Showing papers in "Center for Embedded Network Sensing in 2008"

Participatory Privacy in Urban Sensing

Abstract: Urban sensing systems that use mobile phones enable individuals and communities to collect and share data with unprecedented speed, accuracy and granularity. But employing mobile handsets as sensor nodes poses new challenges for privacy, data security, and ethics. To address these challenges, CENS is developing design principles based upon understanding privacy regulation as a participatory process. This paper briefly reviews related literature and introduces the concept of participatory privacy regulation. PPR reframes negotiations of social context as an important part of participation in sensing-supported research. It engages participants in ethical decision-making and the meaningful negotiation of personal boundaries and identities. We use PPR to establish a set of design principles based on our application drivers.

Deriving State Machines from TinyOS Programs Using Symbolic Execution

Abstract: 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 paper, 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.

Participatory Design of Sensing Networks: Strengths and Challenges

Abstract: Participatory design (PD) involves users in all phases of design to build systems that fit user needs while simultaneously helping users understand complex systems. We argue that traditional PD techniques can benefit participatory sensing: community-based participatory research (CBPR) projects in which complex technologies, such as sensing networks using mobile phones, are the research instruments. Based on our pilot work on CycleSense, a community-based data gathering system for bicycle commuters, we discuss the benefits and challenges of PD in participatory sensing settings, and outline a method to integrate PD into the research process.

NIMSAQ: A novel system for autonomous sensing of aquatic environments

Abstract: NIMS-AQ: A novel system for autonomous sensing of aquatic environments Michael J. Stealey ∗ , Amarjeet Singh ∗ , Maxim A. Batalin ∗ , Brett Jordan † , and William J. Kaiser ∗ ∗ Department † Department of Electrical Engineering, University of California, Los Angeles of Mechanical Engineering, University of California, Los Angeles Email: stealey@ucla.edu physical and chemical parameters such as temperature, pH and solar radiation. A variety of remotely administered and autonomous systems exist for transporting water quality sensors through aquatic environments. Buoyed or moored deployment plat- forms provide one dimensional vertical profiling capabilities over long time periods at key locations [8], [9], [10]. Au- tonomous underwater vehicles (AUVs) have been used exten- sively by the oceanographic community, and more recently in lakes and river systems [11], [12]. Robotic boats [13] and Autonomous Surface Vehicles (ASVs) [14], [15] have now been used for several important aquatic applications, including monitoring the phytoplankton growth and tracking thermal plumes. Tethered systems, such as Riverboat 1 and the Rapidly Deployable Networked InfoMechanical System (NIMS-RD) [16] have been introduced recently to provide ease of deployment and accurate localization respectively. In spite of the recent advancements in technology and commercial viability, currently available systems have many constraints that preclude long-term, remote, autonomous, high-resolution monitoring in the real environment. These constraints include limited availability of energy, precise au- tonomous actuation and highly accurate localization, among others. In addition, a large team effort, involving experts from diverse domains including biology, statistics and engineering, is often required for successful completion of many compli- cated field campaigns in aquatic domain [2]. Limited cam- paign time along with the high associated resource cost (team effort and the cost of robotic and sensing systems) demand for optimized utilization of the available time in the field. In this paper, we introduce a new tethered robotic system for obtaining high-resolution observations of dynamic spatiotemporal phenomena in aquatic environments. This new system, Aquatic Networked InfoMechanical System (hereafter referred to as NIMS-AQ), was conceived based on the increasing requirements of aquatic applications following the success of several field campaigns achieved using the Rapidly Deployable Networked InfoMechanical System [16] (hereafter referred to as NIMS-RD). We describe the system design and development and demonstrate the usefulness of the system through two field sensing campaigns carried out in diverse environmental conditions. First, in the Merced River (California) with flowing water and second, sampling 1 Developed Abstract— As concern for water resource availability in- creases, so does the need for intelligent aquatic sensing appli- cations. The requirements, and complexity of such applications has also increased due to demands for: 1) broad spatial coverage and high spatial resolution monitoring, 2) capability for resolving fine scale spatiotemporal dynamics and 3) the need for rapid system deployment with semi-autonomous operation. With these criteria in mind, we present the Aquatic Networked InfoMechanical System (NIMS-AQ). NIMS-AQ was developed based on experience gained from engineering research and collaboration with aquatic scientists and environmental engi- neers during several in-field measurement campaigns [1], [2], [3]. In this paper we demonstrate the effectiveness of NIMS- AQ through two experimental sensing campaigns encompassing both river and lake environments. Each campaign is centered around critical water resource monitoring objectives such as temperature, flow and contaminant levels. Experimental results for autonomous depth profiling using a submersible sonar system as well as adaptive sampling algorithms guided by phenomena models are presented herein. The found results conform with our objectives for rapid and systematic oper- ation. Preliminary studies also indicate the systems viability for use with an Autonomous Iterative experiment Design for Environmental Applications (A-IDEA) methodology that is currently under development. The IDEA methodology [1] provides effective characterization of spatiotemporal dynamics in aquatic environments. A-IDEA, as it is to be implemented on the NIMS-AQ platform, is also described. I. I NTRODUCTION Water quality degradation is a primary concern for popu- lated and agricultural regions due to the impacts of several sources of pollution including urban runoff and agricultural drainage, among others [4], [5]. A broad class of aquatic monitoring applications require sensing an environment that displays significant heterogeneity in both space and time. As an example, river observations are useful for answering the questions pertaining to the spatiotemporal variability of velocity and water quality dynamics resulting from pollutant inputs, hydrodynamic mixing regimes, and biogeochemical cycling processes that are themselves distributed in time and space. Observations will require an increased resolution above periodic point sampling if we are to understand pro- cesses well enough to predict conditions and manage water quality at arbitrarily large scales [6]. As another example, the growth of planktonic microorganisms, that form much of the base of the food webs in freshwater and marine ecosystems, is dependent on the availability of dissolved nutrients and light. These parameters display highly dynamic spatiotempo- ral distributions [7] often due to interdependence with other by Oceanscience

