Em* is a software environment for developing and deploying Wireless Sensor Network (WSN) applicat... more Em* is a software environment for developing and deploying Wireless Sensor Network (WSN) applications on Linux-class hardware platforms (called "Microservers"). Em* consists of libraries that implement message-passing IPC primitives, tools that support simulation, emulation, and visualization of live systems, both real and simulated, and services that support for networking, sensing, and time synchronization. While Em*'s design has favored ease of use and modularity over efficiency, the resulting increase in overhead has not been an impediment to any of our current projects. 2 This work was made possible with support from the NSF Cooperative Agreement CCR-0120778, and the UC MICRO program (grant 01-031) with matching funds from Intel Corp. Additional support was provided by the DARPA NEST program (the "GALORE" project, grant F33615-01-C-1906). 2.3 Em* Device Support Em* includes native support for a number of devices, including sensors, and radio hardware.
Recent work in wireless embedded networked systems has followed heterogeneous designs, incorporat... more Recent work in wireless embedded networked systems has followed heterogeneous designs, incorporating a mixture of elements from extremely constrained 8-or 16-bit "Motes" to less resourceconstrained 32-bit embedded "Microservers". Emstar is a software environment for developing and deploying complex applications on such heterogeneous networks. Emstar is designed to leverage the additional resources of Microservers by trading-off some performance for system robustness in sensor network applications. It enables fault isolation, fault tolerance, system visiblity, in-field debugging, and resource sharing across multiple applications. In order to accomplish these objectives, Emstar is designed to run as a multi-process system and consists of libraries that implement message-passing IPC primitives, services that support networking, sensing, and time synchronization, and tools that support simulation, emulation, and visualization of live systems, both real and simulated. We evaluate this work by discussing the Acoustic ENSBox, a platform for distributed acoustic sensing that we built using Emstar. We show that by leveraging existing Emstar services, we are able to significantly reduce development time while achieving a high degree of robustness. We also show that a sample application was developed much more quickly on this platform than it would have been otherwise.
Em* is a software environment for developing and deploying Wireless Sensor Network (WSN) applicat... more Em* is a software environment for developing and deploying Wireless Sensor Network (WSN) applications on Linux-class hardware platforms (called "Microservers"). Em* consists of libraries that implement message-passing IPC primitives, tools that support simulation, emulation, and visualization of live systems, both real and simulated, and services that support for networking, sensing, and time synchronization. While Em*'s design has favored ease of use and modularity over efficiency, the resulting increase in overhead has not been an impediment to any of our current projects. 2 This work was made possible with support from the NSF Cooperative Agreement CCR-0120778, and the UC MICRO program (grant 01-031) with matching funds from Intel Corp. Additional support was provided by the DARPA NEST program (the "GALORE" project, grant F33615-01-C-1906). 2.3 Em* Device Support Em* includes native support for a number of devices, including sensors, and radio hardware.
Journal of Signal Processing Systems, Nov 26, 2008
Field biologists use animal sounds to discover the presence of individuals and to study their beh... more Field biologists use animal sounds to discover the presence of individuals and to study their behavior. Collecting bio-acoustic data has traditionally been a difficult and time-consuming process in which individual researchers use portable microphones to record sounds while taking notes of their own detailed observations. The recent development of new deployable acoustic sensor platforms presents opportunities to develop automated tools for bio-acoustic field research. In this work, we implement an AML-based source localization algorithm, and use it to localize marmot alarm-calls. We assess the performance of these techniques based on results from two field experiments: (1) a controlled test of direction-of-arrival (DOA) accuracy using a pre-recorded source signal, and (2) an experiment to detect and localize actual animals in their habitat, with a comparison to ground truth gathered from human observations. Although small arrays yield ambiguities from spatial aliasing of high frequency signals, we show that these ambiguities are readily eliminated by proper bearing crossings of the DOAs from several arrays. These results show that the AML source localization algorithm can be used to localize actual animals in their natural habitat, using a platform that is practical to deploy.
