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Reconfiguration methods for mobile sensor networks

delete2007-10-01
delete33
PRE
AI
A
Aman Kansal
W
William Kaiser
G
Gregory J. Pottie
M
Mani Srivastava
G
Gaurav S. Sukhatme
DOI:10.1145/1281492.1281497delete
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摘要

摘要

En 中文
Motion may be used in sensor networks to change the network configuration for improving the sensing performance. We consider the problem of controlling motion in a distributed manner for a mobile sensor network for a specific form of motion capability. Mobility itself may have a high resource overhead, hence we exploit motility, a constrained form of mobility, which has very low overheads but provides significant reconfiguration potential. We present an architecture that allows each node in the network to learn the medium and phenomenon characteristics. We describe a quantitative metric for sensing performance that is concretely tied to real sensor and medium characteristics, rather than assuming an abstract range based model. The problem of determining the desirable network configuration is expressed as an optimization of this metric. We present a distributed optimization algorithm which computes a desirable network configuration, and adapts it to environmental changes. The relationship of the proposed algorithm to simulated annealing and incremental subgradient descent based methods is discussed. A key property of our algorithm is that convergence to a desirable configuration can be proved even though no global coordination is involved. A network protocol to implement this algorithm is discussed, followed by simulations and experiments on a laboratory test bed.
Keyword:
performance
reliability
measurement
mobile or actuator systems
network protocols
coverage
actuation
motion coordination
spatial resolution
mobility control
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期刊

ACM Transactions on Sensor Networks 封面图
ACM Transactions on Sensor Networks
IF:
4.7
论文数:
999
被引数:
2.0K

机构

U
university of california los angeles
学者数:
5.3W
论文数: 4.2W
被引数: 89
University of California System 封面图
University of California System
学者数:
37.7W
论文数: 33.8W
被引数: 6.6K
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