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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.

This paper describes a new embedded networked sensor platform architecture that combines hardware and software tools providing detailed, fine-grained real-time energy usage information. We introduce the LEAP2 platform, a qualitative step forward over the previously developed LEAP and other similar platforms. LEAP2 is based on a new low power ASIC system and generally applicable supporting architecture that provides unprecedented capabilities for directly observing energy usage of multiple subsystems in real-time. Real-time observation with microsecond-scale time resolution now enables direct accounting of energy dissipation for each computing task as well as for each hardware subsystem. This new hardware architecture is exploited with our new software tools, etop and endoscope. A series of experimental investigations provide high-resolution power information in networking, storage, memory and processing for primary embedded networked sensing applications. Using these results obtained in real-time we show that for a large class of wireless sensor network nodes, there exist several interdependencies in energy consumption between different subsystems. Through the use of our measurement tools we demonstrate that by carefully selecting the system operating points, energy savings of over 60% can be achieved while retaining system performance.

Recent advancement in microsensor technology permits miniaturization of conventional physiological sensors. Combined with low-power, energy-aware embedded systems and low power wireless interfaces, theses sensors now enable patient monitoring in home and workplace environments in addition to the clinic. Low energy operation is critical for meeting long operating lifetime requirement; an energy-aware wearable system is therefore particularly beneficial to adaptively profile and manage energy utilization. Furthermore, important challenges appear as some of these important physiological sensors, such as electrocardiographs (ECG), introduce large energy demand (because of the need for high sampling rate and resolution) and limitations (due to reduced convenience of user wearability). Energy usage of the distributed sensor systems may be reduced by activating and deactivating sensors according to real-time measurement demand as well as energy consumption characteristics. Our results show that with proper adaptive measurement scheduling, an ECG signal from a subject may be needed for analysis only at certain times, such as during or after an exercise activity. This demonstrates that autonomous systems may rely on low energy cost sensors combined with real time computation to determine patient context with high certainty diagnostics and apply this information to properly schedule use of high cost sensors (e.g. ECG sensor systems).We have implemented a wearable system based on standard widely-used handheld computing hardware components. This system relies on a new software architecture and an embedded inference engine developed for theses standard platforms. The performance of the system is evaluated using experimental data sets acquired for subjects wearing this system during an exercise sequence. This same approach can be used in context-aware monitoring of diverse physiological signals in a patient’s daily life. Furthermore, a new energy-aware wearable system is introduced. It is capable of performing real-time energy profiling on major components through a convenient software interface. Exploring the techniques on how to utilize this energy information and optimize the existing context-aware algorithm is the focus of future work.

Tiered sensor networks are gaining currency. We propose a mathematical optimization based algorithm to compose data-fusion services in sensor networks with a decomposition technique to effectively load balance it among the microserver nodes in the network. We also provide a thorough evaluation of localization and a formulation for routing using this framework. In a network with LEAP2-like nodes which can switch functionality between that of a mote and that of a master, we intend to study dynamic reconfiguration algorithms to improve lifetime.

Distributed hydraulic and water quality property characterization aides in understanding a broad range of river issues including confluence and discharge mixing phenomena, groundwater-surface water exchanges, and mapping flow and temperature distributions in the context of habitat restoration efforts. In this work, we characterize the Merced-San Joaquin River confluence zone using rapidly deployable networked infomechanical systems (NIMS RD) technology. NIMS RD robotically delivers velocity (ADV) and multi-parameter water quality sensors to points in a river transect. This presentation provides an overview of the NIMS RD equipment, deployment methods, and results from a seven-day period in August, 2007, upstream and downstream transects were obtained describing velocity, temperature, electrical conductivity (EC), pH, dissolved oxygen and oxidation-reduction potential (ORP) at one upstream each for the Merced and San Joaquin Rivers and two transects approximately 100 and 400m downstream of the effluent. Velocity and water quality parameter distributions are presented for transects above and within the mixing zone of the confluence zone using high resolution raster scans. Results are discussed in terms of river mixing processes and flow and solute mass balance calculations aimed at identifying and quantifying groundwater inputs.