We will demonstrate the operation of the Acoustic Embedded Networked Sensing Box (ENSBox), a plat... more We will demonstrate the operation of the Acoustic Embedded Networked Sensing Box (ENSBox), a platform for prototyping rapid-deployable distributed acoustic sensing systems. The ENSBox is a Linux-based acoustic sensing system with and integrated, high precision self-calibration facility sets it apart from other platforms. This selfcalibration is precise enough to support acoustic source localization applications in complex, realistic environments: e.g., 5
The twentieth century ended with the vision of smart dust: a network of wirelessly connected devi... more The twentieth century ended with the vision of smart dust: a network of wirelessly connected devices whose size would match that of a dust particle, each one a se- containedpackageequippedwithsensing,computation,communication,andpower. Smart dust held the promise to bridge the physical and digital worlds in the most unobtrusive manner, blending together realms that were previously considered well separated. Applications involved scattering hundreds, or even thousands, of smart dust devices to monitor various environmental quantities in scenarios ranging from habitat monitoring to disaster management. The devices were envisioned to se- organize to accomplish their task in the most ef?cient way. As such, smart dust would become a powerful tool, assisting the daily activities of scientists and en- neers in a wide range of disparate disciplines. Wireless sensor networks (WSNs), as we know them today, are the most no- worthy attempt at implementing the smart dust vision. In the last decade, this ?eld has seen a fast-growing investment from both academia and industry. Signi?cant ?nancial resources and manpower have gone into making the smart dust vision a reality through WSNs. Yet, we still cannot claim complete success. At present, only specialist computerscientists or computerengineershave the necessary background to walk the road from conception to a ?nal, deployed, and running WSN system.
The area of sensor networks promises to support the biological and physical sciences by enabling ... more The area of sensor networks promises to support the biological and physical sciences by enabling measurements that were previously impossible. This is accomplished by pushing intelligence into the network and closer to the sensors, enabling sensing to be accomplished at much higher scales and densities with lower cost. Recently, interest in acoustic sensing problems has increased, including the localization and monitoring of birds, wolves, and other species; as well as of localization of electronic devices themselves. This has spurred the development of a rapidly-deployable distributed acoustic sensing platform. A key problem in the development of this platform is the acoustic array calibration problem, which estimates the locations and orientations of a distributed collection of acoustic sensors. We present a system composed of a set of independent acoustic nodes that automatically determines calibration parameters including the relative location and orientation (X, Y, Z, Θ) of each array. These relative coordinates are then fitted to one or more survey points to relate the relative coordinates to a physical map. The application that computes these estimates is itself a distributed sensing application. In this work we present a solution to this position estimation problem, demonstrating a complete vertical application built above a stack of re-usable system components and distributed services, implemented on a deployable embedded hardware platform. We describe: the hardware platform itself; Emstar, a software framework for developing complex embedded system software; a time-synchronized sampling layer; a multihop reliable multicast coordination primitive; a time-of-flight acoustic ranging and direction-of-arrival (DOA) estimation layer; and the top-level application that estimates the position and orientation of each array. We present the results of controlled tests of the ranging and DOA estimation system, as well as the results of deployment experiments in both an urban environment and a forested environment. These results demonstrate that our system outperforms other similar systems, and that it can achieve the sufficient accuracy for anticipated applications, such as bird localization.
This document is the author's post-print version of the journal article, incorporating any revisi... more This document is the author's post-print version of the journal article, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.
Journal of the Acoustical Society of America, May 1, 2008
Terrestrial bioacoustic census is a difficult problem because of propagation characteristics, obs... more Terrestrial bioacoustic census is a difficult problem because of propagation characteristics, obstructions, the diversity of bioacoustic sources, and the impact of noise. To address this problem we have developed VoxNet, a complete hardware and software platform for distributed acoustic monitoring applications. Each VoxNet node is a portable, self-contained processor with a small four-channel acoustic array. Using a distributed set of VoxNet nodes, a forested habitat can be monitored and the behavior of animals can be recorded and analyzed acoustically. In this work we present our experiences applying VoxNet to bioacoustic census. This work is based on data collected using the system during a deployment in Chiapas, Mexico at the Chajul Biological Field Station. The Chajul station is located in a region of dense rain forest and is home to Mexico's most diverse ecosystem. Using VoxNet in this harsh environment, we obtained census estimates based on observation of bird calls.
