Wireless sensor networks (WSNs) have received huge attention during the recent
years due to their applications in a large number of areas such as environmental
monitoring, health and traffic monitoring, surveillance and tracking, and monitoring
and control of factories and home appliances. Also, the rapid developments in the
micro electro-mechanical systems (MEMS) technology and circuit design lead to a
faster spread and adoption of WSNs. Wireless sensor networks consist of a number of
nodes featured in general with energy-limited sensors capable of collecting, processing
and transmitting information across short distances. Clock synchronization plays an
important role in designing, implementing, and operating wireless sensor networks,
and it is essential in ensuring a meaningful information processing order for the data
collected by the nodes. Because the timing message exchanges between different
nodes are affected by unknown possibly time-varying network delay distributions, the
estimation of clock offset parameters represents a challenge. This dissertation presents
several robust estimation approaches of the clock offset parameters necessary for time
synchronization of WSNs via the two-way message exchange mechanism. In this
dissertation the main emphasis will be put on building clock phase offset estimators robust with respect to the unknown network delay distributions.
Under the assumption that the delay characteristics of the uplink and the downlink
are asymmetric, the clock offset estimation method using the bootstrap bias
correction approach is derived. Also, the clock offset estimator using the robust Mestimation
technique is presented assuming that one underlying delay distribution is
mixed with another delay distribution.
Next, although computationally complex, several novel, efficient, and robust estimators
of clock offset based on the particle filtering technique are proposed to cope
with the Gaussian or non-Gaussian delay characteristics of the underlying networks.
One is the Gaussian mixture Kalman particle filter (GMKPF) method. Another
is the composite particle filter (CPF) approach viewed as a composition between
the Gaussian sum particle filter and the KF. Additionally, the CPF using bootstrap
sampling is also presented. Finally, the iterative Gaussian mixture Kalman particle
filter (IGMKPF) scheme, combining the GMKPF with a procedure for noise density
estimation via an iterative mechanism, is proposed.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2010-08-8318 |
| Date | 2010 August 1900 |
| Creators | Lee, Jae Han |
| Contributors | Serpedin, Erchin, Qaraqe, Khalid |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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