Integration of the CORE and EMANE Network Emulators

Ahrenholz, Jeff; Goff, Tom; Adamson, Brian

doi:10.1109/milcom.2011.6127585

Cited by 63 publications

(28 citation statements)

References 5 publications

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“…The protocol is tested on CORE 1 and EMANE 2 network emulators Ahrenholz et al (2011). The advantage of emulation on CORE is that the code developed here can be transplanted to a Linux-based device, e.g., BeagleBone Black board, virtually without any modifications.…”

Section: Communication Architecturementioning

confidence: 99%

“…This fault model is created with velocities of 0.75V for the right fault and 1.0V for the left fault as shown in the Figure 9(a). We evaluated the communication cost of DT-AMR algorithm using CORE network emulator (Ahrenholz et al, 2011). We select CORE as the development and evaluation platform because the sensors that will be deployed on the real volcano will be some-low powered linux based devices such as android, beagle-bone or raspberry pi.…”

Section: Evaluation and Validationmentioning

confidence: 99%

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Distributed tomography with adaptive mesh refinement in sensor networks

Kamath

Shi

Chow

et al. 2017

IJSNET

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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 network; in-network computing.Reference to this paper should be made as follows: Kamath, G., Shi, L., Chow, E. and Song, W-Z. Copyright © 2017 Inderscience Enterprises Ltd. Distributed tomography with adaptive mesh refinement in sensor networks 41Wen-Zhan Song is a Professor in Georgia State University. His research mainly focuses on sensor web, smart grid and smart environment where sensing, computing, communication and control play a critical role and need a transformative study. His research has received 6 million+ research funding from NSF, NASA, USGS, Boeing and etc since 2005, and resulted in 80+ journal papers, conference papers and book chapters in this area. This paper is a revised and expanded version of a paper entitled 'Component-average based distributed seismic tomography in sensor networks' presented at

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Section: Communication Architecturementioning

confidence: 99%

Section: Evaluation and Validationmentioning

confidence: 99%

Distributed tomography with adaptive mesh refinement in sensor networks

Kamath

Shi

Chow

et al. 2017

IJSNET

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“…In order to verify the performance of NC-ODTN protocol, we implement NC-ODTN protocol based on the open source framework ION [8,9] and test the protocol at multiple network scenarios based on the open source network simulation software CORE [12,13] and EMANE [14][15][16]. The experimental results show that compared with the existing TCP/IP protocols, NC-ODTN effectively improves the success rate of data transmission, and greatly reduces the latency of data transmission.…”

Section: Introductionmentioning

confidence: 99%

NC-ODTN:Ocean DTN Network Coding Protocol

Zhang¹,

Li²

2016

Proceedings of the 2016 International Conference on Computer Science and Electronic Technology

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“…In distributed Cyber-Physical System (CPS), each sensor node observes partial phenomena due to spatial and temporal restriction and is able to form only partial rows of least-squares Eq. (1). Traditionally, these partial measurements were gathered at a centralized location, however, with the increase in sensors and their measurements, aggregation is becoming challenging and in some cases infeasible.…”

Section: Introductionmentioning

confidence: 99%

Distributed Randomized Kaczmarz and Applications to Seismic Imaging in Sensor Network

Kamath

Ramanan

Song

2015

2015 International Conference on Distributed Computing in Sensor Systems

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Many real-world wireless sensor network applications such as environmental monitoring, structural health monitoring, and smart grid can be formulated as a leastsquares problem. In distributed Cyber-Physical System (CPS), each sensor node observes partial phenomena due to spatial and temporal restriction and is able to form only partial rows of leastsquares. Traditionally, these partial measurements were gathered at a centralized location. However, with the increase in sensors and their measurements, aggregation is becoming challenging and infeasible. In this paper, we propose distributed randomized kaczmarz that performs in-network computation to solve leastsquares over the network by avoiding costly communication. As a case study, we present a volcano monitoring application on a distributed CORE emulator and use real data from Mt. St. Helens to evaluate our proposed method.

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Integration of the CORE and EMANE Network Emulators

Cited by 63 publications

References 5 publications

Distributed tomography with adaptive mesh refinement in sensor networks

Distributed tomography with adaptive mesh refinement in sensor networks

NC-ODTN:Ocean DTN Network Coding Protocol

Distributed Randomized Kaczmarz and Applications to Seismic Imaging in Sensor Network

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