Statistical Approaches for Data Aggregation in Wireless Sensor Networks
Date
2015-04-30
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Abstract
This thesis provides novel statistical signal processing techniques for pdf-unaware data aggregation in Wireless Sensor Networks (WSNs) in order to improve the accuracy and energy consumption.
Firstly, a model is developed for multi-bit data aggregation quantifying the three major errors namely; measurement errors, quantization errors and channel errors. Analytical expressions for first and second order statistics are obtained. An estimator
is derived to estimate parameters for a single hop network. Simulation results confirm that the proposed estimator outperforms the conventional estimators. The merit of the model is tested in power scheduling problem to demonstrate improvements in energy saving.
Secondly, the performance of a most commonly used data aggregation method is analyzed for multi-hop WSNs by employing techniques such as retransmissions and redundant sensor deployments. However, the improvement in data accuracy is limited. Using the proposed model and estimator, improvement is observed in data accuracy with enhanced network lifetime.
Thirdly, a decision aggregation rule is modelled for the received binary decisions,
by modelling received binary decisions incorporating measurement and transmission
error probabilities. Its performance in single hop homogeneous and heterogeneous
networks is very close to that of the optimum majority and the weighted majority
rules. Moreover, the complexity of the proposed rule is lower than the optimal rules. A bit level aggregation method is also introduced for multi-bit data aggregation, eliminating the computationally expensive data encoding and decoding. This method consistently performs well compared to the numerical multi-bit data aggregation.
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Keywords
Engineering--Electronics and Electrical
Citation
Wickrema Seneviratne, C. K. (2015). Statistical Approaches for Data Aggregation in Wireless Sensor Networks (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/27876