1. Animals produce sounds for diverse biological functions such as defending territories, attract... more 1. Animals produce sounds for diverse biological functions such as defending territories, attracting mates, deterring predators, navigation, finding food and maintaining contact with members of their social group. Biologists can take advantage of these acoustic behaviours to gain valuable insights into the spatial and temporal scales over which individuals and populations interact. Advances in bioacoustic technology, including the development of autonomous cabled and wireless recording arrays, permit data collection at multiple locations over time. These systems are transforming the way we study individuals and populations of animals and are leading to significant advances in our understandings of the complex interactions between animals and their habitats. 2. Here, we review questions that can be addressed using bioacoustic approaches, by providing a primer on technologies and approaches used to study animals at multiple organizational levels by ecologists, behaviourists and conservation biologists. 3. Spatially dispersed groups of microphones (arrays) enable users to study signal directionality on a small scale or to locate animals and track their movements on a larger scale. 4. Advances in algorithm development can allow users to discriminate among species, sexes, age groups and individuals. 5. With such technology, users can remotely and non-invasively survey populations, describe the soundscape, quantify anthropogenic noise, study species interactions, gain new insights into the social dynamics of sound-producing animals and track the effects of factors such as climate change and habitat fragmentation on phenology and biodiversity. 6. There remain many challenges in the use of acoustic monitoring, including the difficulties in performing signal recognition across taxa. The bioacoustics community should focus on developing a
Field biologists use animal sounds to discover the presence of individuals and to study their beh... more Field biologists use animal sounds to discover the presence of individuals and to study their behavior. Collecting bio-acoustic data has traditionally been a difficult and time-consuming process in which individual researchers use portable microphones to record sounds while taking notes of their own detailed observations. The recent development of new deployable acoustic sensor platforms presents opportunities to develop automated tools for bio-acoustic field research. In this work, we implement an AML-based source localization algorithm, and use it to localize marmot alarm-calls. We assess the performance of these techniques based on results from two field experiments: (1) a controlled test of direction-of-arrival (DOA) accuracy using a pre-recorded source signal, and (2) an experiment to detect and localize actual animals in their habitat, with a comparison to ground truth gathered from human observations. Although small arrays yield ambiguities from spatial aliasing of high frequency signals, we show that these ambiguities are readily eliminated by proper bearing crossings of the DOAs from several arrays. These results show that the AML source localization algorithm can be used to localize actual animals in their natural habitat, using a platform that is practical to deploy.
... Use commas. Citation. Lewis Girod; Thanos Stathopoulos; Nithya Ramanathan; Eric Osterweil; To... more ... Use commas. Citation. Lewis Girod; Thanos Stathopoulos; Nithya Ramanathan; Eric Osterweil; Tom Schoellhammer; & Deborah Estrin. (2004). Tools for Deployment and Simulation of Heterogeneous Sensor Networks. UC Los Angeles: Center for Embedded Network Sensing. ...
We present the design, implementation, and evaluation of the Acoustic Embedded Networked Sensing ... more We present the design, implementation, and evaluation of the Acoustic Embedded Networked Sensing Box (ENSBox), a platform for prototyping rapid-deployable distributed acoustic sensing systems, particularly distributed source localization. Each ENSBox integrates an ARM processor running Linux and supports key facilities required for source localization: a sensor array, wireless network services, time synchronization, and precise self-calibration of array position and orientation. The ENSBox's integrated, high precision self-calibration facility sets it apart from other platforms. This self-calibration is precise enough to support acoustic source localization applications in complex, realistic environments: e.g., 5 cm average 2D position error and 1.5 degree average orientation error over a partially obstructed 80x50 m outdoor area. Further, our integration of array orientation into the position estimation algorithm is a novel extension of traditional multilateration techniques. We present the result of several different test deployments, measuring the performance of the system in urban settings, as well as forested, hilly environments with obstructing foliage and 20-30 m distances between neighboring nodes.
k dimensional space, two sensor nodes i (x 1i , x 2i ,…, x ki) and j (x 1j , x 2j ,…, x kj) have ... more k dimensional space, two sensor nodes i (x 1i , x 2i ,…, x ki) and j (x 1j , x 2j ,…, x kj) have measured distance d ij , Linear/nonlinear optimization-based formulation OF: min/max F = M(ε ij) for pairs of nodes i and j that have measured distance d ij .
