Center for Embedded Network Sensing

Through the NAMOS project, our team of biologists and engineers are assisting municipalities in understanding the underlying causes and effects of harmful microalgal blooms. Since early 2007, we have been studying system-level dynamics of the chemical, physical, and biological processes in King Harbor, a shallow, semi-enclosed urban harbor in Redondo Beach, California. For the last two years a network of dock-based water quality sensors in the harbor has continuously provided data on the environmental parameters relevant to bloom formation. Additionally, intensive human-mediated studies of the phytoplankton community distribution and structure are testing several hypotheses on the biological and physical factors affecting algal growth in this system. Recent field experiments have sought to explain the roles of tidal forcing and phytoplankton behavior and physiology in the structuring and distribution of bloom-forming algal communities.

In traditional sensor systems, one of the fundamental problems concerns the placement of sensors. The analogous problem in participatory sensing is choosing users to perform a particular data collection task. This work details a recruitment framework that is designed to help with this process. Specifically, the framework considers the capabilities in terms of sensors available by a particular user, the availability of the user to participate in terms of spatial and temporal contexts, the reputation of the user as a data collector, and the incentive cost associated with the user participating as elements involved in the process of choosing data collectors. The utility of the recruitment service is shown through a series of campaigns related to ecological and sustainability monitoring.

Stationary and mobile ground-based cameras can be used to scale ecological observations, relating pixel information in images to in situ measurements. Currently there are four CENS projects that involve using cameras for scaling ecological observations: 1. Scaling from one individual to the landscape. Pan-Tilt-Zoom cameras can be zoomed in on a tight focus on individual plants and parts of individuals and then zoomed out to get a landscape view, composed of the same and similar species. 2. Estimating photosynthesis over large areas with HDR. High Dynamic Range imaging is a technique to capture an absolute amount of reflected light in an image. For a meadow composed of similarly reflecting species, we can estimate light received by leaves and thus photosynthesis over a wide area. 3. Scaling soil surface temperature measurements. Soil surface temperatures and soil energy balance are related to solar radiation and air temperature. Sunflecks captured with a camera taking panoramic mosaics of images can be used to estimate the radiation load for large areas of unobstructed understory. 4. Expanding plant phenological observations with a nation-wide network of webcams. Twice-daily images from over 1000 internet-connected and freely available cameras have been collected since February 2008. The advance of Spring can be tracked as a "green-up" and related to satellite remote sensing signals.

In this poster, we consider two major themes in the design of sensor networks: data integrity, and sampling strategies. For the data integrity problem, we propose a signature-based fault detection system for identifying both intermittent faults and persistent faults. Data-dependent features using temporal, spatial, and spatio-temporal information that are useful for detecting faults are identified. These features are combined into signatures that characterize each of the different fault types. We also discuss the problem of simultaneous parameter estimation and fault detection. In this case, parameters must be estimated from a distribution that is truncated in various ways as a result of the fault detection algorithm, which can lead to biased estimates. We propose several methods to account for the bias in parameter estimates. For the sampling problem, we describe two on-going projects. The first one deals with situations where sampling as you move (using sampling paths) is more effective than contemplating sampling points. A PAR sensor riding on a NIMS 3D node is one such situation. This configuration is especially well-suited for sampling phenomena that exhibit latent geometric structure, such as light fields in forest understories. We will consider the case where the phenomena can be approximated by a piecewise-constant field and suggest a novel estimation approach when we have sample paths as observations. The second project considers the problem of finding a sampling strategy to optimize the selection of the correct regression model from a set of competing regression models. The solution is driven by minimizing the probability of error in the selection and consists of a sequential algorithm that directs the collection of measurements. We develop an adaptive sampling algorithm to sample the field with a set of static sensors and one mobile sensor. The algorithm aims to jointly minimize the probability of error in the selection and the mobility cost. The algorithm presented provides a significant improvement in the probability of error in the selection of the correct model over the random collection of measurements.

