Collaborative signal and information processing (CSIP) in distributed
sensor networks is an emerging research area, drawing upon
traditionally disparate disciplines such as lower-power communication
and computation, space-time signal processing, distributed and fault
tolerant algorithms, adaptive systems, and sensor fusion and decision
theory.
Recent advances in wireless networking, microfabrication (e.g. MEMS),
and embedded processing have enabled a new generation of sensor
networks for a wide range of tracking and identification problems in
both civilian and military applications, including human-aware
environments, intelligent transportation grids, factory
condition-based monitoring and maintenance, and battlefield
situational awareness. However, unlike centralized sensor platforms,
distributed sensor nets are characterized by limited battery power,
frequent node attrition, and variable data and communication quality.
To scale up to more realistic tracking and classification applications
involving tens of thousands of sensors, heterogeneous sensing
modalities, multiple targets, and non-uniform spatio-temporal scales,
these systems have to rely primarily on collaboration among
distributed sensors to support energy-aware and low-latency
computation and communication.
The PARC Collaborative Sensing Project has taken a systemic approach
to address key CSIP issues of representing, processing, storing, and
querying spatially distributed, multi-modal information from a sensor
field. To extract reliable and timely information from a sensor field,
CSIP must suport cross-node data aggregation, asynchronous execution,
and progressive accuracy. We have developed information driven sensor
querying (IDSQ) as a mechanism for mediating between sensor data and
user queries in a network. IDSQ dynamically tasks sensor nodes to
maximize information gain while minimizing energy and bandwidth
consumption, thereby blurring the traditional separation between
network layers and applications running on the network. In a recent
DARPA field test in the Mojave desert involving 70+ wireless sensor
nodes, IDSQ was successfully demonstrated to support a real-time,
decentralized vehicle tracking application. In this talk, I will
describe the IDSQ theory and algorithms, as well as experimental
results from the field and lab testbeds.
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