Em* is a software environment for developing and deploying Wireless Sensor Network (WSN) applicat... more Em* is a software environment for developing and deploying Wireless Sensor Network (WSN) applications on Linux-class hardware platforms (called "Microservers"). Em* consists of libraries that implement message-passing IPC primitives, tools that support simulation, emulation, and visualization of live systems, both real and simulated, and services that support for networking, sensing, and time synchronization. While Em*'s design has favored ease of use and modularity over efficiency, the resulting increase in overhead has not been an impediment to any of our current projects. 2 This work was made possible with support from the NSF Cooperative Agreement CCR-0120778, and the UC MICRO program (grant 01-031) with matching funds from Intel Corp. Additional support was provided by the DARPA NEST program (the "GALORE" project, grant F33615-01-C-1906). 2.3 Em* Device Support Em* includes native support for a number of devices, including sensors, and radio hardware.
Recent work in wireless embedded networked systems has followed heterogeneous designs, incorporat... more Recent work in wireless embedded networked systems has followed heterogeneous designs, incorporating a mixture of elements from extremely constrained 8-or 16-bit "Motes" to less resourceconstrained 32-bit embedded "Microservers". Emstar is a software environment for developing and deploying complex applications on such heterogeneous networks. Emstar is designed to leverage the additional resources of Microservers by trading-off some performance for system robustness in sensor network applications. It enables fault isolation, fault tolerance, system visiblity, in-field debugging, and resource sharing across multiple applications. In order to accomplish these objectives, Emstar is designed to run as a multi-process system and consists of libraries that implement message-passing IPC primitives, services that support networking, sensing, and time synchronization, and tools that support simulation, emulation, and visualization of live systems, both real and simulated. We evaluate this work by discussing the Acoustic ENSBox, a platform for distributed acoustic sensing that we built using Emstar. We show that by leveraging existing Emstar services, we are able to significantly reduce development time while achieving a high degree of robustness. We also show that a sample application was developed much more quickly on this platform than it would have been otherwise.
Em* is a software environment for developing and deploying Wireless Sensor Network (WSN) applicat... more Em* is a software environment for developing and deploying Wireless Sensor Network (WSN) applications on Linux-class hardware platforms (called "Microservers"). Em* consists of libraries that implement message-passing IPC primitives, tools that support simulation, emulation, and visualization of live systems, both real and simulated, and services that support for networking, sensing, and time synchronization. While Em*'s design has favored ease of use and modularity over efficiency, the resulting increase in overhead has not been an impediment to any of our current projects. 2 This work was made possible with support from the NSF Cooperative Agreement CCR-0120778, and the UC MICRO program (grant 01-031) with matching funds from Intel Corp. Additional support was provided by the DARPA NEST program (the "GALORE" project, grant F33615-01-C-1906). 2.3 Em* Device Support Em* includes native support for a number of devices, including sensors, and radio hardware.
Journal of Signal Processing Systems, Nov 26, 2008
Field biologists use animal sounds to discover the presence of individuals and to study their beh... more Field biologists use animal sounds to discover the presence of individuals and to study their behavior. Collecting bio-acoustic data has traditionally been a difficult and time-consuming process in which individual researchers use portable microphones to record sounds while taking notes of their own detailed observations. The recent development of new deployable acoustic sensor platforms presents opportunities to develop automated tools for bio-acoustic field research. In this work, we implement an AML-based source localization algorithm, and use it to localize marmot alarm-calls. We assess the performance of these techniques based on results from two field experiments: (1) a controlled test of direction-of-arrival (DOA) accuracy using a pre-recorded source signal, and (2) an experiment to detect and localize actual animals in their habitat, with a comparison to ground truth gathered from human observations. Although small arrays yield ambiguities from spatial aliasing of high frequency signals, we show that these ambiguities are readily eliminated by proper bearing crossings of the DOAs from several arrays. These results show that the AML source localization algorithm can be used to localize actual animals in their natural habitat, using a platform that is practical to deploy.
