This dissertation develops optimal algorithms for distributed detection and estimation
in static and mobile sensor networks. In distributed detection or estimation scenarios
in clustered wireless sensor networks, sensor motes observe their local environment,
make decisions or quantize these observations into local estimates of finite length, and
send/relay them to a Cluster-Head (CH). For event detection tasks that are subject to
both measurement errors and communication errors, we develop an algorithm that
combines a Maximum a Posteriori (MAP) approach for local and global decisions with
low-complexity channel codes and processing algorithms. For event estimation tasks that
are subject to measurement errors, quantization errors and communication errors, we
develop an algorithm that uses dithered quantization and channel compensation to ensure
that each mote's local estimate received by the CH is unbiased and then lets the CH fuse
these estimates into a global one using a Best Linear Unbiased Estimator (BLUE). We then
determine both the minimum energy required for the network to produce an estimate
with a prescribed error variance and show how this energy must be allocated amongst the
motes in the network.
In mobile wireless sensor networks, the mobility model governing each node will affect the
detection accuracy at the CH and the energy consumption to achieve this level of accuracy.
Correlated Random Walks (CRWs) have been proposed as mobility models that
accounts for time dependency, geographical restrictions and nonzero drift. Hence, the
solution to the continuous-time, 1-D, finite state space CRW is provided and its statistical
behavior is studied both analytically and numerically. The impact of the motion of sensor
on the network's performance is also studied.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/44825 |
| Date | 27 June 2012 |
| Creators | Sun, Xusheng |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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