Tony Fountain

The Ring Buffer Network Bus (RBNB) DataTurbine Streaming Data Middleware for Environmental Observing Systems

The environmental science and engineering communities are actively engaged in planning and developing the next generation of large-scale sensor-based observing systems. These systems face two significant challenges: heterogeneity of instrumentation and complexity of data stream processing. Environmental observing systems incorporate instruments across the spectrum of complexity, from temperature sensors to acoustic Doppler current profilers, to streaming video cameras. Managing these instruments and their data streams is a serious challenge. Critical infrastructure requirements common to all of these sensor-based observing systems are reliable data transport, the promotion of sensors and sensor streams to first-class objects, a framework for the integration of heterogeneous instruments, and a comprehensive suite of services for data management, routing, synchronization, monitoring, and visualization. In this paper we present the RBNB DataTurbine, an open-source streaming data middleware system, and discuss how the RBNB DataTurbine satisfies the critical cyberinfrastructure requirements core to these sensor-based observing systems. The discussion includes the results from real-world deployments.

Real-time nondestructive structural health monitoring using support vector machines and wavelets

We present an alternative to visual inspection for detecting damage to civil infrastructure. We describe a real-time decision support system for nondestructive health monitoring. The system is instrumented by an integrated network of wireless sensors mounted on civil infrastructures such as bridges, highways, and commercial and industrial facilities. To address scalability and power consumption issues related to sensor networks, we propose a three-tier system that uses wavelets to adaptively reduce the streaming data spatially and temporally. At the sensor level, measurement data is temporally compressed before being sent upstream to intermediate communication nodes. There, correlated data from multiple sensors is combined and sent to the operation center for further reduction and interpretation. At each level, the compression ratio can be adaptively changed via wavelets. This multi-resolution approach is useful in optimizing total resources in the system. At the operation center, Support Vector Machines (SVMs) are used to detect the location of potential damage from the reduced data. We demonstrate that the SVM is a robust classifier in the presence of noise and that wavelet-based compression gracefully degrades its classification accuracy. We validate the effectiveness of our approach using a finite element model of the Humboldt Bay Bridge. We envision that our approach will prove novel and useful in the design of scalable nondestructive health monitoring systems.

Mining IC test data to optimize VLSI testing

We describe an application of data mining and decision analysis to the problem of die-level functional test in integrated circuit manufacturing. Integrated circuits are fabricated on large wafers that can hold hundreds of individual chips (“die”). In current practice, large and expensive machines test each of these die to check that they are functioning properly (die-level functional test; DLFT), and then the wafers are cut up, and the good die are assembled into packages and connected to the package pins. Finally, the resulting packages are tested to ensure that the final product is functioning correctly. The purpose of die-level functional test is to avoid the expense of packaging bad die and to provide rapid feedback to the fabrication process by detecting die failures. The challenge for a decisiontheoretic approach is to reduce the amount of DLFT (and the associated costs) while still providing process feedback. We describe a decisiontheoretic approach to DLFT in which historical test data is mined to create a probabilistic model of patterns of die failure. This model is combined with greedy value-of-information computations to decide in real time which die to test next and when to stop testing. We report the results of several experiments that demonstrate the ability of this procedure to make good testing decisions, good stopping decisions, and to detect anomalous die. Based on experiments with historical test data from Hewlett Packard Company, the resulting system has the potential to

Dynamic resource discovery for sensor networks

As sensor networks mature the current generation of sensor networks that are application-specific and exposed only to a limited set of users will give way to heterogeneous sensor networks that are used dynamically by users that need them. The available sensors are likely to be dynamic (e.g., due to mobility) and heterogeneous in terms of their capabilities and software elements. They may provide different types of services and allow different configurability and access. A critical component in realizing such a vision is dynamic resource discovery. In this paper, we develop a resource discovery protocol for sensor networks, outline some of the challenges involved, and explore solutions to some of the most important ones. Specifically, we first discuss the problem of what resources to track and at what granularity: in addition to the individual sensor capabilities, some resources and services are associated with sensor networks as a whole, or with regions within the network. We also consider the design of the resource discovery protocol, and the inherent tradeoff between interoperability and energy efficiency.

Elements of an integrated health monitoring framework

Internet technologies are increasingly facilitating real-time monitoring of Bridges and Highways. The advances in wireless communications for instance, are allowing practical deployments for large extended systems. Sensor data, including video signals, can be used for long-term condition assessment, traffic-load regulation, emergency response, and seismic safety applications. Computer-based automated signal-analysis algorithms routinely process the incoming data and determine anomalies based on pre-defined response thresholds and more involved signal analysis techniques. Upon authentication, appropriate action may be authorized for maintenance, early warning, and/or emergency response. In such a strategy, data from thousands of sensors can be analyzed with near real-time and long-term assessment and decision-making implications. Addressing the above, a flexible and scalable (e.g., for an entire Highway system, or portfolio of Networked Civil Infrastructure) software architecture/framework is being developed and implemented. This framework will network and integrate real-time heterogeneous sensor data, database and archiving systems, computer vision, data analysis and interpretation, physics-based numerical simulation of complex structural systems, visualization, reliability & risk analysis, and rational statistical decision-making procedures. Thus, within this framework, data is converted into information, information into knowledge, and knowledge into decision at the end of the pipeline. Such a decision-support system contributes to the vitality of our economy, as rehabilitation, renewal, replacement, and/or maintenance of this infrastructure are estimated to require expenditures in the Trillion-dollar range nationwide, including issues of Homeland security and natural disaster mitigation. A pilot website (http://bridge.ucsd.edu/compositedeck.html) currently depicts some basic elements of the envisioned integrated health monitoring analysis framework.