Whose data do you trust? Integrity issues in the preservation of scientific data

Abstract: Integrity of content is a generic issue in curation and preservation, but has not been extensively studied in relation to scientific data. Data are now being seen as an important end product of scholarship in themselves. In this paper, we will discuss data integrity issues in relation to environmental and ecological data, and the implications of these issues on the development of data digital libraries. For users to trust and interpret the data in scientific digital libraries, they must be able to assess the integrity of those data. Criteria for data integrity vary by context, by scientific problem, by individual, and a variety of other factors. The goal of this research is to identify functional requirements for digital libraries of scientific data, encompassing both technical and social factors that can affect data integrity. Mechanisms to ensure data integrity have to be present at each stage in the data life cycle, from data collection to data preservation and curation. The implications of our research on data integrity are multi-fold for the iSchool research community, and we hope to promote discussion of these issues.

Adaptive Sampling With Multiple Mobile Robots

Abstract: Adaptive Sampling With Multiple Mobile Robots Bin Zhang and Gaurav S. Sukhatme Abstract— When a scalar field, such as temperature, is to be estimated from sensor readings corrupted by noise, the estimation accuracy can be improved by judiciously controlling the locations where the sensor readings (samples) are taken. In this paper, we solve the following problem: given a set of static sensors and a group of mobile robots equipped with the same sensors, how to determine the data collecting paths for the mobile robots so that the reconstruction error of the scalar field is minimized. In our scheme, the static sensors are used to provide an initial estimate and the mobile robots refine the estimate by taking additional samples at critical locations. Unfortunately, it is computationally expensive to search for the best set of paths that minimizes the field estimation errors and hence the field reconstruction errors as well). We propose a heuristic to find ‘good’ paths for the robots. Our approach first partitions the sensing field into equal gain subareas and then we use a single robot planning algorithm to generate a path for each robot separately. The properties of this approach are studied in simulation. Our approach also implicitly solves a multi-robot coordination/task allocation problem, where the robots are homogeneous and the size of task set might be large. I. INTRODUCTION A sensor actuator network (also a robotic sensor network), which consists of both static and mobile nodes, provides a new tool for measuring and monitoring the environment. On the one hand, with less energy consumed, the static sensor nodes are able to provide high resolution temporal sampling. On the other hand, with the ability to move, the mobile nodes (henceforth) are able to change the spatial distribution of the sensor readings leading (if running the appropriate algorithm) to a high density of readings in important areas. The key challenge is an adaptive sampling problem - come up with trajectories that the robots can follow, sampling alone which will improve the field reconstruction. In [1], we proposed an adaptive sampling algorithm for a system consisting of a set of static sensor nodes and one mobile robot, a robotic boat. The system, part of the NAMOS project at USC (http://robotics.usc.edu/ namos), is used for measuring scalar fields, such as temperature, salinity and chlorophyll concentration. We have shown [1] that by combining optimal experimental design and path planning, we are able to achieve an improved estimation performance, i.e., a lower Integrated Mean Square Error (IMSE) with the same (finite) initial energy available to the mobile robot. This work is supported in part by the National Science Fundation (NSF) under grants CNS-0325875, IIS-0133947, EIA-0121141 and grants CCR- 0120778, ANI-00331481 (via subcontract). B. Zhang binz@usc.edu and G. S. Sukhatme gaurav@usc.edu are with the Robotic Embedded Systems Laboratory, Computer Science Department, Unversity of Southern California, 941 W. 37th Place, Los Angeles, CA 90089, USA In [1], we assume that the scalar field to be reconstructed changes slowly. That is, during the time the mobile robot is sent out for a data collecting tour, the readings from the static sensors are still valid. However, this might not be true in practice since it takes a while for the mobile robot to finish a tour. One way to overcome this drawback is to use multiple mobile robots in parallel to accomplish the task. If we can generate ‘good’ paths for all the mobile robots and let them carry out the sampling task simultaneously, the speedup could be significant. Another advantage of a system with multiple mobile robots is energy efficiency. In many cases, the ideal distribution (leading to the best reconstruction of the field) of the sensor readings contains several clusters. Since normally, we already have static sensors covering the whole sensing field, the mobile robots may just need to take readings within each cluster. If only one mobile robot is deployed, it has to move between the clusters. If multiple mobile robots are used and the number of robots is more than the number of clusters, each robot only needs to stay within a cluster and the energy to move from one cluster to another could be saved. Fig. 1. One of the robotic boats used in the NAMOS project at USC. In this paper, we investigate the problem of adaptive sampling using multiple mobile robots. Specifically, given a set of static sensor nodes deployed uniformly across the sensing field, and a team of mobile robots each with the same energy, how to exploit the information collected by the static sensors and coordinate the motion of the mobile robots so that error associated with the reconstruction of the underlying scalar field is minimized. Here we assume that all the mobile robots have the same energy consumption profile and the underlying scalar field is continuous and has finite second order derivative at any point.

