Center for Embedded Network Sensing

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.

The contaminant assessment and management (or “Contam”) research area focuses on developing and implementing embedded networked sensing (ENS) technology to support this new observational strategy in the context of mass and energy distributions and fluxes across a range of temporal and synoptic scales. The specific areas of interest for Contam include soils, groundwater, and riparian systems. The Contam application domain is unique relative to the other three CENS applications in that it is often concerned with enabling adaptive management of environmental problems through engineered responses triggered by ENS observations. Example applications include improving our understanding of river metabolism in relation to adjacent and upstream land management practices, creating closed-loop feedback-control systems for conserving irrigation water and avoiding excessive nitrogen application in agricultural systems, delineating nutrient fluxes between groundwater and surface water, and rapid identification of coastal pollutants.

Inorganic nitrogen (nitrate, NO3-) is a major source of pollution in groundwater, surface water and the air. Application of nitrate-containing fertilizers can create distributed or non-point source pollution problems. Scalable nitrate sensors (sensors which are small and inexpensive) would enable us to better assess non-point source pollution processes in agronomic soils, groundwater and rivers. Sensor research groups in the CENS have been working toward high-performance scalable nitrate sensors using (1) potentiometric, (2) amperometric method, and the recent addition is (3) spectrochemical sensor. 1. Potentiometric Nitrate Sensor. This work describes the fabrication and testing of inexpensive PVC-membrane-based ion selective electrodes (ISEs) for monitoring nitrate levels in soil water environments. Over the past year, we emphasized testing of the in situ behavior of fabricated sensors in soils subject to irrigation with dairy manure water. Observed temporal responses for the nitrate sensors exhibit diurnal cycling with elevated nitrate levels at night and depressed levels during the day; these cycles are prominent even after rigorous temperature corrections. We have thus far concluded that while modern ISEs are not yet ready for long-term, unattended deployment, short-term installations (on the order of 2 to 4 days) are viable and may provide semi-quantitative insights into nitrogen dynamics in complex soil systems. 2. Amperometric Nitrate Sensor. A simple and sensitive amperometric nitrate sensor is microfabricated on the silicon chip and uses a nitrate-sensitive silver electrode. The concentration is determined by double-potential-step chronocoulometry because of high SNR and rejection of oxygen background current. The limit of detection ranges from 4 to 75 µM, and the upper limit of the linear range varies between 500 and 2000 µM. HPO42-/PO34- , Ca2+, and Sr2+ show significant interference (> 20 % signal distortion). We proposed a compact sample-preparation system based on Donnan dialysis to minimize the interference. The novel dialyzer uses small-bore tubes in containing solutions and a peristaltic pump in circulating the solutions to promote agitation. By using an anion-exchange membrane (AEM), the dialyzer removes cations completely and reduces interfering anions significantly from groundwater samples. Major advantages of the novel dialyzer are (1) high dialysis efficiency (~ 90 %), (2) linear response, and (3) reasonable throughput (1 samples/hr).A numerical analysis based on the one-dimensional bi-ionic-system-in-series model provides a good prediction of dialyzer performance. 3. Spectrochemical Nitrate Sensor. A multiplexible, miniaturized, and spectrochemical sensor is proposed because optical methods usually provide superior long-term reliability. UV light is absorbed by nitrate while propagating through groundwater sample in a liquid-core capillary waveguide, and absorbance is analyzed to quantify nitrate using a fiber-optic spectrometer. Using fiber-optic cables and optical multiplexers, multiple sensors can be operated remotely by single spectrophotometer and light source. Multivariate analysis is used to compensate these interferences from ionic and neutral species and baseline from dissolved organic species in groundwater, and calculate nitrate concentration precisely. A series of numerical analyses were performed to assess effects of spectrometric noises and interferences. According to the analyses, a large sharp Gaussian noise with a peak located near the peak of nitrate spectra will generate a significant error. Using a bench-top spectrometer, nitrate spectra in wide concentration range (10-7 to 1-4 M) were recorded to obtain concentration. It is observed that spectra were linear over the range.

Although many sensors (e.g., temperature, light level, acceleration, etc.) that are compatible with sensor networks (i.e., sensitive, small, low power, etc.) are now commercially available, two important classes of sensors are not as technologically mature and remain an area of active research: chemical sensors and biological sensors. The sensor-technology-development efforts in the CENS center are focused on these very challenging classes of sensors. Successful development of chemical and biological sensors will enable wireless-sensor-network technology to span the full range of possible classes of measurements. In addition to better performance, the technological emphases are miniaturization and automation of the developed sensors. Specific sensor-technology-development efforts include: (1) amperometric and potentiometric electrochemical sensors for monitoring nitrate-ion detection in ground water; (2) lab-on-a-chip aquatic microorganism analysis system; and (3) ultra sensitive field operational sensor for marine environmental monitoring.

This work describes preliminary results from a 50 station broadband seismic network recently installed from the coast to the high Andes in Peru. UCLA's Center for Embedded Network Sensing (CENS) and Caltech's Tectonic Observatory are collaborating with the IRD (French L'Institut de Recherche pour le Developpement) and the Institute of Geophysics, in Lima Peru in a broadband seismic experiment that will study the transition from steep to shallow slab subduction. The currently installed line has stations located above the steep subduction zone at a spacing of about 6 km. In 2009 we plan to install a line of 50 stations north from this line along the crest of the Andes, crossing the transition from steep to shallow subduction. A further line from the end of that line back to the coast, completing a U shaped array, is in the planning phase. The network is wirelessly linked using multi-hop network software designed by computer scientists in CENS in which data is transmitted from station to station, and collected at Internet drops, from where it is transmitted over the Internet to CENS each night. The instrument installation in Peru is almost finished and we have been receiving data daily from 47 stations (out of total 49) since Jan 2009. Two remain without any network connectivity. The software system provides dynamic link quality based routing, reliable data delivery, and a disruption tolerant shell interface for managing the system from UCLA without the need to travel to Peru. The near real-time data delivery also allows immediate detection of any problems at the sites. We are building a seismic data and GPS quality control toolset that would greatly minimize the station's downtime by alerting the users of any possible problems.

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.

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.

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.

The most common programming languages and platforms for sensor networks foster a low-level programming style. This design provides fine-grained control over the underlying sensor devices, which is critical given their severe resource constraints. However, this design also makes programs difficult to understand, maintain, and debug. In this work, we describe an approach to automatically recover the high-level system logic from such low-level programs, along with an instantiation of the approach for nesC programs running on top of the TinyOS operating system. We adapt the technique of symbolic execution from the program analysis community to handle the event-driven nature of TinyOS, providing a generic component for approximating the behavior of a sensor network application or system component. We then employ a form of predicate abstraction on the resulting information to automatically produce a finite state machine representation of the component. We have used our tool, called FSMGen, to automatically produce compact and fairly accurate state machines for several TinyOS applications and protocols. We illustrate how this high-level program representation can be used to aid programmer understanding, error detection, and program validation.

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.