A broad range of embedded networked sensor (ENS) systems for critical environmental monitoring applications now require complex, high peak power dissipating sensor devices, as well as on-demand high performance computing and high bandwidth communication. Embedded computing demands for these new platforms include support for computationally intensive image and signal processing as well as optimization and statistical computing. To meet these new requirements while maintaining critical support for low energy operation, a new multiprocessor node hardware and software architecture, Low Power Energy Aware Processing (LEAP), has been developed. The LEAP architecture integrates fine-grained energy dissipation monitoring and sophisticated power control scheduling for all subsystems including sensor subsystems. The LEAP2 platform is a second generation LEAP system with even higher resolution energy monitoring as well as the unique ability to do per process and per application energy profiling via a dedicated high performance ASIC. This poster will demonstrate the hardware platform capabilities as well as the energy-aware software currently available for LEAP2.

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 instrumenting civil structures and SHMnet, for monitoring of tall buildings in Los Angeles. SHM is the process of assessing the state of health (e.g., damage) of instrumented structures from measurements. The goal of SHM is to improve safety and reliability of infrastructure systems by detecting damage before it reaches a critical state, or to allow rapid post-event assessment. 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 buildings in Los Angeles as a testbed. To manage this large-scale, multi-disciplinary goal, the work tasks are divided into several key focus areas, each with specific objectives, including the development of; a robust wireless Data Acquisition (DAQ) toolbox suitable for rapid urban deployments, a suite of state-of-the-art sensors for monitoring key structural responses including an innovative laser/photodiode for directly measuring interstory drift, and probabilistic post-event assessment algorithms based on experimental motion-damage relationships. A confluence of events, namely the local boom in tall building design and construction, the LA-DBS instrumentation requirements for tall buildings, the technology and experience of CENS and nees@UCLA, coupled with the active participation of key industrial partners, provides the synergy required to enable the proposed research.ShakeNet is a vibration sensor network designed for use in civil structures such as buildings and bridges for system identification and for identifying potential locations of damage due to earthquake motions. ShakeNet can also be used in test structures loaded to failure to characterize damage signals in waveform data such as those produced by moment-frame weld fractures. ShakeNet is a multi-tier wireless sensing system of 75 wireless nodes that can be rapidly deployed by 2-3 people on, for example, multiple floors of a large building. It will be designed to collect structural vibration measurements for up to a week from each node within the network. This portable system can be used to instrument large structures within hours immediately after an earthquake. Significant aftershocks of even moderate-size earthquakes occur for up to a week after the earthquake. What makes ShakeNet application-realistic is careful attention to the design of its data-acquisition hardware, the ShakeBoard. The ShakeBoard is a 24-bit high resolution acceleration data acquisition system that uses commercial low-noise MEMS accelerometers and conforms to specifications currently being established by the US Geological Survey for their Advanced National Seismic System (ANSS). ShakeNet’s software subsystem will be built upon Tenet; programmable wireless sensing software designed for multi-tier sensor networks. The strength of ShakeNet lies in the planned field tests in order to make it application-realistic. Future tests of ShakeNet, pending project funding, include deployments in moment-frame steel and reinforced concrete buildings, a bridge, and a dam near the San Andreas Fault.

A broad range of embedded networked sensor (ENS) systems for critical environmental monitoring applications now require complex, high peak power dissipating sensor devices, as well as on-demand high performance computing and high bandwidth communication. Embedded computing demands for these new platforms include support for computationally intensive image and signal processing as well as optimization and statistical computing. To meet these new requirements while maintaining critical support for low energy operation, a new multiprocessor node hardware and software architecture, Low Power Energy Aware Processing (LEAP), has been developed. The LEAP architecture integrates fine-grained energy dissipation monitoring and sophisticated power control scheduling for all subsystems including sensor subsystems. The LEAP2 platform is a second generation LEAP system with even higher resolution energy monitoring as well as the unique ability to do per process and per application energy profiling via a dedicated high performance ASIC. This poster will demonstrate the hardware platform capabilities as well as the energy-aware software currently available for LEAP2.

