VoxNet: Reducing Latency in High Data Rate Applications

  • Michael Allen


High data-rate sensing is a challenging aspect of WSN research due to the large amounts of data generated by each sensor node. When data is being generated faster than it can be transported over multiple network hops, there is a need to apply on-node, in-network event detection, filtering and other data processing techniques. Although contingent on the specific application, signals in the audible acoustic spectrum must typically be sampled at kHz rates. This makes acoustics a particularly challenging phenomena in the high date rate class.

In the context of high data-rate sensing, this chapter describes in detail the deployment of a specific application on a platform for distributed acoustic sensing applications called VoxNet. The application, on-line source localization of animals from their vocalizations is a compelling tool for evolutionary biologists; timely in-field position estimates can enable biologists to augment their observations.

A ten-day deployment of VoxNet highlighted several important problems which could not have been predicted in advance, largely related to the instrumentation of the system and end-to-end latency from event detection to position estimate. Using the extensive log data gathered during the deployment, two strategies to improve end-to-end system timeliness were developed. These are Lazy Grouping, a centralized algorithm which performs on-line grouping of event data and facilitates its collection from the network, and an Adaptation policy which allows nodes in the network to individually and dynamically evaluate whether to process data locally based on previous data transfers. Whilst the design and evaluation of these refinements is based on application-specific experiences, the techniques themselves are transferable to a variety of high data rate applications.


Alarm Call Network Stream Transfer Latency Adaptation Policy Dynamic Source Route 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Singapore-MIT Alliance for Research and TechnologySingaporeSingapore

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