Advances in technology and infrastructure have positioned mobile phones as a convenient platform for real-time assessment of an individuals health and behavior, while offering unprecedented accessibility and affordability to both the producers and the consumers of the data. In this paper we address several of the key challenges that arise in leveraging smartphones for health: designing the complex set of building blocks required for an end-to-end system, motivating participants to sustain engagement in long-lived data collection, and interpreting both the data and the quality of the data collected. We present AndWellness, a mobile to web platform that records, analyzes, and visualizes data from both prompted experience samples entered by the user, as well as continuous streams of data passively collected from sensors onboard the mobile device. In order to address the system design and participation motivation challenges, we have incorporated feedback from hundreds of behavioral and technology researchers, focus group participants, and end-users of the system in an iterative design process. AndWellness additionally includes rich system and user analytics to instrument the act of participation itself and ultimately to contextualize and better understand the factors affecting the quality of collected data over time. We evaluate the usability and feasibility of AndWellness using data from 3 studies with a variety of populations including young moms and recent breast cancer survivors. More than 85% of the diverse set of participants who responded to exit surveys claim they would use AndWellness for further personal behavior discovery.
This Technical Report provides a review of a new embedded computing platform enabling research, education and training, and product development based on the Intel Atom processor architecture. This introduces a dramatic advance in the capability for direct characterization of energy and power dissipation in embedded computing platforms and the associated capabilities for optimization of performance and energy. This report includes development, usage, and example operation and results with platform applications in mobile computing, distributed sensing, network routing, and wireless access point implementation. In each case, Atom LEAP is intended to provide both a reference design and a high throughput, easily implemented solution with an unprecedented advance in the capability for characterizing energy usage at a level of computing task and operating system detail substantially superior to prior methods.
Participatory Sensing is a process whereby individuals and communities use mobile phones and web services to observe, analyze, and present personal and environmental artifacts, events and experiences. In this technical report we describe a community data campaign that made use of smartphone based participatory sensing for environmental needs assessment. Community organizers defined the content of the participatory sensing campaign. 68 individuals participated over the course of 6 weeks, uploading over 450 mini-surveys, including over 700 images.
Many robotic path planning applications, such as search and rescue, involve uncertain environments with complex dynamics that can be only partially observed. When selecting the best subset of observation locations subject to constrained resources (such as limited time or battery capacity) it is an important problem to trade off exploration (gathering information about the environment) and exploitation (using the current knowledge about the environment most effectively) for efficiently observing these environments. Even the nonadaptive setting, where paths are planned before observations are made, is NP-hard, and has been subject to much research. In this paper, we present a novel approach to adaptive informative path planning that addresses this exploration-exploitation tradeoff. Our approach is nonmyopic, i.e. it plans ahead for possible observations that can be made in the future. We quantify the benefit of exploration through the "adaptivity gap" between an adaptive and a nonadaptive algorithm in terms of the uncertainty in the environment. Exploiting the submodularity (a diminishing returns property) and locality properties of the objective function, we develop an algorithm that performs provably near-optimally in settings where the adaptivity gap is small. In case of large gap, we use an objective function that simultaneously optimizes paths for exploration and exploitation. We also provide an algorithm to extend any single robot algorithm for adaptive informative path planning to the multi robot setting while approximately preserving the theoretical guarantee of the single robot algorithm. We extensively evaluate our approach on a search and rescue domain and a scientific monitoring problem using a real robotic system.
For decades, the Codes of Fair Information Practice have served as a model for data privacy, protecting personal information collected by governments and corporations. But professional data management standards such as the Codes of Fair Information Practice do not take into account a world of distributed data collection, nor the realities of data mining and easy, almost uncontrolled, dissemination. Emerging models of information gathering create an environment where recording devices, deployed by individuals rather than organizations, disrupt expected flows of information in both public and private spaces. We suggest expanding the Codes of Fair Information Practice to protect privacy in this new data reality. An adapted understanding of the Codes of Fair Information Practice can promote individuals’ engagement with their own data, and apply not only to governments and corporations, but software developers creating the data collection programs of the 21st century. To support user participation in regulating sharing and disclosure, we discuss three foundational design principles: primacy of participants, data legibility, and engagement of participants throughout the data life cycle. We also discuss social changes that will need to accompany these design principles, including engagement of groups and appeal to the public sphere, increasing transparency of services through voluntary or regulated labeling, and securing a legal privilege for raw location data.
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.
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 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.
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.
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 capacity to adaptively manage irrigation and associated contaminant transport is desirable from the perspectives of water conservation, groundwater quality protection, and other concerns. This paper introduces the application of a feedback-control strategy known as Receding Horizon Control (RHC) to the problem of irrigation management. The RHC method incorporates sensor measurements, predictive models, and optimization algorithms to maintain soil moisture at certain levels or prevent contaminant propagation beyond desirable thresholds. Theoretical test cases are first presented to examine the RHC scheme performance for the control of soil moisture and nitrate levels in a soil irrigation problem. Then, soil moisture control is successfully demonstrated for a center-pivot system in Palmdale, CA where reclaimed water is used for agricultural irrigation. Real-time soil moisture, temperature, and meteorological data were streamed wirelessly to a field computer to enable autonomous execution of the RHC algorithm. The RHC scheme is demonstrated to be a viable strategy for achieving water reuse and agricultural objectives while minimizing negative impacts on environmental quality.