Large-scale environmental sensing, e.g., understanding microbial processes in an aquatic ecosystem, requires coordination across a multidisciplinary team of experts working closely with a robotic sensing and sampling system. We describe a human-robot team that conducted an aquatic sampling campaign in Lake Fulmor, San Jacinto Mountains Reserve, California during three consecutive site visits (May 9–11, June 19–22, and August 28–31, 2006). The goal of the campaign was to study the behavior of phytoplankton in the lake and their relationship to the underlying physical, chemical, and biological parameters. Phytoplankton form the largest source of oxygen and the foundation of the food web in most aquatic ecosystems. The reported campaign consisted of three system deployments spanning four months. The robotic system consisted of two subsystems—NAMOS (networked aquatic microbial observing systems) comprised of a robotic boat and static buoys, and NIMS-RD (rapidly deployable networked infomechanical systems) comprised of an infrastructure-supported tethered robotic system capable of high-resolution sampling in a two-dimensional cross section (vertical plane) of the lake. The multidisciplinary human team consisted of 25 investigators from robotics, computer science, engineering, biology, and statistics.We describe the lake profiling campaign requirements, the robotic systems assisted by a human team to perform high fidelity sampling, and the sensing devices used during the campaign to observe several environmental parameters. We discuss measures taken to ensure system robustness and quality of the collected data. Finally, we present an analysis of the data collected by iteratively adapting our experiment design to the observations in the sampled environment. We conclude with the plans for future deployments.

This paper reports the first application of iterative experimental design methodology for high spatiotemporal resolution characterization of river and lake aquatic systems performed using mobile robot sensing systems. Both applications involve dynamic phenomena spread over large spatial domain: 1) Characterization of contaminant concentration and flow at the confluence of two major rivers displaying dynamics due to flow of the water; and 2) Characterization of rapidly evolving biological processes such as phytoplankton dynamics in a lake system. We describe the development and application of a new general purpose method for mobile robot sensing in such environments - Iterative experiment Design for Environmental Applications (IDEA). IDEA introduces in-field adaptation of mobile robotic sensing system. Analysis of the complex spatial and temporal structures associated with each observed environment is presented. Detailed characterization of the observed environment using IDEA methodology is used as an informed prior to improve the performance of the existing adaptive experimental design approaches for mobile robotic systems - stratified adaptive sampling and hierarchical non-stationary Gaussian Processes.

Distributed, high-density spatiotemporal observations are proposed for answering many river related questions, including those pertaining to hydraulics and multi-dimensional river modeling, geomorphology, sediment transport and riparian habitat restoration. In spite of the recent advancements in technology, currently available systems have many constraints that preclude long term, remote, autonomous, high resolution monitoring in the real environment. We present here a case study of an autonomous, high resolution robotic spatial mapping of cross-sectional velocity and salt concentration in a river basin. The scientific objective of this investigation was to characterize the transport and mixing phenomena at the confluence of two distinctly different river streams - San Joaquin River and its tributary Merced River. Several experiments for analyzing the spatial and temporal trends at multiple cross-sections of the San Joaquin River were performed during the campaign from August 21-25, 2006. These include deterministic dense raster scans and in-field adapted experimental design. Preliminary analysis from these experiments illustrating the range of investigations is presented with the focus on adaptive experiments that enable sparse sampling to provide larger spatial coverage without discounting the dynamics in the phenomena. Lessons learned during the campaign are discussed to provide useful insights for similar robotic investigations in aquatic environments.

Ecological observatories are a new class of multiuser research infrastructure designed and deployed to address a broad range of continental-scale ecological questions that until only recently were not technologically feasible. These highly networked ecological observatories, spread across the United States and featuring a diverse integration of programmable sensing capabilities and remote observational functions are expected to enable a transformation in the scope of environmental research, particularly in relation to understanding how global climate shifts and local and regional land use changes will quantitatively affect the composition, structure, and dynamics of the nation’s ecosystems and services. Observing systems research focused on terrestrial ecology is one of four core research application fields of the Center for Embedded Networked Sensing, which operates an engineering and experimental test bed located at the James San Jacinto Mountains Reserve, a biological field station that is part of the University of California Natural Reserve System. This paper describes the various experimental and deployed embedded networked sensing systems at the James Reserve, and suggests how similar systems and related infrastructure will be key to meeting the engineering and science objectives of future ecological research.

