A peer-to-peer collaboration framework for multi-sensor data fusion

Authors:
Panho Lee;Anura P. Jayasumana;H. M. N. Dilum Bandara;Sanghun Lim;V. Chandrasekar
Affiliations:
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA;Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA;Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA;Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA;Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA
Venue:
Journal of Network and Computer Applications
Year:
2012

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Abstract

A peer-to-peer collaboration framework for multi-sensor data fusion in resource-rich radar networks is presented. In this high data volume real-time application, data from multiple radars are combined to improve the accuracy of radar scans (e.g., correct for attenuation) and to provide a composite view of the area covered by the radars. Data fusion process is subject to two constraints: (1) the accuracy requirement of the final fused results, which may be different at different end nodes, and (2) the real-time requirements of the application. The accuracy requirement is achieved by dynamically selecting the appropriate set of data to exchange among the multiple radar nodes. A mechanism for selecting a dataset based on current application-specific needs is presented. We also present a dynamic peer-selection algorithm, Best Peer Selection (BPS), that chooses a set of peers based on their computation and communication capabilities to minimize the data processing time per integration algorithm. Simulation-based results show that BPS can deliver a significant performance improvement, even when the peers have high variability in available network and computation resources.