Data Management at Kenting's Underwater Ecological Observatory

The management of real-time streaming data in large-scale collaborative applications presents major processing, communication and administrative challenges. To that end, an open-source RBNB DataTurbine provides an excellent basis for developing robust streaming data middleware. The current RBNB DataTurbine streaming data middleware system satisfies a core set of critical infrastructure requirements including reliable data transport, the promotion of sensors and sensor streams to first-class objects, a framework for the integration of heterogeneous instruments, and a comprehensive suite of services for data management, routing, synchronization, monitoring, and visualization. As a part of PRAGMA telescience group, in collaboration with the National Center for High- Performance Computing (NCHC) Taiwan, researchers at the San Diego Supercomputer Center (SDSC) deployed RBNB DataTurbine-based system to acquire data from underwater cameras (in the ocean) at Renting. More specifically, we describe a system that integrates sensors (underwater video cameras) with computing and storage Grids to create a complete fabric for conducting e-Science. The system is currently used for observation by marine research scientists at the Research Center for Biodiversity, Academia Sinica in Taiwan. The described system increased performance and availability of the captured videos and we are, so far, pleased with its results.

Conceptual Challenges and Practical Issues in Building The Global Lake Ecological Observatory Network

Freshwater lakes provide a number of important ecosystem services such as supply of drinking water, support of biotic diversity, transportation of commercial goods, and opportunity for recreation. Wireless sensor networks allow continuous, fine-grained, in situ measurements of key variables such as water temperature, dissolved gases, pH, conductivity, and chlorophyll. Instrumenting lakes with sensors capable of sampling environmental variables is becoming a standard practice. Furthermore, many limnologists around the world are interested in getting access to and performing research on data collected from lakes around the globe to provide local, regional and even global understanding of lake ecosystems. To that end, a number of limnologists, information technology experts, and engineers have joined forces to create a new, grassroots, international network, the Global Lake Ecological Observatory Network. One of our goals is to build a global scalable, persistent network of lake ecology observatories. However, implementing and designing technology that meets requirements of a large-scale distributed observing systems such as GLEON has, thus far, been challenging and instructive. In this paper, we describe several key conceptual challenges in building GLEON network. We also describe several practical issues and lessons learned during operation of a typical GLEON site.

An ocean observatory sensor network application

We describe our implementation of a novel deep ocean sensor network, the MBARI Free Ocean CO2 Enrichment (FOCE). FOCE is a system designed for installation in the deep ocean to enable manipulative experiments that explore the impact of deep ocean increase in CO2 and resulting pH change on ocean biogeochemistry and ecology. This system uses control feedback and pH sensors to inject CO2 into a small volume of seawater, thus creating a controlled environment per science requirements. To implement this system, we utilized the MBARI-developed network middleware known as “SIAM”, which provides a standardized interface to instruments on a sensor network. For the FOCE application we integrated Open Source DataTurbine (OSDT) into SIAM. OSDT provides asynchronous communication links between distributed components, and is particularly well-suited to streaming instrument data. Combined with the existing synchronous SIAM framework, these features enabled a straightforward and efficient architecture for our application. We describe how we achieved our goals of software reuse of infrastructure and instrument services, instrument-in-the-loop control, and rapid assembly of a scalable end-to-end sensor network system.

Using 9-1-1 call data and the space–time permutation scan statistic for emergency event detection

The space-time permutation scan statistic has been previously used to detect disease outbreaks, without need for uniform population at risk, control group data, or information about the distribution of population-at-risk in order to establish the statistical significance of found clusters of cases. This paper shows results from using the space-time permutation scan statistic to detect clusters of 9-1-1 emergency calls. These clusters are then correlated with wide-scale emergency events as reported on the news. Using several examples, it is shown that these clusters are useful for estimating the location, temporal extent, and human impact of such emergency events.

The Open Source DataTurbine Initiative: Empowering the Scientific Community with Streaming Data Middleware

DataTurbine is a robust real-time streaming data engine that lets you quickly stream live data from experiments, labs, web cams and even Java-enabled cell phones. It acts as a “black box” to which applications and devices send and receive data. Think of it as express delivery for your data, be it numbers, video, sound or text. For ecological applications, DataTurbine is useful for moving data in near real time from sensors to field stations to data centers. DataTurbine handles time series data over networks with intermittent connectivity. It is vendor-neutral, so it works with sensors and dataloggers from a variety of manufacturers and research labs.