Location Centric Distributed Computation and Signal Processing Parmesh Ramanathan University of Wisconsin, Madison Co-Investigators:A. Sayeed, K. K. Saluja,

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Presentation transcript:

Location Centric Distributed Computation and Signal Processing Parmesh Ramanathan University of Wisconsin, Madison Co-Investigators:A. Sayeed, K. K. Saluja, Y.-H. Hu

Project Goals Tailor communication primitives for location-centric computing (Task 1) Develop robust, multi-resolution signal processing algorithms (Task 2) Develop strategies for fault-tolerance and self-testing (Task 3)

Task 1 Accomplishments (1/4) On paper Developed network API for location-centric computing (UW-API) Sender controlled Developed routing scheme for sensor networks (UW-Routing) Location-aided On demand route establishment Route caching

Task 1 Accomplishments (2/4) On WINSNG2.0 nodes Implemented UW-API and UW-Routing Integrated with CSP algorithms Integrated with other SITEX02 modules Participated in SITEX02

Task 1 Accomplishments (3/4) On ns-2 Implemented UW-API and UW-routing Compared the performance to pre- SITEX02 diffusion routing and ISI’s network API for a target tracking application

ns-2 Sample Results Implemented a target tracking application in a sensor field using three approaches using ns-2 SP-I (Subscribe-Publish-I): Approach being used in SITEX02 operational experiment Loc-Cen: Our push-based approach SP-II: Approximating the push-based approach using ISI’s network API

Sample Results Payload: Data exchange between sensors for CSP Routing: Messages sent purely to maintain network-level connectivity

Task 1 Accomplishments (4/4) On Linux Workstations Emulated Sensoria’s RF modem API using sockets over Ethernet Implemented playback mechanism to replay SITEX02 data Can synchronously replay on a network of workstations

Task 1: Plan for 2002 Compare performance with post- SITEX02 release of diffusion routing in ns-2 Compare the performance on SITEX02 data Enhance UW-API to better support fault-tolerance

Task 2 Accomplishments Developed CSP algorithms for detection, classification, localization, and tracking using acoustic sensors Evaluated the algorithms using Matlab Implemented the algorithms on WINSNG2.0 nodes using UW-API for collaboration Presently evaluating algorithms through playback of SITEX02 data

UW-CSP Algorithms At each node Energy detection Target classification In each region Region detection and classification Energy based localization Least square tracking Hand-off policy

Sample CFAR Detection Result Sitex02 node 4 channel 1, recorded on Mon Nov 13, :17: msec to 15:45:02 84 msec. Length: 8 minutes 32 seconds Green line: 0.75s interval Upper dash line: 3  Lower dash line: 

Sample Feature Vectors Currently, 3 classes: AAV, DW, and LAV Trained with Sitex00 broadband data from BAE and Xerox (AAV and LAV), and Sitex02 DW data pt FFT on time series.

Energy Based Localization Factors affecting location estimate accuracy: Energy estimate y(t) Sensor locations r i Energy decay exponents  Sensor gain variation g i As such, the (n  1) energy ratio circles may not intersect at a unique position Nonlinear cost function that may contain multiple local minimum:

Robust Least Square Tracking Model x(t) and y(t) as polynomials of time t Solve polynomial coefficients using least square solution. Predict future position by fitting future time into model. Can handle non-even time samples in CPA method. Adaptive update formula with forgetting factor. Implementation: Easy to compute Few parameters to pass Adaptive update formula using plane rotation Parameters to pass to another region

Task 2: Plan for 2002 Work with Sitex00 and Sitex02 time series Improve detection using classification results Improve classification by Finding better feature Feature reduction Different classifiers Improve localization Better implementation Multiple targets Improving tracking Multiple targets Track association Multi-modal processing Node modal fusion Region detection and classification fusion Localization using seismic time series and incorporate PIR modality

Task 3 Accomplishments (1/3) Developed fault-tolerant centralized fusion algorithm for target detection Presented at April 2000 PI meeting Results presented at FUSION 2001 conference Efficient for sparse sensor networks Developed fault-tolerant hierarchical fusion algorithms for target detection Paper submitted to DSN 2002

Task 3 Accomplishments (2/3) For centralized and hierarchical approaches, developed analytic model to characterize probability of detection Probability of false alarm Probability of failure Simulated the approaches in Matlab

Value versus Decision Fusion

Task 3 Accomplishments (3/3) Developed a better approach to characterize sensor deployments with respect to unauthorized traversal and monitoring Implemented the approach in Matlab Paper submitted to MobiHoc 2002

Unauthorized Traversal and Monitoring Exposure:Probability of detection Deals with noise Tradeoff between false alarm and exposure Incorporates value and decision fusion algorithms Can deal with sensor faults

Unauthorized Traversal

Unauthorized Travesal

Task 3: Plan for 2002 Evaluate impact of faults on target tracking using SITEX02 data Develop fault-tolerant algorithms for localization and tracking Investigate methods for diagnosing faulty sensors