The exponential progress of technology development, driven in many cases by Moore’s Law, has enabled the combination of sensing, computation and wireless communication in small, low-power devices that can be embedded directly in the physical environment. Recent research has resulted in several new classes of embedded networked sensing systems that can be rapidly distributed in the environment to study phenomena with unprecedented detail. Embedded networked sensing systems are transforming the way in which physical, biological and chemical changes are detected and quantified. These results are leading to new mechanistic understanding of the environment and, consequently, to new models and predictions for better assessment and management of environmental challenges.This white paper describes the emerging technologies used in distributed sensing systems and the opportunities these systems present for environmental management, and in particular, water quality protection. A team of faculty, students, and staff at the Center for Embedded Networked Sensing (CENS) wrote the report. CENS is a National Science Foundation sponsored Science and Technology Center, headquartered at the University of California, Los Angeles (UCLA). In addition to UCLA, the California Institute of Technology, the Riverside and Merced campuses of the University of California, and the University of Southern California are partners in the center. CENS is developing embedded networked sensing systems and applying this technology to critical scientific and social applications. The Foresight and Governance Project at the Woodrow Wilson International Center for Scholars edited and finalized this document for the U.S. Environmental Protection Agency’s Office of Water.This paper first briefly describes the potential applications of sensing systems to four common water quality management problems. This potential includes: (1) providing early warning for septic systems, (2) allowing for the trading of credits for non-point source runoff, (3) monitoring beach water quality, and (4) management of combined sewer overflows. Section 4 describes these scenarios in further detail.Section 1 provides an overview of sensors (i.e., the devices that convert environmental phenomena into an electronic response) and actuators (i.e., the devices that convert electrical signals into mechanical responses). Sensors have the potential to detect physical, chemical, biological, and radiation properties in the environment. A variety of sensors is currently available for networked environmental sensing, while others are still in early research and development phases. Physical sensors for water quality monitoring are generally the most field-ready and scalable to distributed applications, followed by chemical and then biological sensors. The costs for these sensors depend on the physical, chemical, or biological parameter of interest. Indicator sensors and event-triggering sampling can be used when direct detection sensors are not ready for field deployment. To more extensively detect environmental properties, even more sophisticated sensors and sensing strategies are needed, including: (1) hardening novel sensors types (such as lab-on-a-chip technology) to withstand harsh conditions for extended periods, and (2) devising integrated sensing systems for higher order observations, such as quantifying materials fluxes in the environment.Section 2 on Deployment Platforms discusses three new sensing system classes: static, mobile robotic, and mobile handheld. These sensing systems differ from traditional measurement systems in that sensors are attached to wireless radios that enable real-time communication of the data collected. For any particular situation, the best system class to use depends on the environment’s spatial and temporal variation. Among the three classes of sensing systems, mobile handheld systems are best used when the environmental phenomena of interest cover a broad area and do not require great spatial resolution. Static sensing systems are best used over smaller areas when high spatial resolution is not required, and mobile robotic systems are appropriate for intensive measurement of very small areas. To improve overall sensing efficiency (e.g., time or cost), adaptive sampling allows the system to dynamically adjust its measurement location or frequency to meet spatial or temporal variation in the environment. Sensing platforms can also be combined such that different platforms can provide information at different scales. This type of multi-scale system can also often help improve the efficiency of a monitoring effort. Despite the opportunities these sensing systems present, the ability to deploy them in the field can be limited by power availability and faults that interfere with communication or sensing hardware.To help address some of the challenges facing the effective implementation of sensing systems and the interpretation of the acquired data, section 3 discusses the usefulness of considering the entire “life cycle” of data in a sensing system. This life cycle consists of three distinct phases: design and deployment of the observing system; operation and monitoring; and analysis, modeling and data sharing.The final section of the report offers recommendations for future research. In spite of the substantial success in research and development activities that has given rise to existing sensing systems, relatively few have been deployed in real-world applications. The time is ripe to expand the range of applications where embedded sensing systems are used. Some of the key recommendations outlined in section 5 for novel uses of embedded sensing systems include: Sensors and Actuators • (1) Long-term research and development for sensors where new or improved detection methods are needed and (2) short-term market incentives targeted at moving already well-developed sensing technology from research prototypes (e.g., biological and chemical sensors) to commercially available products. • Long-term research to develop detection methods for carbonaceous compounds, heavy metals, large molecular mass molecules such as dissolved organic compounds and dissolved organic nitrogen compounds, pathogenic organisms, biologically-active compounds, biomarkers, and lab-on-achip sensors. • Research on methods to minimize sensor maintenance in the field. • Investments to bring prototype technologies, such as small robust nitrate sensors that can be deployed for long periods, to market in forms suitable for environmental sensing. Deployment Platforms • Investments in a range of pilot studies to determine specific deployment and analysis methodologies for target systems (e.g., septic system or sewage discharge monitoring). • Definition of requirements of large scale uses of the technology to encourage the production of user-friendly systems. The Data Life Cycle • The encouragement of pilot deployments to test and refine data management tasks for specific applications. • Continued research and testing of tools to improve system robustness and ensure high-quality data. • Additional focus on the integration of sensing systems with external data sources and third-party applications, especially map-based visualization with tools for both rigorous GIS techniques and more public friendly web applications. Training • Training at multiple levels (school systems and professional development) to ensure that a ready workforce exists that is prepared to use these new sensing technologies.Embedded networked sensing systems will form a critical infrastructure resource for society—they will monitor and collect information on such diverse subjects as plankton colonies, endangered species, soil and air contaminants, medical patients, and buildings, bridges and other manmade structures. Investments in further research to help bring the sensing technologies discussed in this report into practice will transform the way we monitor and manage the health of our natural resources and predict and respond to crises.

