Location Centric Distributed Computation and Signal

Processing

University of Wisconsin, Madison

Acknowledgements:DARPA SensIT Contract F30602-00-2-0055

Team Members

Faculty P. Ramanathan A. Sayeed

Students K.-C. Wang T.-L. Chin T. Clouqueur

Y.-H. Hu K. K. Saluja

A. Ashraf A. D’Costa M. Duarte

D. Li X. Sheng V.

Phipatansuphorn

Sensor Network Characteristics

Commands/queries are typically issued to a geographic region instead of specific nodes, such as: Average temperature in a given region Unidentified object counts in a given region Specific object tracking in a given region

Only devices in the specified geographic region need to participate in executing a command/query

Solution: Location-Centric Computing

Location-Centric Computing?

All nodes are aware of own location (GPS).

However, geographic regions are the only addressable entities. Regions play the traditional role of a node. Regions are created before commands are issued. Each node participates in activities of regions it

belongs to. Embedded manager region coordinates

intra-region activities.

Implementation

Application Programming Interface UW-API:

Set of communication primitives tailored for location centric information exchange

Networking Support UW-Routing:

Location-aware routing scheme for wireless ad hoc sensor networks

UW-API

Data exchange primitives:

Affinity with standard message passing interface for distributed computing E.g. the well-known MPI 1.1 library

Regional communication services: Send, Receive, Reduce, Barrier

Administrative primitives: Create_region and delete_region

UW-API

Example: SN_Send Sends a message from a node to all nodes in

the addressed region Used to send commands and data

Example: SN_Reduce Aggregates data within a region. Aggregates data as min, max, average, sum, …. Collect results in embedded manager region.

UW-Routing

A location-aware on-demand routing protocol Each node maintains a routing table of paired

[destination region, next hop] entries.

Routing entry is created on demand using RouteRequest (RREQ) and RouteReply (RREP).

RREQ and RREP flood in a limited scope similar to Location-aided Routing [Vaidya] .

Inter-Region and Intra-Region Routing for Send

1. Message sent from a source node to a region.

2. Message flooded to all nodes in the region.

Collaborative Target Tracking

Create regions at possible entry points.

Nodes in the created regions collaborate to detect any entering targets.

Collaborative Target Tracking

When a target is detected, nodes in the region start localization and tracking.

Tracking results are used to estimate future target locations.

Additional regions are created in possible target locations.

Target Detection

At each node: Energy Detector Dynamic noise level

estimation.

Target detected if received energy exceeds estimated noise level.

Constant false alarm rate maintained.

Within a region: Decision fusion Nodal detection

decisions sent to manager nodes.

Manager nodes fuse nodal decisions into regional decision.

Different fusion wrights for different modalities.

Target Classification

At each node: Maximum Likelihood

classifier using Gaussian model for the classes

Training done using SITEX02 data

Acoustic modality only

Decisions as AAV, DW, HMMWV, or Unknown

Within a region: Nodal classification

decisions sent to manager nodes.

Manager nodes fuse nodal decisions into regional decision.

Target type with most votes becomes “winner”.

Target Localization

2

1

1

||

||)(

E

E

rr

rrr i

ii

)(maxarg* rr i

PIR Modality Estimate target to be

at the projection from a detecting node onto the road.

PIR Localizations occur infrequently as compared to acoustic.

PIR localizations are less susceptible to noise.

Acoustic Modality Energy based

localization Let Ei be the energy

reading at sensor node i located at ri.

Estimate target to be at a location where

Target Tracking and Prediction

Tracker is based on Kalman Filter.

Tracker assigns different weights to acoustic and PIR localization estimates.

Tracker provides feedback to acoustic based localization in terms of better refined search area.

Tracker predicts location of target in the near future.

Tracker predictions are used to create and activate additional regions for possible target detection.

