologies de la informació amb ciutadans digitalment connectats, millorant les capacitats d’observació, per desenvolupar un nou tipus de recerca coneguda com a Ciència Ciutadana. La ciència ciutadana té el potencial d’incrementar el coneixement del medi ambient, i dels ecosistemes aquàtics en particular, mitjançant l'ús de persones sense coneixement científic específic per recollir i analitzar grans conjunts de dades. Creiem que les eines basades en ciència ciutadana -programari lliure juntament amb maquinari de baix cost i del tipus "fes-ho tu mateix" (do-it-yourself en anglès)- poden ajudar a apropar la ciència del camp oceanogràfic als ciutadans. A mesura que el gran públic participa activament en l'anàlisi de dades, la recerca esdevé també una nova via d’educació pública. Aquest és l’objectiu d’aquesta tesis, demostrar com el programari lliure i el maquinari de baix cost "fes-ho tu mateix" s’apliquen de forma efectiva a la recerca oceanogràfica i com pot desenvolupar-se cap a ciència ciutadana. Analitzem quatre escenaris diferents on es demostra aquesta idea: un exemple d’ús de programari lliure per anàlisi de vídeos de monitoratge de llagostes; una demostració utilitzant tècniques similars de processat de vídeo en un dispositiu in-situ de baix cost "fes-ho tu mateix" per monitoratge de fauna submarina; un estudi utilitzant programari lliure d’aprenentatge automàtic (machine learning en anglès) com a mètode per millorar observacions biològiques; i finalment uns resultats preliminars, com a prova de la seva viabilitat, de com un mostreig manual de mostres d’aigua podria ser reemplaçat per maquinari de baix cost "fes-ho tu mateix" amb sensors òptics

Introducing distributed dynamic data-intensive (D3) science: Understanding applications and infrastructure

A common feature across many science and engineering applications is the amount and diversity of data and computation that must be integrated to yield insights. Data sets are growing larger and becoming distributed; and their location, availability and properties are often time-dependent. Collectively, these characteristics give rise to dynamic distributed data-intensive applications. While "static" data applications have received significant attention, the characteristics, requirements, and software systems for the analysis of large volumes of dynamic, distributed data, and data-intensive applications have received relatively less attention. This paper surveys several representative dynamic distributed data-intensive application scenarios, provides a common conceptual framework to understand them, and examines the infrastructure used in support of applications.Comment: 38 pages, 2 figure

Process Development: Methodology and Implementation Strategy

This white paper investigates methods to conduct effective and efficient process development within the context of the NSF funded construction of the Ocean Observatory Facility. The basis of this process development investigation was the CGSN Sensor Life Cycle project. During the course of this project the sensor life process was developed with CGSN project team members and sensor manufacturers. Initial process collaboration meetings were also conducted across the OOI organization with the Regional Scale Node (RSN) and CyberInfrastructure (CI) implementing organizations. The CGSN Sensor Life Cycle project provided valuable insights into collaborative process development. The input and feedback from CGSN, RSN, and CI captured during the collaborative process development are reflected throughout this white paper. Process development is defined as creating and implementing business processes--the steps and procedures that govern how resources are used to create products and services that meet the needs of particular customers.1 Process development is part of an extensive body of knowledge often referred to as business process management (BPM). Process development is not new; and there are many existing methodologies. 2-8 This white paper focuses on process development within the context of the Ocean Observatories Initiative (OOI) program. OOI process development faces the same challenges as any large organization: Complex cross functional processes, individual contribution vs. standardization, resistance to changing existing processes, organizational silos of activity, reliance on complex technology, remote locations and virtual teams, scarce project resources, and a diverse customer base. This white paper provides a practical guide and methodology that addresses these challenges. This “how to” guide for process development is comprised of three main components: 1) The Process Development Template, a series of steps for developing processes; 2) the Process Development Implementation Steps, a series of steps for managing process development within an organization; and 3) Step-by-step examples created during the Sensor Life Cycle project that illustrate process development deliverables. An integral part of process development is leveraging existing best practices. During the CGSN Sensor Life Cycle project, best practice organizations and individuals were identified, contacted, and engaged. The CGSN process project team continues to nurture these relationships, and collect and analyze information. The source documents have been placed in the Confluence project repository under CGSN Process Development (pending). The OOI Implementing Organizations (IOs) operations and maintenance strategies refer to a Telecom Operations Map (TOM).9-11 The TOM, a best practices publication developed by the TeleManagement Forum, is a reference framework for categorizing processes. This white paper discusses how the TOM, the Process Development Template (PDT), and the Process Development Implementation Steps (PDIS) complement each other. Together they provide a complete process development methodology. Process development may cross Implementing Organization (IO) boundaries or be contained within an IO. The scale of process development projects range from a simple process involving a few resources, to a complex process executed over many months. This practical process development methodology accommodates different levels of effort, efficiently and effectively utilizes project resources, and rapidly develops sustainable processes. This white paper may be seen as a “living training document” that can be applied, improved and updated as the OOI process development methodology continues to evolve. The organization of the white paper is as follows. Section 1 provides a general process development methodology overview, and demonstrates the complimentary aspects of the TOM, PDT and PDIS. Section 2 introduces the PDT and describes it in detail. Section 3 provides process development implementation steps that greatly increase the quality, speed, and collaborative nature of process development. Section 4 provides examples of PDT project deliverables, providing insights into understanding the application of the PDT.Keywords: Process, Process Development, Ocean Observatories Initiative, Process Methodology, OO

