Michael Meisinger

Spatiotemporal path planning in strong, dynamic, uncertain currents

This work addresses mission planning for autonomous underwater gliders based on predictions of an uncertain, time-varying current field. Glider submersibles are highly sensitive to prevailing currents so mission planners must account for ocean tides and eddies. Previous work in variable-current path planning assumes that current predictions are perfect, but in practice these forecasts may be inaccurate. Here we evaluate plan fragility using empirical tests on historical ocean forecasts for which followup data is available. We present methods for glider path planning and control in a time-varying current field. A case study scenario in the Southern California Bight uses current predictions drawn from the Regional Ocean Monitoring System (ROMS).

Rapid Systems of Systems Integration - Combining an Architecture-Centric Approach with Enterprise Service Bus Infrastructure

Rapid, yet methodical, systems of systems integration is in high demand. Application areas such as homeland security and disaster response add to the challenge because of a unique set of integration requirements; three examples are: (1) a high demand for flexibility with respect to the configuration and support of business processes to anticipate and cater to changing threat and mitigation scenarios, (2) high agility demands during both development and production to address legacy and emergent capabilities, processes, applications and technologies, (3) wide variety of trust relationships among and across stakeholders and their organizations. In this paper we report on an approach for balancing challenging integration requirements while rapidly delivering a high-quality, value added, integrated system architecture and service-based implementation infrastructure. In particular, we show how the choice of an enterprise service bus as a deployment infrastructure helps discharge many of the obligations induced by the mentioned requirements - if it is combined with an agile, yet systematic approach for architecture discovery and design

Cyberinfrastructure for the US Ocean Observatories Initiative: Enabling interactive observation in the ocean

The Ocean Observatories Initiative (OOI) is an environmental observatory covering a diversity of oceanic environments, ranging from the coastal to the deep ocean. Construction is planned to begin in mid-2010 with deployment phased over five years. The key integrating element of the OOI is a comprehensive cyberinfrastructure whose design is based on loosely coupled distributed services, and whose elements are expected to reside throughout the OOI observatories, from seafloor instruments to deep sea moorings to shore facilities to computing and archiving infrastructure. There are six main components to the design comprising the core capability container, consisting of four elements providing services for users and distributed resources and two infrastructural elements providing core services. The Sensing and Acquisition component provides capabilities to acquire data from and manage distributed seafloor instrument resources, including their interactions with the infrastructure power, communication and time distribution networks. The Data Management component provides capabilities to distribute and archive data, including cataloging, versioning, metadata management, and attribution and association services. The Analysis and Synthesis element provides a wide range of services to users, including control and archival of models, event detection, quality control services and collaboration capabilities to create virtual laboratories and classrooms. The Planning and Prosecution element gives the ability to plan, simulate and execute observation missions using taskable instruments, and turns the OOI into an interactive observatory. The remaining elements are the Common Operating Infrastructure that provides core services to manage distributed, shared resources in a policy-based framework. It includes capabilities for efficient and scalable communication, to manage identity and policy, manage the resource life cycle, and catalog/repository services for observatory resources. The Common Execution Infrastructure provides an elastic computing framework to initiate, manage and store processes that may range from initial operations on data at a shore station to the execution of a complex numerical model on the national computing infrastructure, and on compute clouds.

Interaction-based Runtime Verification for Systems of Systems Integration

Complex distributed systems pose great challenges for quality assurance. Size, complexity and concurrency of these systems often render traditional verification techniques impractical. In particular, this is true for systems integration efforts, where additional challenges arise from the independent evolution of the composed systems. Runtime verification provides a systematic strategy for analytical quality assurance of such systems. Key elements of runtime verification are system models, ways to inject these models into the observed system and a framework for analysing and monitoring the runtime behaviour against the models. The approach we present in this article is based on interaction models. We specify expected system interactions using Message Sequence Charts (MSC), from which we generate distributed runtime monitors for each of the components. We use aspect-oriented programming (AOP) techniques to inject the monitors into the implementation of the components. Thereby, we verify the adherence of the distributed system interactions with the MSC model. The focus of this article is the runtime verification in the systems integration domain; here, Enterprise Service Buses (ESB) have emerged as a powerful infrastructure for integrating complex distributed systems. In the context of an ESB we leverage the Spring AOP framework to inject the runtime monitors. As a result we obtain a comprehensive, tool-supported approach for model-based runtime verification of interactions. We demonstrate our approach using the Central Locking System as running example of an integrated embedded system.

