Title: Distributed tomography with adaptive mesh refinement in sensor networks

Authors: Goutham Kamath; Lei Shi; Edmond Chow; Wen-Zhan Song

Addresses: Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA ' Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA ' College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA ' Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA

Abstract: Existing seismic instrumentation systems do not yet have the capability to recover the physical dynamics with sufficient resolution in real time. Currently, seismologists use centralised tomography inversion algorithm, which requires manual data gathering from each station and months to generate tomography. To address these issues a distributed approach is required which can avoid data collection from large number of sensors and perform in-network imaging to real-time tomography. In this paper, we present a distributed adaptive mesh refinement (AMR) solution to invert seismic tomography over large dense network, which avoids centralised computation and expensive data collection. Our approach first discretises the data and filters them using adaptive mesh to make it well-conditioned. The system is implemented and evaluated using a CORE emulator and we show that the filtered well-conditioned system has lower dimension and improved convergence rate than the original system, thereby decreasing the communication overhead over the network.

Keywords: distributed sensing; adaptive mesh; seismic tomography; sensor networks; in-network computing; distributed tomography; earthquakes; volcano monitoring; volcanoes.

DOI: 10.1504/IJSNET.2017.080604

International Journal of Sensor Networks, 2017 Vol.23 No.1, pp.40 - 52

Received: 12 Oct 2014
Accepted: 27 Mar 2015

Published online: 25 Nov 2016 *

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