CENS researchers are developing flexible wireless sensing technologies that can be used in a variety of scientific and social applications. These technologies produce data that often have value to both the immediate research questions and to longer-term studies of longitudinal phenomena. CENS sensing systems are being deployed in many different real-world settings. Managing sensor deployments and the resulting data can be difficult. This poster outlines our work in developing tools to help CENS researchers conduct deployments and manage the resulting data, specifically the CENS Deployment Center, Sensorbase, and the deployment webpages created for the Seismic Deployment in Peru. The CENS Deployment Center (CENSDC) is a web-based repository for CENS deployment information. The CENSDC provides a central location for researchers to document deployment activities through the creation of pre-deployment plans and post-deployment feedback/notes. By allowing users to describe their deployment experiences, including lessons learned, troubleshooting techniques, and guidance for future deployments, the CENSDC attempts to capture the tacit knowledge about equipment setups, deployment locations, and field preparations that play a critical role in data collection techniques. Sensorbase is a database for CENS sensor collected data. Users can set up automated data uploads into Sensorbase from remote wifi enabled nodes deployed in the field, enabling researchers to monitor and manage their data remotely. Sensorbase can also generate email alerts when user-defined conditional changes in data occur, eliminating the need to search through the collected data to see that something is wrong (or right) with the deployment. Also, a programmatic approach to doing some of the features previously allowed only in the web user interface has been implemented so that sensors in the field can do more without human interaction. Finally, Sensorbase allows users to designate all or portions of their data to be shared with other researchers. The Peru deployment is a joint UCLA and Caltech project to study seismic activity along the South American subduction zone. Along with the seismic data, the seismic team is collecting various kinds of technical data to measure the health of the seismic stations, as well as of the wireless networks that connect them to each other and to the internet. These measures help the seismic team to identify problems as they arise. We created a number of interfaces that display network health metrics for the installed stations to enable members of the seismic team to view the current status of the wireless links across the transect, and helping them to be more responsive to emerging problems. These tools facilitate more efficient sensor deployments by allowing researchers to discover problems with data in real-time, identify and describe the problems, and annotate the solutions for future deployments. Through this process, the resulting data should be of higher quality in the short term, and more easily used and reused in the long term.

Trajectory design for Autonomous Underwater Vehicles (AUVs) is of great importance to the oceanographic research community. Intelligent planning is required to maneuver one or many vehicles to high-valued locations to collect data with scientific merit. We consider the use of ocean model predictions to determine the locations to be visited by a team of AUVs, which then provides near-real time, in situ measurements back to the model to increase model skill and the accuracy of future predictions. Iterative application of this procedure determines relevant points of interest that allow the AUV fleet to monitor and track a chosen oceanographic feature. For this study, we select the ocean feature to be a freshwater plume, as their colder, nutrient-rich water promotes productivity, and may result in the formation of a Harmful Algal Bloom (HAB). Monitoring and predicting the formation and evolution of HABs is an area of active research for southern California coastal communities due to their production of harmful toxins that can affect humans and marine wildlife. The movement of the freshwater plumes is predicted by use of the Regional Ocean Modeling System (ROMS) oceanic model applied to our primary area of interest, the Southern California Bight (SCB). Based on the chosen feature, the ROMS prediction, the number of AUVs, each vehicle's operational velocity and the duration of the sampling mission, an algorithm determines waypoints (sampling locations) for the AUV(s) to visit. A trajectory for each vehicle is then generated based on the computed waypoints. We present samples of these trajectories and their implementation results for single and multiple-vehicle experiments that were conducted off the coasts of Los Angeles and Catalina Island. This research represents a first approach to an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks.