Automated Sensor Network to Advance Ocean Science

Oceanography is evolving from a shipbased expeditionary science to a distributed, observatory-based approach in which scientists continuously interact with instruments in the field. These new capabilities will facilitate the collection of long-term time series while also providing an interactive capability to conduct experiments using data streaming in real time. n nThe U.S. National Science Foundation has funded the Ocean Observatories Initiative (OOI), which over the next 5 years will deploy infrastructure to expand scientists ability to remotely study the ocean. The OOI is deploying infrastructure that spans global, regional, and coastal scales. A global component will address planetary-scale problems using a new network of moored buoys linked to shore via satellite telecommunications. A regional cabled observatory will “wire” a single region in the northeastern Pacific Ocean with a high-speed optical and power grid. The coastal component will expand existing coastal observing assets to study the importance of high-frequency forcing on the coastal environment.

Serving ocean model data on the cloud

The NOAA-led Integrated Ocean Observing System (IOOS) and the NSF-funded Ocean Observatories Initiative Cyberinfrastructure Project (OOI-CI) are collaborating on a prototype data delivery system for numerical model output and other gridded data using cloud computing. The strategy is to take an existing distributed system for delivering gridded data and redeploy on the cloud, making modifications to the system that allow it to harness the scalability of the cloud as well as adding functionality that the scalability affords.

The Uses of Norms

This chapter presents a variety of applications of norms. These applications include governance in sociotechnical systems, data licensing and data collection, understanding software development teams, requirements engineering, assurance, natural resource allocation, wireless grids, autonomous vehicles, serious games, and virtual worlds.

Cyberinfrastructure for the US Ocean Observatories Initiative

The US National Science Foundations Ocean Observatories Initiative (OOI) is an environmental observatory covering a diversity of oceanic environments, ranging from the coastal to the deep ocean. The key integrating element of the OOI is a comprehensive cyberinfrastructure (CI) that is a substantial departure from previous approaches that will have a significant impact on oceanography over the next twenty years. This paper provides a high-level overview of the CI architecture, as well as the integration and deployment strategies that will ensure its success.

OCean Observatories Initiative Scientific Data Model

The Ocean Observatories Initiative (OOI) through its Cyberinfrastructure (CI) Implementing Organization is developing a next generation platform for ocean sciences that will integrate a wide variety of information resources at scales unattainable before in the earth and ocean sciences. We introduce a novel scientific data model that enables distributed, large-scale storage and query of science data. Our model is built on multiple levels of abstraction ranging from domain-specific at the top down to encodings for message-oriented transport and persistence at the base. The key is exposing the properties of scientific feature types separately from the underlying structure of the data, which in turn is separated from their representation. The data representation is further isolated from the serialization and encoding used for transport and persistence. Our model greatly simplifies expressions of provenance and versioning of various data entities. It is robust, scalable and reliable. We implemented it for the first release of the OOI Integrated Observatory Network (ION), with rollout to operations currently underway.

Towards an Autonomous Space In-situ Marine Sensorweb

We describe ongoing efforts to integrate and coordinate space and marine assets to enable autonomous response to dynamic ocean phenomena such as algal blooms, eddies, and currents. Thus far we have focused on the use of remote sensing assets (e.g. satellites) but future plans include expansions to use a range of in-situ sensors such as gliders, autonomous underwater vehicles, and buoys/moorings.