Achieving Participatory Privacy Regulation: Guidelines for CENS Urban Sensing

Abstract: Achieving Participatory Privacy Regulation: Guidelines for CENS Urban Sensing Katie Shilton, Jeff Burke, Deborah Estrin, Mark Hansen, Mani B. Srivastava June 25, 2008 Center for Embedded Networked Sensing University of California Los Angeles {kshilton, destrin, mbs}@ucla.edu, jburke@remap.ucla.edu, cocteau@stat.ucla.edu

{WiLSoN: The Wirelessly Linked Seismological Network and Its Application in the Middle American Subduction Experiment

Abstract: Author(s): Husker Allen; Stubailo Igor; Lukac Martin; Naik Vinayak; Guy Richard; Davis Paul; Estrin Deborah

Monolithic fabrication of three-dimensional microfluidic networks for constructing cell culture array with an integrated combinatorial mixer

Abstract: Author(s): Liu Mike C.; Ho Dean; Tai Yu-Chong | Abstract: We present a novel method to monolithically fabricate three-dimensional (3-D) microfluidic networks and based on this fabrication technology, we have constructed the first cell culture device with an integrated combinatorial mixer. The device is designed for screening the combinatorial effects of multiple compound exposure on cultured cells, and a 1 cm 1 cm proof-of-concept chip having a three-input combinatorial mixer and eight individually isolated micro culture-chambers has been fabricated. The 3-D microfluidic networks are fabricated utilizing the surface micromachining of parylene C (poly(chloro-p-xylylene)), and the monolithic method enables the device to achieve precise alignment in between microchannels and favorably obviates multilayer bonding processes. By incorporating several microfluidic "overpass" structures to allow one microfluidic channel to cross over other microfluidic channels, the combinatorial mixer is able to simultaneously generate all the combinations of the input fluidic streams for output to the microchambers. Cell culturing inside parylene C micro culture-chambers has been successfully performed, and the ability to simultaneously treat arrays of cells with different combinations of compounds has been demonstrated with experiments using three different cell stains. Our scaleable process can enable the fabrication of devices with a high-input combinatorial mixer with applications not only for high-throughput cell-based assays but also for conducting researches in combinatorial chemistry or the pharmaceutical industry. At the same time, the fabrication technique will have general applicability for building complex 3-D microfluidic devices, which can broaden the applications for current lab-on-a-chip systems.

Fixing Faults in Wireless Sensing Systems with Confidence

Abstract: This paper presents Confidence, a tool for identifying and addressing faults in wireless sensing systems. Confidence pinpoints potential sensor and network faults in real time, allowing users to validate unexpected data and address any failures in the field. By introducing a well defined, low-dimension feature space, and functions to map sensor data into this space, we are able to achieve fault detection and diagnosis with relatively simple mechanisms such as outlier detection. Users can directly modify system outcomes by altering a classification label in instances when Confidence's automated algorithm draws the wrong inference. This label is applied to all similar points in the feature space, enabling Confidence to learn from user interaction in the field. This abstraction for incorporating user knowledge provides a lightweight and easy- to-understand interface for the user, while limiting user bur- den and reducing the required a priori environmental knowledge. Confidence has performed well on real-world deployments, including one deployment of 130 sensors, replayed datasets, and network simulations. Confidence accurately detects and diagnoses at least 90% of all data, and user interaction improves it's performance.

