Center for Embedded Network Sensing

In conjunction with Caltech and the Geophysical Institute of Peru, we installed our network of 49 seismic sites across steep and shallow subduction regions in Peru. Flat slab subduction is thought to have formed much of the major geology of the western United States some 100 million years ago. By examining such processes presently active in Central and South America we can piece together the history. The data from the Peruvian sites is delivered to UCLA every night and we have collected almost 1 year so far. In the GeoNet experiment, the science objective is to use a rapidly installable wirelessly linked seismic network to make near-real time unaliased observations in aftershock or volcanic zones. The immediate technical objective is to collaborate with Reftek to construct a new generation digital acquisition system (DAS) based on the CENS-developed LEAP (low-power energy aware processing) system and a newly developed low-power A/D converter from Texas Instruments. We also look into the possibility of using the cell phones for seismic data collection and research. Phones are increasingly being equipped with not only accelerometers, but also cameras, Global Positioning System (GPS) receivers and Internet connectivity. This makes them very attractive for use in data mining. We have tested a number of small USB accelerometers and a cell phone on a shake table and the results are encouraging.

Mobile sensor platforms such as Autonomous Underwater Vehicles (AUVs) and robotic surface vessels, combined with static moored sensors compose a diverse sensor network that is able to provide macroscopic environmental analysis tool for ocean researchers. Working as a cohesive networked unit, the static buoys are always online, and provide insight as to the time and locations where a federated, mobile robot team should be deployed to effectively perform large scale spatio-temporal sampling on demand. Such a system can provide pertinent in situ measurements to marine biologists whom can then advise policy makers on critical environmental issues. This poster presents recent field deployment activity of AUVs demonstrating the effectiveness of our embedded communication network infrastructure throughout southern California coastal waters. We also report on progress towards real-time, web-streaming data from the multiple sampling locations and mobile sensor platforms. Static monitoring sites included in this presentation detail the network nodes positioned at Redondo Beach and Marina Del Ray. One of the deployed mobile sensors highlighted here are autonomous Slocum gliders. These nodes operate in the open ocean for periods as long as one month. The gliders are connected to the network via a Freewave radio modem network composed of multiple coastal base-stations. This increases the efficiency of deployment missions by reducing operational expenses via reduced reliability on satellite phones for communication, as well as increasing the rate and amount of data that can be transferred. Another mobile sensor platform presented in this study are the autonomous robotic boats. These platforms are utilized for harbor and littoral zone studies, and are capable of performing multi-robot coordination while observing known communication constraints. All of these pieces fit together to present an overview of ongoing collaborative work to develop an autonomous, region-wide, coastal environmental observation and monitoring sensor network.

Data, statistical models and inferential procedures permeate CENS deployments, from the four founding scientific application areas to the more recent urban sensing campaigns. This cross-center research breaks down into three classes of research: 1) General statistical models for embedded sensing, with specific applications to data quality and continuous sampling, 2) Significant CENS-designed and supported databases and repositories, and 3) Studies into the data lifecycle for embedded sensing systems.

This poster summarizes the body of CENS work in the San Joaquin River (SJR) basin that is aimed at creating a prototypical observation-modeling-management (feedback-control) system. The objective of the proposed system is to clarify the linkages between land use and chemical transport and fate along the soil zone-groundwater-surface water flow path. Work to date is presented on the following sub-projects: (1) The application of high resolution river multi-scale observations to define a 2-D hydrodynamic model at the SJR-Merced River confluence, (2) The use of embedded sensor systems known as temperature javelins to estimate local groundwater fluxes into the Merced River upstream of the confluence, and (3) The installation of long-term sensor systems aimed at continuously observing the flow path between agricultural systems and the Merced River.

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.

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.

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.

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.

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.