In this paper, we present simulation and experimental studies of multiple bird source separation based on the Voxnet acoustic array node. The Approximate Maximum Likelihood (AML) method is used to estimate blindly the direction-of-arrivals(DOAs) of the sources to generate the steering vectors in order to separate the sources via beamforming. Simulation and measured data confirmed the proper operations of the AML beamforming algorithm and the Voxnet hardware node.

In this paper, we present an overview of a system that we are developing for the continuous monitoring of water distribution systems in Singapore. We discuss the design and implementation of the system that enables real-time detection of pipe burst events.

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.

Sensors capable of sensing phenomena at high data rates on the order of tens to hundreds of thousands of samples per second are now widely deployed in many industrial, civil engineering, scientific, networking, and medical applications. In aggregate, these sensors easily generate several million samples per second that must be processed within milliseconds or seconds. The computation required includes both signal processing and event stream processing. XStream is a stream processing system for such applications. XStream introduces a new data type, the signal segment, which allows applications to manipulate isochronous (regularly spaced in time) collections of sensor samples more conveniently and efficiently than the asynchronous representation used in previous work. XStream includes a memory manager and scheduler optimizations tuned for processing signal segments at high speeds. In benchmark comparisons, we show that XStream outperforms a leading commercial stream processing system by more than three orders of magnitude. On one application, the commercial system processed 72.7 Ksamples/sec, while XStream processed 97.6 Msamples/sec.

Distributed acoustic sensing underlies an increasingly important class of sensor network applications, from habitat monitoring and bioacoustic census to security applications and virtual fences. VoxNet is a complete hardware and software platform for distributed acoustic monitoring applications that focuses on three key goals: (1) rapid deployment in realistic environments; (2) a high level programming language that abstracts the user from platform and network details and compiles into a high performance distributed application; and (3) an interactive usage model based on run-time installable programs, with the ability to run the same high level program seamlessly over live or stored data. The VoxNet hardware is self-contained and weather-resistant, and supports a four-channel microphone array with automated time synchronization, localization, and network coordination. Using VoxNet, an investigator can visualize phenomena in real-time, develop and tune online analysis, and record raw data for off-line analysis and archival. This paper describes both the hardware and software elements of the platform, as well as the architecture required to support distributed programs running over a heterogeneous network. We characterize the performance of the platform, using both microbenchmarks that evaluate specific aspects of the platform and a real application running in the field.

Applications that combine live data streams with embedded, parallel, and distributed processing are becoming more commonplace. WaveScript is a domain-specific language that brings high-level, type-safe, garbage-collected programming to these domains. This is made possible by three primary implementation techniques, each of which leverages characteristics of the streaming domain. First, we employ a novel evaluation strategy that uses a combination of interpretation and reification to partially evaluate programs into stream dataflow graphs. Second, we use profile-driven compilation to enable many optimizations that are normally only available in the synchronous (rather than asynchronous) dataflow domain. Finally, we incorporate an extensible system for rewrite rules to capture algebraic properties in specific domains (such as signal processing).

This paper investigates an application of mobile sensing: detecting and reporting the surface conditions of roads. We describe a system and associated algorithms to monitor this important civil infrastructure using a collection of sensor-equipped vehicles. This system, which we call the Pothole Patrol (P2), uses the inherent mobility of the participating vehicles, opportunistically gathering data from vibration and GPS sensors, and processing the data to assess road surface conditions. We have deployed P2 on 7 taxis running in the Boston area. Using a simple machine-learning approach, we show that we are able to identify potholes and other severe road surface anomalies from accelerometer data. Via careful selection of training data and signal features, we have been able to build a detector that misidentifies good road segments as having potholes less than 0.2% of the time. We evaluate our system on data from thousands of kilometers of taxi drives, and show that it can successfully detect a number of real potholes in and around the Boston area. After clustering to further reduce spurious detections, manual inspection of reported potholes shows that over 90% contain road anomalies in need of repair.

Field biologists use animal sounds to discover the presence of individuals and to study their behavior. Collecting bioacoustic data has traditionally been a difficult and time consuming process in which 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.