Large and bright screens on today's mobile phones account for significant energy demand on phones' batteries. In this paper we propose an algorithm that, given the energy profile of the screen, finds the optimal schedule to minimize screen energy dissipation when the phone is idle. We profile the screen energy consumption of two popular smartphones, Nokia N95 and E71, through carefully designed micro-benchmarks. Our energy measurement results suggest that the default screen schedules on these phones are far from optimal - on average our algorithm performs 50% better than default. We also find that on the E71 not using the dim state of the screen and directly turning it off is more energy-efficient. We improve the performance of our screen scheduling algorithm by considering the history of each user's interaction with his/her phone. We study the interaction patterns of six volunteers with their smartphones. The results suggest that the distribution of the length of idle times for each user does not change over time. Therefore, the screen scheduler can learn this distribution during a learning phase and use it to improve screen management. We show that the screen energy consumption can be further reduced by up to 60% using this technique.
Soil salinization is a potentially negative side effect of irrigation with reclaimed water. While optimization schemes have been applied to soil salinity control, these have typically failed to take advantage of real-time sensor feedback. This study incorporates current soil observation technologies into the optimal feedback-control scheme known as Receding Horizon Control (RHC) to enable successful autonomous control of soil salinization. RHC uses real-time sensor measurements, physically-based state prediction models, and optimization algorithms to drive field conditions to a desired environmental state by manipulating application rate or irrigation duration/frequency. A simulation model including the Richards equation coupled to energy and solute transport equations is employed as a state estimator. Vertical multi-sensor arrays installed in the soil provide initial conditions and continuous feedback to the control scheme. An optimization algorithm determines the optimal irrigation rate or frequency subject to imposed constraints protective of soil salinization. A small-scale field test demonstrates that the RHC scheme is capable of autonomously maintaining specified salt levels at a prescribed soil depth. This finding suggests that, given an adequately structured and trained simulation model, sensor networks, and optimization algorithms can be integrated using RHC to autonomously achieve water reuse and agricultural objectives while managing soil salinization.
Current sensor networks experience many faults that hamper the ability of scientists to draw significant inferences. We develop a method to systematically identify when these faults occur so that proper corrective action can be taken. We propose an adaptable modular framework that can utilize different modeling methods and approaches to identifying trustworthy sensors. We focus on using hierarchical Bayesian space-time (HBST) modeling to model the phenomenon of interest, and use maximum a posteriors selection to identify a set of trustworthy sensors. Compared to an analogous linear autoregressive system, we achieve excellent fault detection when the HBST model accurately represents the phenomenon.
Participatory sensing tasks deployed mobile devices to form interactive, participatory sensor networks that enable public and professional users to gather, analyze and share local knowledge. Mobile Personal Sensing (MPS) is a platform for participatory sensing with which users use mobile phones to record and transmit sound, images, location, motion data, and web services to aggregate and interpret the assembled information. The data gathered through MPS is personal, as well as being potentially valuable in many aspects; it quantifies habits, routines, associations, and is easy to mine. However, for these reasons, protecting individual privacy, documenting ownership, and providing visibility of processing are important. We propose Personal Data Vault (PDV), the architecture to support these new design criteria by “auditing” all activities on the data (TraceAudit) and dynamically “re-sampling” data feeds to service providers (Adaptive Filter). The TraceAudit allows the user to track how the data is processed as well as who is using the data in order to provide transparency of data processing and foster a market of “certified” service providers. The adaptive filters govern how the data is sent from PDV to service providers in order to provide a better quality of services with minimal data using two methods: error-tolerant data sampling and anomaly detection.
At UCLA, the Center for Embedded Network Sensing (CENS) in the School of Engineering received NSF funding for a unique project titled: Women @ CENS, created to explore issues of gender equity in engineering and computer science (ECS) undergraduate research internship programs. The Women @ CENS project includes two studies: 1) an evaluation of our own CENS REU program and 2) a national study of REUs in ECS. The goals of these studies were to learn about promising practices in addressing gender equity in the REU setting from our own summer internship program, and to learn about what other REUs were doing in regards to promoting gender equity such that more women will choose to pursue advanced degrees and faculty careers in ECS. Study One utilizes the evaluation of the CENS REU over four program years to understand what has and has not worked for our female students in particular. For Study Two, we surveyed program directors of NSF funded Computer Science and Engineering REUs nationwide about espoused program goals, practices, participant demographics, and in particular, specific efforts designed to address gender inequity in these fields.
Author(s): Stauffer, Beth; Darjany, Lindsay; Coit, Dustin; Seubert, Erica; Oberg, Carl; Das, Jnaneshwar; Sukhatme, Gaurav; Caron, David | Abstract: 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.
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.
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.
Author(s): Hajj Chehade, Nabil; Nair, Sheela; Parker, Andrew; Hansen, Mark; Pottie, Greg | Abstract: 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.