Energy harvesting for marine based sensors

This work examines powering marine based sensors (MBSs) by harvesting energy from their local environment. MBSs intrinsically operate in remote locations, traditionally requiring expensive maintenance expeditions for battery replacement and data download. Nowadays, modern wireless communication allows real-time data access, but adds a significant energy drain, necessitating frequent battery replacement. Harvesting renewable energy to recharge the MBSs battery, introduces the possibility of autonomous MBS operation, reducing maintenance costs and increasing their applicability. The thesis seeks to answer if an unobtrusive energy harvesting device can be incorporated into the MBS deployment to generate 1 Watt of average power. Two candidate renewable energy resources are identified for investigation, ocean waves and the thermal gradient across the air/water interface. Wave energy conversion has drawn considerable research in recent years, due to the large consistent energy flux of ocean waves compared to other conventional energy sources such as solar or wind, but focussing on large scale systems permanently deployed at sites targeted for their favourable wave climates. Although a small amount of research exists on using wave energy for distributed power generation, the device sizes and power outputs of these systems are still one to two orders of magnitude larger than that targeted in this thesis. The present work aims for an unobtrusive device that is easily deployable/retrievable with a mass less than 50kg and which can function at any deployment location regardless of the local wave climate. Additionally, this research differs from previous work, by also seeking to minimise the wave induced pitch motion of the MBS buoy, which negatively affects the data transmission of the MBS due to tilting and misalignment of the RF antenna. Thermal energy harvesting has previously been investigated for terrestrial based sensors, utilising the temperature difference between the soil and ambient air. In this thesis, the temperature difference between the water and ambient air is utilised, to present the first investigation of this thermal energy harvesting concept in the marine environment. A prototype wave energy converter (WEC) was proposed, consisting of a heaving cylindrical buoy with an internal permanent magnet linear generator. A mathematical model of the prototype WEC is derived by coupling a hydrodynamic model for the motion of the buoy with a vibration energy harvester model for the generator. The wave energy resource is assessed, using established mathematical descriptions of ocean wave spectra and by analysing measured wave data from the coast of Queensland, resulting in characteristic wave spectra that are input to the mathematical model of the WEC. The parameters of the WEC system are optimised, to maximise the power output while minimising the pitch motion. A prototype thermal energy harvesting device is proposed, consisting of a thermoelectric device sandwiched between airside and waterside heat exchangers. A mathematical model is derived to assess the power output of the thermal energy harvester using different environmental datasets as input. A physical prototype is built and a number of experiments performed to assess its performance. The results indicate that the prototype WEC should target the high frequency tail of ocean wave spectra, diverging from traditional philosophy of larger scale WECs which target the peak frequency of the input wave spectrum. The analysis showed that the prototype WEC was unable to provide the required power output whilst remaining below 100kg and obeying a 40 degrees pitch angle constraint to ensure robust data transmission. However, a proposed modification to the WECs cylindrical geometry, to improve its hydrodynamic coupling to the input waves, was shown to enable the WEC to provide the required 1W output power whilst obeying the pitch constraints and having a mass below 50kg. The thermal energy harvester results reveal that the thermal gradient across the air/water interface alone is not a suitable energy resource, requiring a device with a cross-sectional area in excess of 100m² to power a MBS. However, including a solar thermal energy collector to increase the airside temperature, greatly improves the performance and enables a thermal energy harvester with a cross-sectional area on the order of 1m² to provide 1W of output power. The findings in this thesis suggest that a well hydrodynamically designed buoy can provide two major benefits for a MBS deployment: enabling efficient wave energy absorption by the MBS buoy, and minimising the wave induced pitch motion which negatively affects the data transmission