We will demonstrate the operation of the Acoustic Embedded Networked Sensing Box (ENSBox), a plat... more We will demonstrate the operation of the Acoustic Embedded Networked Sensing Box (ENSBox), a platform for prototyping rapid-deployable distributed acoustic sensing systems. The ENSBox is a Linux-based acoustic sensing system with and integrated, high precision self-calibration facility sets it apart from other platforms. This selfcalibration is precise enough to support acoustic source localization applications in complex, realistic environments: e.g., 5
The twentieth century ended with the vision of smart dust: a network of wirelessly connected devi... more The twentieth century ended with the vision of smart dust: a network of wirelessly connected devices whose size would match that of a dust particle, each one a se- containedpackageequippedwithsensing,computation,communication,andpower. Smart dust held the promise to bridge the physical and digital worlds in the most unobtrusive manner, blending together realms that were previously considered well separated. Applications involved scattering hundreds, or even thousands, of smart dust devices to monitor various environmental quantities in scenarios ranging from habitat monitoring to disaster management. The devices were envisioned to se- organize to accomplish their task in the most ef?cient way. As such, smart dust would become a powerful tool, assisting the daily activities of scientists and en- neers in a wide range of disparate disciplines. Wireless sensor networks (WSNs), as we know them today, are the most no- worthy attempt at implementing the smart dust vision. In the last decade, this ?eld has seen a fast-growing investment from both academia and industry. Signi?cant ?nancial resources and manpower have gone into making the smart dust vision a reality through WSNs. Yet, we still cannot claim complete success. At present, only specialist computerscientists or computerengineershave the necessary background to walk the road from conception to a ?nal, deployed, and running WSN system.
The area of sensor networks promises to support the biological and physical sciences by enabling ... more The area of sensor networks promises to support the biological and physical sciences by enabling measurements that were previously impossible. This is accomplished by pushing intelligence into the network and closer to the sensors, enabling sensing to be accomplished at much higher scales and densities with lower cost. Recently, interest in acoustic sensing problems has increased, including the localization and monitoring of birds, wolves, and other species; as well as of localization of electronic devices themselves. This has spurred the development of a rapidly-deployable distributed acoustic sensing platform. A key problem in the development of this platform is the acoustic array calibration problem, which estimates the locations and orientations of a distributed collection of acoustic sensors. We present a system composed of a set of independent acoustic nodes that automatically determines calibration parameters including the relative location and orientation (X, Y, Z, Θ) of each array. These relative coordinates are then fitted to one or more survey points to relate the relative coordinates to a physical map. The application that computes these estimates is itself a distributed sensing application. In this work we present a solution to this position estimation problem, demonstrating a complete vertical application built above a stack of re-usable system components and distributed services, implemented on a deployable embedded hardware platform. We describe: the hardware platform itself; Emstar, a software framework for developing complex embedded system software; a time-synchronized sampling layer; a multihop reliable multicast coordination primitive; a time-of-flight acoustic ranging and direction-of-arrival (DOA) estimation layer; and the top-level application that estimates the position and orientation of each array. We present the results of controlled tests of the ranging and DOA estimation system, as well as the results of deployment experiments in both an urban environment and a forested environment. These results demonstrate that our system outperforms other similar systems, and that it can achieve the sufficient accuracy for anticipated applications, such as bird localization.
This document is the author's post-print version of the journal article, incorporating any revisi... more This document is the author's post-print version of the journal article, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.
Journal of the Acoustical Society of America, May 1, 2008
Terrestrial bioacoustic census is a difficult problem because of propagation characteristics, obs... more Terrestrial bioacoustic census is a difficult problem because of propagation characteristics, obstructions, the diversity of bioacoustic sources, and the impact of noise. To address this problem we have developed VoxNet, a complete hardware and software platform for distributed acoustic monitoring applications. Each VoxNet node is a portable, self-contained processor with a small four-channel acoustic array. Using a distributed set of VoxNet nodes, a forested habitat can be monitored and the behavior of animals can be recorded and analyzed acoustically. In this work we present our experiences applying VoxNet to bioacoustic census. This work is based on data collected using the system during a deployment in Chiapas, Mexico at the Chajul Biological Field Station. The Chajul station is located in a region of dense rain forest and is home to Mexico's most diverse ecosystem. Using VoxNet in this harsh environment, we obtained census estimates based on observation of bird calls.
