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Open Access Publications from the University of California

CENS, a NSF Science & Technology Center, is developing Embedded Networked Sensing Systems and applying this revolutionary technology to critical scientific and social applications. Like the Internet, these large-scale, distributed, systems, composed of smart sensors and actuators embedded in the physical world, will eventually infuse the entire world, but at a physical level instead of virtual. An interdisciplinary and multi-institutional venture, CENS involves hundreds of faculty, engineers, graduate student researchers, and undergraduate students from multiple disciplines at the partner institutions of University of California at Los Angeles (UCLA), University of Southern California (USC), University of California Riverside (UCR), California Institute of Technology (Caltech), University of California at Merced (UCM), and California State University at Los Angeles (CSULA).

Cover page of Imagers as Biological Sensors

Imagers as Biological Sensors


There exist many biological sensing applications where direct measurement is either impossible, extremely invasive, or extremely time consuming. For example, measuring the presence/absence of birds at a feeder station currently requires a human to watch a camera pointed at the feeder, identifying when birds arrive and leave. Similarly, measuring CO2 flux from a plant requires placing the plant inside a growth chamber, destructively modifying the environment. We propose using imagers as biological sensors by constructing a procedure that uses images to obtain approximate measurements of these phenomena. This procedure, composed of state-of-the-art computer vision, image processing, and statistical learning algorithms, will be evaluated in the context of a specific application and shown to be general through multiple instantiations. Through application, it has been found that many of these algorithms make unacceptable assumptions about their input. Providing accurate data to biologists and ecologists, though the appropriate modification of these algorithms, is the ultimate goal of this work.

Cover page of Ecological Sensing in a Southern California Forest: Integrating Environmental Abiotic and Biotic Measurements to Understand Ecosystem Function.

Ecological Sensing in a Southern California Forest: Integrating Environmental Abiotic and Biotic Measurements to Understand Ecosystem Function.


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.

Cover page of Subduction Zone Seismic Experiment in Peru: Results From a Wireless Seismic Network

Subduction Zone Seismic Experiment in Peru: Results From a Wireless Seismic Network


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.

Cover page of New Wireless Miniature Sensor Technologies for CENS

New Wireless Miniature Sensor Technologies for CENS


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.

Cover page of Field Operational Sensor and Lab-on-a-Chip System for Marine Environmental Monitoring and Analysis

Field Operational Sensor and Lab-on-a-Chip System for Marine Environmental Monitoring and Analysis


This is a project that aims to expedite research in marine biology using chip-based and state-of-the-art detection technology. The project is a joint effort that will incorporate the expertise of three different groups, Dr. Chih-Ming Ho at UCLA, Dr. David Caron at USC and Dr. Yu-Chong Tai at Caltech. One main focus of the project is to develop Lab-on-a-chip devices that reduce total sample volume and detection time. Also, the chips can be fabricated in large quantities with minimal cost so many experiments can be run in parallel. Here at Caltech, a chip will be developed to culture a small number of algae and screen for factors inducing toxin production. Algal bloom and toxins produced by different algae have always caused problems to the environment and marine ecology. Pseudo-nitzschia is one type of algae that produces a neural toxin called Domoic Acid, which when transferred through the food chain causes sickness and mortality in marine mammals and seabirds. However, during Pseudo-nitzschia bloom, Domoic Acid is not always produced. In another word, growth of algae does not equal Domoic Acid production. Studies done by other groups have suggested that many factors (such as trace metal, macronutrient, or ionic concentration) might induce or suppress algae to produce toxin. Yet, exact causes are unclear. To completely elucidate the causes of toxin production, many potential compounds will have to be screened. This leads to an enormous amount of experiments to be performed and large quantity of reagents and cells to be used. To speed up the process of screening for possible factors inducing toxin production, we would like to make a chip to culture Pseudo-nitzschia under different growing conditions. At the same time, an Ultra Sensitive Electrochemical Sensor will be developed for detection of Domoic Acid at Dr. Chih-Ming Ho’s lab at UCLA. The current state-of-the-art detection technology indicates that per cell toxin load may range over 2 or 3 orders of magnitude but its sensitivity is limited since a sample size of at least 100 cells/mL is required. The new sensor will be able to push the sensitivity to 10 cells/mL or to even single molecules of Domoic Acid. This sensor will not only enable the detection of Domoic Acid produced by algae cells inside the culture chip, such sensor will also have the broad application of detecting Domoic Acid from field samples.

