THESIS
2014
xi, 93 pages : illustrations ; 30 cm
Abstract
A large-scale sensor network typically consists of numerous low-cost and resources constrained
sensor nodes working in a self-organizing manner. Being embedded in the physical
world, wireless sensor networks (WSNs) present a wide range of failures, due to environment
conditions, hardware limitations and software uncertainties, and so on. Once
deployed, the interactivity of a WSN greatly decreases, and network managers must investigate
network behaviors with limited visibility into applications. Based on a real world
environment monitoring sensor network system CitySee, this thesis addresses three key
aspects for network parameters management in wireless sensor networks, i.e., injecting
performance related time-varying metrics into each sensor node, and collecting these metrics...[
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A large-scale sensor network typically consists of numerous low-cost and resources constrained
sensor nodes working in a self-organizing manner. Being embedded in the physical
world, wireless sensor networks (WSNs) present a wide range of failures, due to environment
conditions, hardware limitations and software uncertainties, and so on. Once
deployed, the interactivity of a WSN greatly decreases, and network managers must investigate
network behaviors with limited visibility into applications. Based on a real world
environment monitoring sensor network system CitySee, this thesis addresses three key
aspects for network parameters management in wireless sensor networks, i.e., injecting
performance related time-varying metrics into each sensor node, and collecting these metrics
to enhance network visibility, providing more practical and efficient diagnosis models
in wireless sensor networks. Through intensive simulations and real world implementations,
I evaluate the performance of the proposed methods in a real system and verify the
applicability.
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