Interactive environmental sensing: Signal and image processing challenges

Abstract: Networked embedded acoustic sensors and imagers allow scientists to observe biological and environmental phenomena at high sampling rates and multiple scales. Such sampling can create large data sets that often require some form of automated processing to extract useful information. However, to guarantee the accuracy of the data, the scientist must be included in the processing, rather than treating it as a black box, an approach we call interactive environmental sensing. In this paper we describe the challenges of such an approach and motivate it with several examples from bioacoustics, plant phenology and avian biology.

The Marginal Utility of Cooperation in Sensor Networks

Abstract: IPSN 2008 Workshop on Signal and Information Processing St. Louis, Missouri http://ipsn.acm.org/2008/ Schedule: April 21 9:00AM-5:30PM Session1: 9-11:00 AM, Chair: Moura 9:00 - 9:30AM: Statistical Inference and Interactive Statistical Mechanics, Mitter, MIT 9:30 -10:00AM: Location-free Distributed Coverage Verification in Sensor Networks, Jadbabaie, UPenn 10:00-10:30AM: Distributed Compressed Sensing, Baraniuk, Rice 10:30-11:00AM: Coffee Break Session2: 11:00-1:00 PM, Chair: Shah 11:00- 11:30PM: Clock Synchronization in Wireless Networks, Freris and Kumar, UIUC 11:30- 12:00PM: Distributed LMS for Consensus-Based Adaptive Signal Processing, Giannakis, UMN 12:00-12:30PM: Distributed Processing in Sensor Networks under Imperfect Communication, Kar and Moura 12:30 - 1:00PM: Morning Group Discussion 1:00 - 2:00PM: Lunch Break Session3: 2 - 3:30 PM, Chair: Jadbabaie 2:00-2:30PM: Abnormal Event Detection through Behavior Subtraction, Saligrama, BU 2:30-3:00PM: Decentralized Random-field Estimation for Sensor Networks Using Quantized Spatially Correlated Data and Fusion-center Feedback, Dogandzic, Iowa State 3:00-3:30PM: Linear Decentralized Estimation, Xiao, Cui, Luo (UMN) and Goldsmith (Stanford) 3:30 - 4:00PM: Coffee Break Session4 : 4:00-5:30 PM, Chair: Dogandzic 4:00 - 4:30PM: Some algorithms for sensor networks, Shah, MIT 4:30 - 5:00PM: The Marginal Utility of Cooperation in Sensor Networks, Pottie, UCLA 5:00 - 5:30PM: Afternoon Group Discusion

Optimal Spectrum Management in Multiuser Interference Channels

Abstract: In this paper, we investigate the optimal spectrum management problem in multiuser frequency selective interference channels. First, a simple pairwise condition for FDMA to be optimal is discovered: for any two among all the users, as long as the normalized cross couplings between them two are both larger than or equal to 1/2, orthogonalization between these two users is optimal for every existing user. Therefore, this single condition applies to achieving all Pareto optimal points of the rate region. Furthermore, not only is this condition sufficient, but in symmetric channels, it is also necessary for FDMA to be always optimal. When the normalized cross couplings are less than 1/2, the optimal spectrum management strategy can be a mixture of frequency sharing and FDMA, depending on users’ power constraints. We first explicitly solve the sum-rate maximization problem in two user symmetric flat channels by solving a closed form equation, providing the optimal spectrum management with a clear intuition as the optimal combination of flat FDMA and flat frequency sharing. Next, we show that this result leads to a primal domain convex optimization formulation for generalizations to frequency selective channels. Finally, we show that all the general optimization problems with n>=2 users and an arbitrary weighted sum-rate objective function in non-symmetric frequency selective channels can be solved by primal domain convex optimization with the same methodology.

Fixing Faults with Confidence

Abstract: This paper presents Confidence, a tool for identifying and addressing faults in wireless sensing systems. Confidence pinpoints potential sensor and network faults in real time, allowing users to validate unexpected data and address any failures in the field. By introducing a well defined, low-dimension feature space, and functions to map sensor data into this space, we are able to achieve fault detection and diagnosis with relatively simple mechanisms such as outlier detection. Users can directly modify system outcomes by altering a classification label in instances when Confidence's automated algorithm draws the wrong inference. This label is applied to all similar points in the feature space, enabling Confidence to learn from user interaction in the field. This abstraction for incorporating user knowledge provides a lightweight and easy-to-understand interface for the user, while limiting user burden and reducing the required a priori environmental knowledge. Confidence has performed well on real-world deployments, including one deployment of 130 sensors, replayed datasets, and network simulations. Confidence accurately detects and diagnoses at least 90% of all data, and user interaction improves it's performance.