Many environmental applications require high temporal frequency (rapidly changing) and spatially distributed phenomena to be sampled with high fidelity. This requires mobile sensing elements to perform guided sampling in regions of high variability. We propose a multiscale approach for efficiently sampling such phenomena. This approach introduces a hierarchy of sensors according to the sampling fidelity, spatial coverage, and mobility characteristics. In this paper, we report the development of a two-tier multiscale system where information from a low-fidelity, high spatial (global) sensor actuates a mobile robotic node, carrying a high-fidelity, low spatial coverage (spot measurement) sensor, to perform guided sampling in the regions of high phenomenon variability. As a case study of the proposed multiscale paradigm, we investigated the spatiotemporal distribution of the light intensity in a forest understory. The performance of the multiscale approach is verified in simulation and on a physical system. Results suggest that our approach is adequate for the problem of high-frequency spatiotemporal phenomena sampling and significantly outperforms traditional sampling approaches such as a raster scan.

Increasing demands on water supplies, along with concerns about non-point source pollution, and water quality–based ecological factors all point to the need for observing stream flow perturbations and pollutant discharges at higher resolution than has been practical until now. This work presents a rapidly deployable Networked Infomechanical System (NIMS RD) technology for observing spatiotemporal variability in hydraulic and chemical properties across stream channels. NIMS RD is comprised of two supporting towers and a suspension cable delivering power and Internet connectivity for controlling and actuating the tram-like NIMS unit. The NIMS unit is capable of raising and lowering a payload of sensors, allowing a preprogrammed or data-actuated adaptive scan to be completed across a stream channel. In this paper, NIMS RD is demonstrated in two relevant cases: (1) elucidating spatiotemporal variations in nutrients and other biologically significant stream constituents in Medea Creek, a small urban stream in Southern California; and (2) using high-resolution synoptic sampling of steady velocity and salinity distributions across the San Joaquin River in Central California to provide quantitative salt load estimates. For Medea Creek, temperature and specific conductivity (SC) exhibited varying cross-sectional patterns throughout each of three 24 hour scans carried out over three summer months. Both temperature and SC displayed repeating sinusoidal diel fluctuations independent of the spatial variation. For each of the months the cross-sectional variation was less during the late nighttime and morning hours than during the afternoon and early nighttime hours. For the San Joaquin River, high-resolution velocity distributions from NIMS RD were successfully reproduced in separate deployments and quantitatively matched stage-based volumetric flow rates at the site. The product of the velocity and associated SC distributions yielded total salt load estimates similar to previously reported values, but no basis for direct comparison was available.

In many sensing applications, including environmental monitoring, measurement systems must cover a large space with only limited sensing resources. One approach to achieve required sensing coverage is to use robots to convey sensors within this space.Planning the motion of these robots – coordinating their paths in order to maximize the amount of information collected while placing bounds on their resources (e.g., path length or energy capacity) – is a NP-hard problem. In this paper, we present an efficient path planning algorithm that coordinates multiple robots, each having a resource constraint, to maximize the “informativeness” of their visited locations. In particular, we use a Gaussian Process to model the underlying phenomenon, and use the mutual information between the visited locations and remainder of the space to characterize the amount of information collected. We provide strong theoretical approximation guarantees for our algorithm by exploiting the submodularity property of mutual information. In addition, we improve the efficiency of our approach by extending the algorithm using branch and bound and a region-based decomposition of the space. We provide an extensive empirical analysis of our algorithm, comparing with existing heuristics on datasets from several real world sensing applications.

