Center for Embedded Network 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.

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.

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.

overview poster so no abstract.

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.

The overarching theme of the Center’s Aquatic application area continues to be the creation and application of a new genre of wireless sensing systems that will provide real-time monitoring capabilities of chemical, physical and biological parameters in freshwater and coastal marine ecosystems. High-resolution temporal and spatial measurements are essential for understanding the highly dynamic nature of aquatic ecosystems and the rapid response of microbial communities to environmental driving forces. Our unique approach to aquatic sensing and sampling, Networked Aquatic Microbial Observing Systems (NAMOS), employs coordinated measurements between stationary sensing nodes (buoys and pier-based sensors) and robotic vehicles (surface robotic boats and autonomous underwater vehicles) to provide in-situ, real-time presence for observing plankton dynamics (e.g. phytoplankton abundance, dissolved oxygen), and linking them to pertinent environmental variables (e.g. temperature, light, nutrients, etc.). Specific projects undertaken in this application area involve the development and deployment of sensor networks to examine harmful algal blooms within King Harbor, City of Redondo Beach, and the construction of mobile sensor networks in open coastal waters off southern California. The latter research involves deployments of autonomous surface and underwater vehicles, and the development of hardware and software for coordinated activities of these robotic vehicles.

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.

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.

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.