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Sensor networks generate a variety of data streams in different temporal and spatial resolutions. The data come as numbers text, images, and audio and are dynamically produced periodically and sporadically. How can we organize hundreds of thousands of such data streams? How can we make it easy for scientists and engineers to publish and share such data streams? In this paper, we present our solution, SensorBase.org. It is a web application that not only provides the user with the functionality of a traditional database management system, but also runs under the notion of a Web 2.0 data experience with a responsive user interface design and RSS data feed techniques. SensorBase.org also aims to be a data search engine to promote exploration. Like a web search engine, the user should be able to search for structures, or rather, “signals”, in the data using simple language queries. We provide a solution to a specific type of signal search problem and describe a search framework derived from the solution. A visualization component is in development to help people understand the data and to enhance the results provided by a data search engine. The development of SensorBase.org allows us, the statisticians, to influence “data practices”. People share data and add documentation within the guidelines of SensorBase.org which supports good experimental design and helps future modeling efforts. Last but not least, SensorBase.org provides a base for data integrity and data quality.

Distributed, high-density spatiotemporal observations are proposed for answering many river-related questions, including those pertaining to hydraulics and multi-dimensional river modeling, geomorphology, sediment transport and riparian habitat restoration. We present here a case study of an autonomous, high-resolution robotic spatial mapping of cross-sectional velocity and salt concentration in a river basin. Several experiments for analyzing the spatial and temporal trends at multiple cross-sections of the San Joaquin River were performed during the campaign from August 21-25, 2006. Preliminary analysis from these experiments illustrating the range of investigations is presented. Lessons learned during the campaign are discussed to provide useful insights for similar robotic investigations in aquatic environments.

The creation over the past decade of unanticipated applications of the Internet, such as web services, peer-to-peer file sharing, networked gaming, podcasting, and voice telephony, is resulting in a recent rethinking of the core Internet infrastructure and the original architecture choices. In this project however we propose to go beyond reacting to these applications that have already emerged, and proactively consider the network architecture implications of a new class of applications involving embedded sensing technology as it moves from scientific, engineering, defense, and industrial contexts to the wider personal, social and urban contexts. Today, applications are emerging which draw on sensed information about people, objects, and physical spaces. These applications enable new kinds of social exchange: By collecting, processing, sharing, and visualizing this information, they can offer us new and unexpected views of our communities. They require new algorithms and software mechanisms because unlike scientific applications of distributed sensing, a single system is widely distributed, intermittently connected, and privately administered; and unlike traditional Internet applications the physical inputs are critical to the behavior.

CENS programs are designed to create an educational pipeline to graduate school, provide support and development to our graduate student affiliates and increase the diversity of our Center and those pursuing education and careers in science and engineering.

Plant and animal phenology measurements provide an important means of understanding the relationship between periodic phenomena, such as leaf flushes or bird nesting dates, and microenvironmental and climatic conditions. The collection of phenological data, however, can be labor intensive, and thus basic phenological information about many plant and animal species is not known. Imaging technologies at the James Reserve are currently being used to collect high-frequency, multiple-location phenological images together with micrometeorological data. Using simple algorithms for color analysis of images, results from the MossCam project have indicated that rainfalls of even 1 mm can result in significant increases of photosynthesis by the moss Tortula priceps and that leaf area of Rhododendron is positively correlated with green: red pixel ratios. Current work on improving algorithms, extending studies to analyze annual carbon budgets, and correlating micrometeorological data with nesting behavior will allow us to increase our understanding animal population dynamics and the climatic effects on plant distributions and primary productivity.

Understanding the dynamics of a microclimate environment is difficult because of the number of factors that lead to changes in the environment. Minimizing the number of sensors needed to accurately characterize the environment results in low deployment and maintenance cost, while maximizing the utility of the data. Optimal sensor placement is difficult because it is dependent upon the properties of the environment, the types of obstacles in the environment, as well as the sensing phenomenon. We have deployed a sensor network in a botanical garden consisting of both static and portable nodes. Each node is equipped with temperature and humidity sensors, and readings were taken once per minute for over a month. Using the data from this deployment we present and evaluate two different approaches to sensor placement. The basis for our deployment approach is spatial-temporal statistical analysis that combines splinebased modeling, principal component analysis, and data partitioning. We prove that sensor network deployment is an NP-complete problem using a transformation from the dominating set problem. We also develop an integer linear programming (ILP) formulation that calculates the provably optimal solution to the network deployment problem.