This demonstration will present a radio testbed that is being used to characterize the quality of directional peer-to-peer wireless communications in wilderness environments.

Increasing demands on water supplies, non-point source pollution, and water quality-based ecological concerns all point to the need for observing stream flow perturbations and pollutant discharges at higher resolution than was practical in the past. This work presents the results from a test of a rapidly deployable river observational approach consisting of four components: (1) existing geospatial data and federal, state, and private river gauging infrastructure for identifying key river reaches and critical sampling times, (2) human- actuated sensor deployments for broad spatial characterization of the targeted river reach, (3) stationary sensors embedded in the river and its sediments for longer term spatiotemporal observations within the targeted reach, and (4) the robotic Networked Infomechanical System (NIMS RD) for high resolution scanning of spatiotemporal hydraulic and chemical properties at specific points along the reach. The approach is demonstrated for a test bed deployment at the confluence of the Merced and San Joaquin Rivers in Central California. After identifying a suitable reach for the test deployment, a network of on-line gauging stations, accessed through the California Data Exchange Center (CDEC), is used to coordinate the timing of the subsequent three deployment aspects based on flow and river stage forecasts. Kayak-mounted sonar and water quality sensors are used to rapidly survey the test zone bathymetry and basic water quality parameters (temperature, salinity). Results from the rapid survey are then used to guide locations of the sediment- anchored sensor arrays (javelins) which include temperature, water pressure (depth) and water quality sensors distributed vertically at screened intervals. The NIMS RD is comprised of two supporting towers and a suspension cable delivering power and Internet connectivity for controlling and actuating the tram-like NIMS unit. The NIMS unit is capable of raising and lowering a payload of sensors, allowing a preprogrammed or data-actuated adaptive scan to be completed across a stream channel. In this work, NIMS RD is deployed to provide high resolution synoptic sampling of velocity, electrical conductivity (EC), nitrate, ammonium, pH, and temperature distributions across the San Joaquin River channel in the confluence zone. Results from the different aspects of the test deployment are presented and discussed in the context of potential science and engineering applications of this multiscale networked sensing strategy.

Wireless sensor networks (WSNs) are a relatively new and rapidly developing technology; they have a wide range of applications including environmental monitoring, agriculture, and public health. Shared technology is a common usage model for technology adoption in developing countries. WSNs have great potential to be utilized as a shared resource due to their on-board processing and ad-hoc networking capabilities, however their deployment as a shared resource requires that the technical community first address several challenges. The main challenges include enabling sensor portability—the frequent movement of sensors within and between deployments, and rapidly deployable systems—systems that are quick and simple to deploy. We first discuss the feasibility of using sensor networks as a shared resource, and then describe our research in addressing the various technical challenges that arise in enabling such sensor portability and rapid deployment. We also outline our experiences in developing and deploying water quality monitoring wireless sensor networks in Bangladesh and California.

Monitoring environmental phenomena by distributed sensor sampling confronts the challenge of unpredictable variability in the spatial distribution of phenomena often coupled with demands for a high spatial sampling rate. The introduction of actuation-enabled robotics sensors permits a system to optimize the sampling distribution through runtime adaptation. However, such systems must efficiently dispense sampling points or otherwise suffer from poor temporal response. In this paper we propose and characterize an active modeling system. In our approach, as the robotic sensor acquires measurement samples of the environment, it builds a model of the phenomenon. Our algorithm is based on an incremental optimization process where the robot supports a continuous, iterative process of 1) collecting samples with maximal coverage in the design space, 2) building the environmental model 3) predicting sampling point locations that contribute the greatest certainty regarding the phenomenon 4) and sampling the environment based on a combined measure of information gain and navigation and sampling cost. This can provide significant reductions in the magnitude of field estimation error with a modest navigational trajectory time. We evaluate our algorithm through a simulation, using a combination of static and mobile sensors sampling light illumination field.

