Lewis Girod

Fine-grained network time synchronization using reference broadcasts

Recent advances in miniaturization and low-cost, low-power design have led to active research in large-scale networks of small, wireless, low-power sensors and actuators. Time synchronization is critical in sensor networks for diverse purposes including sensor data fusion, coordinated actuation, and power-efficient duty cycling. Though the clock accuracy and precision requirements are often stricter than in traditional distributed systems, strict energy constraints limit the resources available to meet these goals.We present Reference-Broadcast Synchronization, a scheme in which nodes send reference beacons to their neighbors using physical-layer broadcasts. A reference broadcast does not contain an explicit timestamp; instead, receivers use its arrival time as a point of reference for comparing their clocks. In this paper, we use measurements from two wireless implementations to show that removing the senders nondeterminism from the critical path in this way produces high-precision clock agreement (1.85 ± 1.28μsec, using off-the-shelf 802.11 wireless Ethernet), while using minimal energy. We also describe a novel algorithm that uses this same broadcast property to federate clocks across broadcast domains with a slow decay in precision (3.68 ± 2.57μsec after 4 hops). RBS can be used without external references, forming a precise relative timescale, or can maintain microsecond-level synchronization to an external timescale such as UTC. We show a significant improvement over the Network Time Protocol (NTP) under similar conditions.

Instrumenting the world with wireless sensor networks

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.

Robust range estimation using acoustic and multimodal sensing

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.

Sympathy for the sensor network debugger

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 Sympathys process of failure localization reduces.

Locating tiny sensors in time and space: a case study

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.

A system for simulation, emulation, and deployment of heterogeneous sensor networks

Recently deployed Wireless Sensor Network systems (WSNs) are increasingly following <i>heterogeneous</i> 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 <i>heterogeneous</i> systems, as well as to support the measurement and visualization of <i>operational</i> 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 <i>heterogeneous</i> 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.

Target classification and localization in habitat monitoring

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.

Preprocessing in a tiered sensor network for habitat monitoring

We investigate task decomposition and collaboration in a two-tiered sensor network for habitat monitoring. The system recognizes and localizes a specified type of birdcalls. The system has a few powerful macronodes in the first tier, and many less powerful micronodes in the second tier. Each macronode combines data collected by multiple micronodes for target classification and localization. We describe two types of lightweight preprocessing which significantly reduce data transmission from micronodes to macronodes. Micronodes classify events according to their cross-zero rates and discard irrelevant events. Data about events of interest is reduced and compressed before being transmitted to macronodes for target localization. Preliminary experiments illustrate the effectiveness of event filtering and data reduction at micronodes.

Localization in sensor networks

The development of large scale distributed sensor systems is a significant scientific and engineering challenge, but they show great promise for a wide range of applications. The capability to sense and integrate spatial information with other elements of a sensor application is critical to exploring the full potential of these systems. In this article we discuss the range of application requirements, introduce a taxonomy of localization mechanisms, and briefly discuss the current state of the art in ranging and positioning technologies. We then introduce two case studies that illustrate the range of localization applications.

Sympathy: a debugging system for sensor networks [wireless networks]

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.