A main thrust of the seismic application is the development of algorithms for performing high spatial resolution measurements and modeling of structures using wired and wireless arrays. The UCLA Factor building is a testbed for predictive numerical modeling, wireless network, and smart event-detection developments related to structural health monitoring. Waveform data from the 72-node array in the 17-story moment-resisting steel frame Factor building are used in comparison with finite element calculations for predictive behavior.

Following in the footsteps of the field Networked Infomechanical Systems (NIMS) currently deployed at the James San Jacinto Mountain Reserve in Idyllwild California, two smaller scale NIMS architectures have emerged. The Laboratory Systems (NIMS-LS) provides an experimental testbed for verification of actuated sensing of environmental phenomena. The NIMS-LS System also includes emulation of environmental phenomena (i.e. light distribution in ecosystems). The Rapidly Deployable (NIMS-RD) platform provides a flexible means of sampling environmental phenomena directly with minimal infrastructure, rapid deployability and minimal impact on the environment. NIMS-RD has been deployed at the James San Jacinto Mountain Reserve, Medea Creek in the Santa Monica Mountains, Merced River in Merced CA and White Mountains Research Station near Bishop CA.

Rapidly growing stress on the natural environment has created an urgent demand for new environmental exploration systems that operate remotely and autonomously. As sensor networks are applied to these critical environmental monitoring problems, a series of new requirements have emerged for extensive characterization of dynamic phenomena in three-dimensional environments. The unpredictable presence of obstacles to sensing and the inherent temporal evolution of events create limitations for optimal deployment of sensors in the environment. Networked Infomechanical Systems (NIMS) technology has been developed to introduce precise and sustainable actuation for distributed sensing. NIMS devices are suspended on cable infrastructure to provide a combination of broad range motion combined with accurate localization.

High Integrity in Sensor Networks studies the effect of compromised data on sensor network data analysis and data fusion algorithms. We are at the start of a study to better understand the compromised data itself so that we can model it in such a way to characterize this affect on analysis. The accelerated life experiment we describe here was instrumented to see how the accuracy of a temperature and humidity sensor changes across mote and sensor systems and across time as the battery dies. We found that the error in the measurements was stable over time, but the bias in the measurements varied greatly across the motes. The experiment gave us more information on the important variables to include in calibration function, so that we can estimate the true measured value from the reported value of the sensor.

Rapidly growing stress on the natural environment has created an urgent demand for new environmental exploration systems that operate remotely and autonomously. As sensor networks are applied to these critical environmental monitoring problems, a series of new requirements have emerged for extensive characterization of dynamic phenomena in three-dimensional environments. The unpredictable presence of obstacles to sensing and the inherent temporal evolution of events create limitations for optimal deployment of sensors in the environment.

Radio duty cycling has received significant attention in sensor networking literature, particularly in the form of protocols for medium access control and topology management. While many protocols have claimed to achieve significant duty-cycling benefit in theory and simulation, these benefits have often not translated to practice. The dominant factor that prevents the optimal usage of the radio in real deployment settings is time uncertainty between sensor nodes. This poster presents an uncertainty-driven approach to duty-cycling where a model of long-term clock drift is used to minimize duty-cycling overhead.

In this poster, two major strategies for sampling phenomena with high rate of spatiotemporal variation, namely, multiscale sampling and adaptive sampling, are presented. Multiscale sampling is based on the hypothesis that a combination of sparse sensing along with auxiliary multiscale/multimode sensing will yield accuracy approaching exhaustive sampling. Adaptive sampling is a feature based sampling technique. Algorithms and simulation results for both techniques are demonstrated.

Monitoring of environmental phenomena with embedded networked sensing confronts the challenges of both unpredictable variability in the spatial distribution of phenomena, coupled with demands for a high spatial sampling rate in three dimensions. For example, low distortion mapping of critical solar radiation properties in forest environments may require two-dimensional spatial sampling rates of greater than 10 samples/m2 over transects exceeding 1000 m2. Clearly, adequate sampling coverage of such a transect requires an impractically large number of sampling locations. This work describes a new approach where the deployment of an adaptive sampling algorithm on a mobile sensor node improves the performance of spatiotemporal sampling density to better cope with a set of environmental mapping demands. Here the robot actively builds a statistical model of the environment and picks samples selectively to increase the performance of such modeling. In addition we will present our active modeling simulation that has been implemented in R statistical computing language and can potentially run on the robot in real time.