UW-Senware

UW-Routing

RF Modem Library

Base Thread DLT Thread

UW-APITimeseriesRepository

FantasticData

Data Acquisition

Display GUI

Data Logger

RF Modem RF Modem

(Sensoria)

(BBN)

(BAE)

(Virginia Tech)

(U Maryland)

Case Studies on Two Testbeds

Single vehicle (AAV, DW, HMMWV) traversing SITEX02 sensor field SITEX02 timeseries collected in 29 Palms,

CA. on Nov 14, 2002

Two vehicles (AAV and DW) crossing each other in Waltham sensor field Synthetic timeseries based on SITEX02 data

Two vehicle (AAV and DW) meeting each other and turning back in Waltham sensor field Synthetic timeseries based on SITEX02 data

Control vs Payload Messages (AAV run SITEX02)

0

200

400

600

800

1000

1200

1400

88-byte messages

1 41 42 48 49 50 51 52 53 54 55 58 59 60 61

Node ID

Control vs. Payload message counts

Control

Payload

In-Region vs Out-of-Region Messages (AAV run SITEX02)

0

200

400

600

800

1000

1200

1400

88-byte messages

1 41 42 48 49 50 51 52 53 54 55 58 59 60 61

Node ID

Per Node In-Region vs. Out-of-Region Messages

Out-of-Region

In-Region

Packets Injected vs Forwarded (AAV run SITEX02)

0

200

400

600

800

1000

1200

1400

88-byte messages

1 41 42 48 49 50 51 52 53 54 55 58 59 60 61

Node ID

Per Node Packet Injection and Forward Count

Packet_Forwarded

Packet_Injected

Per Node Bandwidth Consumed (AAV run SITEX02)

Per Node Bandwidth Consumption vs. Time

0

200

400

600

800

1000

1200

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1600

86260 86280 86300 86320 86340 86360 86380

Time (sec)

Ban

dwid

th (B

ytes

per

sec

)

Bandwidth

Time Trace of Messages Sent/Forwarded (AAV SITEX02)

Target Detection(AAV SITEX02)

Target Classification(AAV SITEX02)

Target Tracking (AAV SITEX02)

Publications (1 of 2)

R. Brooks, P. Ramanathan, and A. Sayeed, “Distributed target classification and tracking in sensor networks,” Submitted to IEEE Proceedings.

P. Ramanathan, K. K. Saluja, and Y.-H. Hu, “Collaborative sensor signal processing for target detection, localization, and tracking, To appear in Army Sciences Conferecens, December 2002.

T. Clouqueur, V. Phipatansuphorn, P. Ramanathan, and K. K. Saluja, “Sensor deployment strategy for target detection”, Workshop on Sensor Networks and Applications, September 2002.

A. D’Costa, Y.-H. Hu, and A. M. Sayeed, “Classification of targets using multiple sensing modalities in distributed micro-sensor networks, Submitted for publication.

V. Phipatansuphorn and P. Ramanathan, “Vulnerability of sensor networks to unauthorized traversal and monitoring,” Submitted to IEEE Transactions on Computers, April 2002.

Publications (2 of 2)

T. Clouqueur, K. K. Saluja, and P. Ramanathan, “Fault tolerance in collaborative sensor networks for target detection,” Submitted to IEEE Transactions on Computers, April 2002.

D. Li and Y.-H. Hu, “Energy based collaborative source localization using acoustic microsensor array,” Submitted for publication, February 2002.

D. Li, K. Wong, Y.-H. Hu, and A. Sayeed, “Detection, classification, and tracking of targets,” IEEE Signal Processing Magazine, March 2002.

K.-C. Wang and P. Ramanathan, “Multiuser receiver aware multicast in CDMA-based multihop wireless networks,” in Proceedings of Mobihoc, pp. 291-294, October 2001.

T. Clouqueur, P. Ramanathan, K. K. Saluja, and K.-C. Wang, “Value-fusion versus decision-fusion for fault-tolerance in collaborative target detection in sensor networks,” in Proceedings of Fourth International Conference on Information Fusion, August 2001.