1. Animals produce sounds for diverse biological functions such as defending territories, attract... more 1. Animals produce sounds for diverse biological functions such as defending territories, attracting mates, deterring predators, navigation, finding food and maintaining contact with members of their social group. Biologists can take advantage of these acoustic behaviours to gain valuable insights into the spatial and temporal scales over which individuals and populations interact. Advances in bioacoustic technology, including the development of autonomous cabled and wireless recording arrays, permit data collection at multiple locations over time. These systems are transforming the way we study individuals and populations of animals and are leading to significant advances in our understandings of the complex interactions between animals and their habitats. 2. Here, we review questions that can be addressed using bioacoustic approaches, by providing a primer on technologies and approaches used to study animals at multiple organizational levels by ecologists, behaviourists and conservation biologists. 3. Spatially dispersed groups of microphones (arrays) enable users to study signal directionality on a small scale or to locate animals and track their movements on a larger scale. 4. Advances in algorithm development can allow users to discriminate among species, sexes, age groups and individuals. 5. With such technology, users can remotely and non-invasively survey populations, describe the soundscape, quantify anthropogenic noise, study species interactions, gain new insights into the social dynamics of sound-producing animals and track the effects of factors such as climate change and habitat fragmentation on phenology and biodiversity. 6. There remain many challenges in the use of acoustic monitoring, including the difficulties in performing signal recognition across taxa. The bioacoustics community should focus on developing a
Field biologists use animal sounds to discover the presence of individuals and to study their beh... more Field biologists use animal sounds to discover the presence of individuals and to study their behavior. Collecting bio-acoustic data has traditionally been a difficult and time-consuming process in which individual researchers use portable microphones to record sounds while taking notes of their own detailed observations. The recent development of new deployable acoustic sensor platforms presents opportunities to develop automated tools for bio-acoustic field research. In this work, we implement an AML-based source localization algorithm, and use it to localize marmot alarm-calls. We assess the performance of these techniques based on results from two field experiments: (1) a controlled test of direction-of-arrival (DOA) accuracy using a pre-recorded source signal, and (2) an experiment to detect and localize actual animals in their habitat, with a comparison to ground truth gathered from human observations. Although small arrays yield ambiguities from spatial aliasing of high frequency signals, we show that these ambiguities are readily eliminated by proper bearing crossings of the DOAs from several arrays. These results show that the AML source localization algorithm can be used to localize actual animals in their natural habitat, using a platform that is practical to deploy.
... Use commas. Citation. Lewis Girod; Thanos Stathopoulos; Nithya Ramanathan; Eric Osterweil; To... more ... Use commas. Citation. Lewis Girod; Thanos Stathopoulos; Nithya Ramanathan; Eric Osterweil; Tom Schoellhammer; & Deborah Estrin. (2004). Tools for Deployment and Simulation of Heterogeneous Sensor Networks. UC Los Angeles: Center for Embedded Network Sensing. ...
We present the design, implementation, and evaluation of the Acoustic Embedded Networked Sensing ... more We present the design, implementation, and evaluation of the Acoustic Embedded Networked Sensing Box (ENSBox), a platform for prototyping rapid-deployable distributed acoustic sensing systems, particularly distributed source localization. Each ENSBox integrates an ARM processor running Linux and supports key facilities required for source localization: a sensor array, wireless network services, time synchronization, and precise self-calibration of array position and orientation. The ENSBox's integrated, high precision self-calibration facility sets it apart from other platforms. This self-calibration is precise enough to support acoustic source localization applications in complex, realistic environments: e.g., 5 cm average 2D position error and 1.5 degree average orientation error over a partially obstructed 80x50 m outdoor area. Further, our integration of array orientation into the position estimation algorithm is a novel extension of traditional multilateration techniques. We present the result of several different test deployments, measuring the performance of the system in urban settings, as well as forested, hilly environments with obstructing foliage and 20-30 m distances between neighboring nodes.
k dimensional space, two sensor nodes i (x 1i , x 2i ,…, x ki) and j (x 1j , x 2j ,…, x kj) have ... more k dimensional space, two sensor nodes i (x 1i , x 2i ,…, x ki) and j (x 1j , x 2j ,…, x kj) have measured distance d ij , Linear/nonlinear optimization-based formulation OF: min/max F = M(ε ij) for pairs of nodes i and j that have measured distance d ij .
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Papers by Lewis Girod