Monitoring of environmental phenomena with embedded networked sensing confronts the challenges of both unpredictable variability in the spatial distribution of phenomena, coupled with demands for a high spatial sampling rate in three dimensions. For example, low distortion mapping of critical solar radiation properties in forest environments may require two-dimensional spatial sampling rates of greater than 10 samples/m2 over transects exceeding 1000 m2 . Clearly, adequate sampling coverage of such a transect requires an impractically large number of sensing nodes. A new approach, Networked Infomechanical System (NIMS), has been introduced to combine autonomous-articulated and static sensor nodes enabling sufficient spatiotemporal sampling density over large transects to meet a general set of environmental mapping demands.This paper describes our work on a critical parts of NIMS, the Task Allocation module. We present our methodologies and the two basic greedy Task Allocation policies - based on time of the task arrival (Time policy) and distance from the robot to the task (Distance policy). We present results from NIMS deployed in a forest reserve and from a lab testbed. The results show that both policies are adequate for the task of spatiotemporal sampling, but also complement each other. Finally, we suggest the future direction of research that would both help us better quantify the performance of our system and create more complex policies combining time, distance, information gain, etc.

Distributed embedded sensor networks are now being successfully deployed in environmental monitoring of natural phenomena as well as for applications in commerce and physical security. While substantial progress in sensor network performance has appeared, new challenges have also emerged as these systems have been deployed in the natural environment. First, in order to achieve minimum sensing fidelity performance, the rapid spatiotemporal variation of environmental phenomena requires impractical deployment densities. The presence of obstacles in the environment introduces sensing uncertainty and degrades the performance of sensor fusion systems in particular for the many new applications of image sensing. The physical obstacles encountered by sensing may be circumvented by a new mobile sensing method or Networked Infomechanical Systems (NIMS). NIMS integrates distributed, embedded sensing and computing systems with infrastructure-supported mobility. NIMS now includes coordinated mobility methods that exploits adaptive articulation of sensor perspective and location as well as management of sensor population to provide the greatest certainty in sensor fusion results. The architecture, applications, and implementation of NIMS will be discussed here. In addition, results of environmentally-adaptive sampling, and direct measurement of sensing uncertainty will be described.

Monitoring environmental phenomena by distributed sensor sampling confronts the challenge of unpredictable variability in the spatial distribution of phenomena often coupled with demands for a high spatial sampling rate. The introduction of actuation-enabled robotics sensors permits a system to optimize the sampling distribution through runtime adaptation. However, such systems must efficiently dispense sampling points or otherwise suffer from poor temporal response. In this paper we propose and characterize an active modeling system. In our approach, as the robotic sensor acquires measurement samples of the environment, it builds a model of the phenomenon. Our algorithm is based on an incremental optimization process where the robot supports a continuous, iterative process of 1) collecting samples with maximal coverage in the design space, 2) building the environmental model 3) predicting sampling point locations that contribute the greatest certainty regarding the phenomenon 4) and sampling the environment based on a combined measure of information gain and navigation and sampling cost. This can provide significant reductions in the magnitude of field estimation error with a modest navigational trajectory time. We evaluate our algorithm through a simulation, using a combination of static and mobile sensors sampling light illumination field.

Distributed embedded sensor networks are now being successfully deployed in environmental monitoring of natural phenomena as well as for applications in commerce and physical security. Distributed architectures have been developed for cooperative detection, scalable data transport, and other capabilities and services. However, the complexity of environmental phenomena has introduced a new set of challenges related to sensing uncertainty associated with the unpredictable presence of obstacles to sensing that appear in the environment. These obstacles may dramatically reduce the effectiveness of distributed monitoring. Thus, a new distributed, embedded, computing attribute, self-awareness, must be developed and provided to distributed sensor systems. Selfawareness must provide the ability for a deployed system to autonomously detect and reduce its own sensing uncertainty. The physical constraints encountered by sensing require physical reconfiguration for detection and reduction of sensing uncertainty. Networked Infomechanical Systems (NIMS) consisting of distributed, embedded computing systems provides autonomous physical configuration through controlled mobility. The requirements that lead to NIMS, the implementation of NIMS technology, and its first applications are discussed here.

A key challenge in sensor networks is ensuring the sustainability of the system at the required performance level, in an autonomous manner. Sustainability is a major concern because of severe resource constraints in terms of energy, bandwidth and sensing capabilities in the system. In this paper, we envision the use of a new design dimension to enhance sustainability of in sensor networks—the use of controlled mobility. We argue that this capability can alleviate resource limitations and improve system performance by adapting to deployment demands. While opportunistic use of external mobility has been considered before, the use of controlled mobility is largely unexplored. We also outline the research issues associated with effectively utilizing this new design dimension. Two system prototypes are described to present first steps towards realizing the proposed vision.

