Paper Info
Title
Component-Average Based Distributed Seismic Tomography in Sensor Networks
Abstract
Existing volcano instrumentation and monitoring system use centralized approach for data collection and image reconstruction and they lack the capability of obtaining real time information. A new distributed method is required which can obtain a high resolution seismic tomography in real time. In this paper, we present a component-average distributed multi-resolution evolving tomography algorithm for processing data and inverting volcano tomography in the network, while avoiding centralized computation and costly data collection. The new algorithm distributes the computational burden to sensor nodes and performs real time tomographic inversion under constraints of network resources. We implemented and evaluated the algorithmin a customized simulator using synthetic data. The experiment results validate that our proposed algorithm not only balances the computation load but also achieves high data loss tolerance.
Year
DOI
Venue
2013
10.1109/DCOSS.2013.17
Distributed Computing in Sensor Systems
Keywords
Field
DocType
data collection,seismic tomography,real time tomographic inversion,high data loss tolerance,sensor networks,inverting volcano tomography,new algorithm,costly data collection,real time information,real time,proposed algorithm,synthetic data,tomography,distributed computing,mathematical model,volcanoes,image reconstruction,computational modeling
Data loss,Real-time data,Computer science,Real-time computing,Synthetic data,Artificial intelligence,Distributed computing,Computation,Iterative reconstruction,Computer vision,Brooks–Iyengar algorithm,Tomography,Wireless sensor network
Conference
ISBN
Citations 
PageRank 
978-1-4799-0206-4
11
0.67
References 
Authors
11
3
Name
Order
Citations
PageRank
Goutham Kamath1282.66
Lei Shi2333.86
Wen-Zhan Song3113291.12