Field biologists use animal sounds to discover the presence of individuals and to study their behavior. The recent development of new deployable acoustic sensor platforms presents opportunities to develop automated tools for bio-acoustic field research. In this work, we demonstrate a real-time wireless sensor system that implements an AML-based source localization algorithm. We will demonstrate a system that is easy to set up and that can localize a whistle in the demonstration area in real time. This demonstration will use the techniques we described in our corresponding paper [1].

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 resource-constrained 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 multiprocess 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.

We have developed statistical error modeling techniques for acoustic signal detection-based ranging measurements in the framework of wireless ad-hoc sensor networks (WASNs). The models are used as the basis for solving the location discovery problem in sensor networks. We first demonstrate that the major difficulty in location discovery is how to treat errors by proving the location discovery in presence of noisy measurements is a NP-complete problem, even in onedimensional space. Consequently, we formulate the location discovery as an instance of nonlinear function minimization that optimizes each of the empirically derived statistical error models. The minimization problem is then solved using a conjugate gradient-based nonlinear function optimization solver.\n We validate the efficiency of the approach by conducting comprehensive experiments on both deployed and simulated WASNs. The results indicate that the statistical model-based approach significantly improves the location accuracy compared with the approaches using the traditional optimization objectives. In addition, the localized version of our location discovery algorithm is capable of finding competitive solutions using significantly lower communication cost.

In recent decades, the cost of acoustic technologies has declined dramatically. Advances in networks, storage devices, and power management have made it practical to consider the remote location of sensors. Nonetheless, many challenges remain for the fabrication, deployment, and use of remote sensors. This paper provides an overview of the issues involved in developing remote acoustic sensors. We discuss physical design and the integration of components, data storage and communication issues, signal acquisition and classification, and the relationship of these issues to power usage requirements

WaveScope is a data management and continuous sensor data system that integrates relational database and signal processing operations into a single system. WaveScope is motivated by a large number of signal-oriented streaming sensor applications, such as: preventive maintenance of industrial equipment; detection of fractures and ruptures in various structures; in situ animal behavior studies using acoustic sensing; network traffic analysis; and medical applications such as anomaly detection in EKGs. These target applications use a variety of embedded sensors, each sampling at fine resolution and producing data at high rates ranging from hundreds to hundreds of thousands of samples per second. Though there has been some work on applications in the sensor network community that do this kind of signal processing (for example, shooter localization [5], industrial equipment monitoring [4], and urban infrastructure monitoring [2]), these applications are typically custombuilt and do not provide reusable high-level programming framework suitable for easily building new signal processing applications with similar functionality. This poster shows how WaveScope supports these types of application in a single, unified framework, providing both high run-time performance and easy application development.

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.

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 cm average 2D position error and 1.5 degree average orientation error over a 80x50 m outdoor area.

Being embedded in the physical world, sensor networks present a wide range of bugs and misbehavior qualitatively different from those in most distributed systems. Unfortunately, due to resource constraints, programmers must investigate these bugs with only limited visibility into the application. This paper presents the design and evaluation of Sympathy, a tool for detecting and debugging failures in sensor networks. Sympathy has selected metrics that enable efficient failure detection, and includes an algorithm that root-causes failures and localizes their sources in order to reduce overall failure notifications and point the user to a small number of probable causes. We describe Sympathy and evaluate its performance through fault injection and by debugging an active application, ESS, in simulation and deployment. We show that for a broad class of data gathering applications, it is possible to detect and diagnose failures by collecting and analyzing a minimal set of metrics at a centralized sink. We have found that there is a tradeoff between notification latency and detection accuracy; that additional metrics traffic does not always improve notification latency; and that Sympathy's process of failure localization reduces.

This work presents a preliminary design and evaluation of Sympathy, a debugging tool for pre-deployment sensor networks. Sympathy consists of mechanisms for collecting system performance metrics with minimal memory overhead; mechanisms for recognizing events based on these metrics; and a system for collecting events and their spatio-temporal context. Sympathy introduces the idea of correlating seemingly unrelated events, and providing context for these events, in order to track down bugs and find their root causes. Eventually, Sympathy will be part of a system that can aid in debugging sensor networks both pre- and post-deployment.