Monitoring of environmental phenomena with embedded networked sensing confronts the challenges of both unpredictable variability in the spatial distribution of phenomena, coupled with demands for a high spatial sampling rate in three dimensions. For example, low distortion mapping of critical solar radiation properties in forest environments may require two-dimensional spatial sampling rates of greater than 10 samples/m² over transects exceeding 1000 m². Clearly, adequate sampling coverage of such a transect requires an impractically large number of sensing nodes. This paper describes a new approach where the deployment of a combination of autonomous-articulated and static sensor nodes enables sufficient spatiotemporal sampling density over large transects to meet a general set of environmental mapping demands.To achieve this we have developed an embedded networked sensor architecture that merges sensing and articulation with adaptive algorithms that are responsive to both variability in environmental phenomena discovered by the mobile sensors and to discrete events discovered by static sensors. We begin by describing the class of important driving applications, the statistical foundations for this new approach, and task allocation. We then describe our experimental implementation of adaptive, event aware, exploration algorithms, which exploit our wireless, articulated sensors operating with deterministic motion over large areas. Results of experimental measurements and the relationship among sampling methods, event arrival rate, and sampling performance are presented.

The performance of a sensor network may be best judged by the quality of application specific information return. The actual sensing performance of a deployed sensor network depends on several factors which cannot be accounted at design time, such as environmental obstacles to sensing. We propose the use of mobility to overcome the effect of unpredictable environmental influence and to adapt to run time dynamics. Now, mobility with its dependencies such as precise localization and navigation is expensive in terms of hardware resources and energy constraints, and may not be feasible in compact, densely deployed and widespread sensor nodes. We present a method based on low complexity and low energy actuation primitives which are feasible for implementation in sensor networks. We prove how these primitives improve the detection capabilities with theoretical analysis, extensive simulations and real world experiments. The significant coverage advantage recurrent in our investigation justifies our own and other parallel ongoing work in the implementation and refinement of self-actuated systems.

Susanne Roas,1 Felix Bernhart,2 Michael Schwarz,3 Walter Kaiser,4 Georg Noll5 1Department of Internal Medicine, University Hospital, Zurich, 2Private Practice, Biberist, 3Ambulatorium Wiesendamm, Basel, 4Healthworld (Schweiz) AG, Steinhausen, 5HerzKlinik Hirslanden, Zurich, Switzerland Background: Most hypertensive patients need more than one substance to reach their target blood pressure (BP). Several clinical studies indicate the high efficacy of antihypertensive combinations, and recent guidelines recommend them in some situations even as initial therapies. In general practice they seem widespread, but only limited data are available on their effectiveness under the conditions of everyday life. The objectives of this survey among Swiss primary care physicians treating hypertensive patients were: to know the frequency of application of different treatment modalities (monotherapies, free individual combinations, single-pill combinations); to see whether there are relationships between prescribed treatment modalities and patient characteristics, especially age, treatment duration, and comorbidities; and to determine the response rate (percentage of patients reaching target BP) of different treatment modalities under the conditions of daily practice. Methods: This cross-sectional, observational survey among 228 randomly chosen Swiss primary care physicians analyzed data for 3,888 consecutive hypertensive patients collected at one single consultation. Results: In this survey, 31.9% of patients received monotherapy, 41.2% two substances, 20.9% three substances, and 4.7% more than three substances. By combination mode, 34.9% took free individual combinations and 30.0% took fixed-dose single-pill combinations. Combinations were more frequently given to older patients with a long history of hypertension and/or comorbidities. In total, 67.8% of patients achieved their BP target according to their physician's judgment. When compared, single-pill combinations were associated with a higher percentage of patients achieving target BP than free individual combinations and monotherapies for the total sample and for patients with comorbidity. Conclusion: Antihypertensive combination therapy was widely used in Swiss primary care practices. The number of prescribed substances depended on age, treatment duration, and type and number of comorbidities. Although the response rate was generally modest under the conditions of daily practice, it was higher for single-pill combinations than for monotherapies and free individual combinations. Further studies are needed to confirm these observations. Keywords: hypertension, combination therapy, comorbidity, single-pill combination, fixed-dose combination