Cover page of Deriving State Machines from TinyOS programs using Symbolic Execution

Deriving State Machines from TinyOS programs using Symbolic Execution


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.

Cover page of Pan-American Sensors for Environmental Observations (PASEO): An Interdisciplinary Pan-American Advanced Studies Institute (PASI)

Pan-American Sensors for Environmental Observations (PASEO): An Interdisciplinary Pan-American Advanced Studies Institute (PASI)


PASEO was an NSF-funded PASI, which is a two-week training program for U.S. and Latin American early-career scientists and engineers. It took place in February 2009 in Bahia Blanca and Buenos Aires, Argentina. The host institutions were Instituto Argentino de Oceanografia (CONICET-funded), and Instituto Nacional de Tecnologia Industrial (INTI). Collaborating U.S. groups included GLEON, WATERS Network, and Codar Inc. The topic of PASEO was developing and deploying current sensor technologies to obtain a fuller understanding of environmental and ecological systems. PASEO was intended to be a multi-cultural and multi-disciplinary active learning experience. Applicants participated in active learning sessions related to the fabrication, deployment, and analysis of data streaming from environmental sensors. Scientific topics included lake metabolism, eco-hydrology in a saltwater marsh, soil moisture and energy balances, and plant phenology. Opportunities for hands-on training included buoyed and robotic sensor deployment, soil sensors, DTS temperature measurements, coastal radar, and low-temperature co-fired ceramics (LTCC) sensor fabrication methods.

Cover page of Improving Personal and Environmental Health Decision Making with Mobile Personal Sensing

Improving Personal and Environmental Health Decision Making with Mobile Personal Sensing


CENS is focusing on three types of health applications. Personalized medicine (AndWellness, AndAmbulation), epidemiological data collection (Project Surya), and personal decision making and awareness (PEIR). Each of these applications uses a similar systems architecture: time, location (GPS), and motion (accelerometer) trace collection on the mobile phone with a user interface, scientific model-based analytics used to draw inferences from the data, and graphical map or calendar based feedback to users. The specifics of each component depend on the type of data collected, the target populations, and the goals of the project. The UI for AndWellness includes an ecological momentary assessment, which is a set of questions a user completes regarding their feelings at that moment; and control over the time, location, and frequency of reminders, which are included to remind users to complete the assessments. The AndWellness UI aims to make the assessment easy to understand and quick to complete. The UI for Project Surya is designed for rural villagers living in India who will likely not know how to read. Therefore the UI will be primarily graphically based, and have little or no text. The specific analytics used for each project differs based on the goal of the project. All four applications use activity classification algorithms in order to infer a user's activity from the GPS and/or accelerometer traces. The similarity ends here. Project Surya uses image analysis algorithms to infer soot levels from images of specialized filters and calibrated color charts. AndWellness uses simple statistical calculations to calculate base-rates for a small set of behaviors that are measured with the ecological momentary assessments. PEIR uses models from the Air Resources Board and other GIS streams to compute users' carbon impact, particulate exposure, and fast food exposure from a location trace. The feedback for each project is presented using a map and/or calendar based interface, based on the data and goals of the project. Because AndWellness users are interested in identifying patterns in space and time across weeks or months, AndWellness presents data in both a calendar and map-based interface, and makes it easy to cross reference any event across either mode. PEIR uses a map to highlight routes and the pollution exposure, and bar graphs to show aggregates for each of the three metrics computed by the analytics. AndAmbulation solely uses a calendar interface because users are most interested in trends over time.