CENS research related to developing and implementing structural health monitoring (SHM) systems is advancing on two distinct but related fronts: ShakeNet, a portable wireless sensor network for rapid, post-event deployments and SHMnet, a novel SHM system for permanent monitoring of tall buildings and special structures in Los Angeles. The primary objective of the SHMnet research is the development of a robust SHM system along with the associated hardware and software, using tall and special structures (e.g., bridges, port structures, dams) in Los Angeles as a testbed. More specifically, the development of a wireless Data Acquisition (DAQ) toolbox suitable for rapid urban deployments, a suite of state-of-the-art sensors for monitoring key structural responses including innovative methods for directly measuring interstory displacements, and probabilistic post-event assessment algorithms based on experimental motion-damage relationships. Progress on these fronts is highlighted. One rather unique aspect of this research stems from partnerships with strong-motion instrumentation programs (SMIPs) such as CSMIP, ANSS, and the LA-DBS. The proposed SHMnet leverages both building access and instrumentation requirements already facilitated by one or more SMIPs. However, a critical look at structural instrumentation guidelines of various SMIP agencies exposed a lack of uniformity of experience-based specifications. To this end, we sought to establish a quantitative basis for key structural instrumentation specifications, namely sample rate, resolution, and time synchronization. This was accomplished by analyzing signal errors associated with data acquisition processes and engineering sensitivity analyses of several intensity measures and engineering demand parameters. Results from these studies will be useful in updating current structural instrumentation specifications of major SMIPs as well as provide specifications for SHMnet tools. ShakeNet is a portable wireless sensor network for instrumenting large civil structures such as buildings and bridges. The focus of ShakeNet design is to take advantage of wireless technology for deployments in structural environments where power or communications infrastructure is nonexistent or unavailable. It is designed to collect structural vibration measurements for up to a week from each node within the network by deployment in large structures within hours after an earthquake. It will consist of 25 sensor nodes and 5 to 10 master-tier nodes (Stargates or other embedded computers) that provide increased communications capacity. The ShakeNet software subsystem is built upon Tenet; programmable wireless sensing software designed for multi-tier sensor networks. ShakeNet will be deployed and tested on several structures that represent a range of structure types, environments, ages, and degrees of retrofit. They include the Seven Oaks Dam in Redlands, CA, the Santa Ana River Bridge in Riverside, CA, 1100 Wilshire Blvd. in downtown Los Angeles, CA, and the Long Beach Veterans Administration Hospital in Long Beach, CA.

Understanding the interactions between belowground and aboveground process and how they respond to annual climatic variability remains a challenging task. In this study, we combined molecular techniques with high frequency images from automated minirhizotrons to determine the identity and temporal variability of fine roots and mycorrhizal fungi in a mixed-conifer forest in Southern California. We also examined how changes in fine roots and mycorrhizal fungi are related to leaf phenology and water dynamics over the course of the growing season. Throughout the study, there was considerable variation in ectomycorrhizal roots, with greater ectomycorrhizal roots during the dry summer months compared to early spring. Although the total number of ectomycorrhizal fungi did not change, there was a significant change in the ectomycorrhizal fungal community over the course of the growing season. Arbuscular mycorrhizal roots, on the other hand, showed little variation during the growing season. Sap flow peaked in mid-June, and corresponded well to the formation of new leaves and a period of relatively high soil moisture. Soil respiration varied between 1 µmol CO2 m-2 s-1 and 3.5 µmol CO2 m-2 s-1 during the year, with greater rates corresponding to periods of relatively high soil moisture and high soil temperature. By integrating data from a wide range of sensors, we can better understand the biophysical factors influencing the flux of carbon and water through an ecosystem.

Geographic Information Systems (GIS) is a powerful mapping tool that can be used to reveal spatial and temporal relationships of a criteria of interest. We have used GIS to visualize the seasonal and spatial distribution of microbial pollution obtained from the Heal the Bay beach water quality report (2007). These maps can be used to inform sampling decisions; more specifically, we can use it to identify areas of chronic pollution and can be used as a testbed for a rapid sensing system for bacteria. This rapid detection system can be used to provide higher resolution and understanding of water pollution as well as assist in understanding/characterizing environmental water quality in specific areas. We propose the subsequent use of an covalently-linked immumomagnetic separation/ATP quantification assay that is rapid, robust, and field-portable as an instrument to conduct monitoring of E. coli and Enterococcus in marine and freshwater systems.