Recently deployed Wireless Sensor Network systems (WSNs) are increasingly following heterogeneous designs, incorporating a mixture of elements with widely varying capabilities. The development and deployment of WSNs rides heavily on the availability of simulation, emulation, visualization and analysis support. In this work, we develop tools specifically to support heterogeneous systems, as well as to support the measurement and visualization of operational systems that is critical to addressing the inevitable problems that crop up in deployment. Our system differs from related systems in three key ways: in its ability to simulate and emulate heterogeneous systems in their entirety, in its extensive support for integration and interoperability between motes and microservers, and in its unified set of tools that capture, view, and analyze real time debugging information from simulations, emulations, and deployments.

No abstract available.

In distributed wireless sensing applications such as unattended ground sensor systems, remote planetary exploration, and condition-based maintenance, where the deployment site is remote and/or the scale of the network is large, individual emplacement and configuration of the sensor nodes is difficult. Hence, network self-assembly and continuous network self-organization during the lifetime of the network in a reliable, efficient, and scalable manner are crucial for successful deployment and operation of such networks. This paper provides an overview of the concept of network self-assembly for ad hoc wireless sensor networks at the link layer, with descriptions of results from implementation of a novel network formation mechanism for wireless unattended ground sensor applications using a multicluster hierarchical topology and a novel dual-radio architecture.

We are developing an acoustic habitat-monitoring sensor network that recognizes and locates specific animal calls in real time. We investigate the system requirements of such a real-time acoustic monitoring network. We propose a system architecture and a set of lightweight collaborative signal processing algorithms that achieve real-time behavior while minimizing inter-node communication to extend the system lifetime. In particular, the target classification is based on spectrogram pattern matching while the target localization is based on beamforming using time difference of arrival (TDOA). We describe our preliminary implementation on a commercial off the shelf (COTS) testbed and present its performance based on testbed measurements.

As the cost of embedded sensors and actuators drops, new applications will arise that exploit high density networks of small devices capable of a variety of sensing tasks. Although individual devices may have limited functionality, the true value of the system comes from the emergent behavior that arises when data from many places in the system is combined. This type of data fusion has a number of requirements, but two of the most important are: 1) synchronized time, precise enough to resolve movement in the sensed phenomenon (e.g., sound); and 2) known geographic locations, on a similar scale to the sensors' size and deployment density. However, the installation cost of a localization system with sufficient granularity is considerable, because of the large amount of effort required to deploy such a system and make all the measurements required to tune it. In this paper, we describe a system based on COTS components that incorporates our novel time synchronization and acoustic ranging techniques. The result is a low-cost, readily available platform for distributed, coherent signal processing.

Pervasive micro-sensing and actuation may revolutionize the way in which we understand and manage complex physical systems: from airplane wings to complex ecosystems. The capabilities for detailed physical monitoring and manipulation offer enormous opportunities for almost every scientific discipline, and it will alter the feasible granularity of engineering. We identify opportunities and challenges for distributed signal processing in networks of these sensing elements and investigate some of the architectural challenges posed by systems that are massively distributed, physically-coupled, wirelessly networked, and energy limited.

Many applications of robotics and embedded sensor technology can benefit from fine-grained localization. Fine-grained localization can simplify multi-robot collaboration, enable energy efficient multi-hop routing for low-power radio networks, and enable automatic calibration of distributed sensing systems. We focus on range estimation, a critical prerequisite for fine-grained localization. While many mechanisms for range estimation exist, any individual mode of sensing can be blocked or confused by the environment. We present and analyze an acoustic ranging system that performs well in the presence of many types of interference, but can return incorrect measurements in non-line-of-sight conditions. We then suggest how evidence from an orthogonal sensory channel might be used to detect and eliminate these measurements. The work illustrates the more general research theme of combining multiple modalities to obtain robust results.