Abstract

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.