Center for Embedded Network Sensing

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.

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.

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.

The average person is currently unaware of the real-time energy consumption for the different household appliances that he uses. At best, he can observe the monthly or bi-monthly bill indicating the total power consumption of all the appliances combined. This makes it difficult to improve the consumption efficiency, since there is no visibility in the data that he can access. We believe that real-time appliance level monitoring is necessary to allow residents to manage their energy consumption efficiently. However, monitoring end-point level power consumption is difficult to impossible with current technologies because expensive sensors, or professionally installation is necessary. In addition, device aesthetic and the inherent intrusiveness of direct in-line sensors to measure the energy usage at every end-point complicate such a system installation. Since appliances emit measurable signals when they are consuming resources, we argue that less-invasive sensors can be used for inferring real-time resource consumption. However, indirect sensors cannot be calibrated during manufacturing because of varying ambient conditions. Thus, the main challenge becomes to provide a method that autonomously calibrates the sensors. We seek to develop an easy and self-configurable monitoring system for very fine grained resource monitoring in residential spaces.

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.

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.

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.