Networked Computing in Wireless Sensor Networks for Structural Health Monitoring

This paper studies the problem of distributed computation over a network of wireless sensors. While this problem applies to many emerging applications, to keep our discussion concrete, we will focus on sensor networks used for structural health monitoring. Within this context, the heaviest computati...

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Vydané v:IEEE/ACM transactions on networking Ročník 20; číslo 4; s. 1203 - 1216
Hlavní autori: Jindal, Apoorva, Liu, Mingyan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.08.2012
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Approximation
Approximation methods
Base stations
Clustering
Computation
Decomposition
degree-constrained data collection tree
Delay
Energy consumption
Health monitoring (engineering)
Monitoring
networked computing
Networks
Sensors
singular value decomposition (SVD)
structural health monitoring (SHM)
Studies
Wireless sensor networks
wireless sensor networks (WSNs)
ISSN:1063-6692, 1558-2566
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Shrnutí:This paper studies the problem of distributed computation over a network of wireless sensors. While this problem applies to many emerging applications, to keep our discussion concrete, we will focus on sensor networks used for structural health monitoring. Within this context, the heaviest computation is to determine the singular value decomposition (SVD) to extract mode shapes (eigenvectors) of a structure. Compared to collecting raw vibration data and performing SVD at a central location, computing SVD within the network can result in significantly lower energy consumption and delay. Using recent results on decomposing SVD, a well-known centralized operation, we seek to determine a near-optimal communication structure that enables the distribution of this computation and the reassembly of the final results, with the objective of minimizing energy consumption subject to a computational delay constraint. We show that this reduces to a generalized clustering problem and establish that it is NP-hard. By relaxing the delay constraint, we derive a lower bound. We then propose an integer linear program (ILP) to solve the constrained problem exactly as well as an approximate algorithm with a proven approximation ratio. We further present a distributed version of the approximate algorithm. We present both simulation and experimentation results to demonstrate the effectiveness of these algorithms .
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ISSN:1063-6692
1558-2566
DOI:10.1109/TNET.2011.2175450