Self-organization And Resource Allocation In Wireless Sensor Networks
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In this dissertation, we utilize clustering to organize wireless sensors into an energy-efficient hierarchy. We propose a Medium-Contention Based ClusterHeadship Auction (MCCHA) scheme, through which sensors self-organize themselves into energy-efficient clusters by bidding for cluster headship. This scheme is based on a new criterion that can be used by each sensor node to make a distributed decision on whether electing to be a cluster head or a non-head member, which is a fully distributed approach. Although MCCHA uses only local information, it achieves better performance in terms of effective lifetime and Data/Energy Ratio compared with native LEACH, which relies on other routing algorithms to access global information. A complementary exponential data correlation model is also introduced to simulate different data aggregation effect. To better understand the clustering issue in wireless sensor networks, we model the end-to-end distance for a given number of hops in dense planar Wireless Sensor Networks in this dissertation. We derive that the single-hop distance and postulate Beta distribution for 2-hop distance shows Beta distribution for two hops. The multi-hop distance approaches Gaussian when the number of hops is three or greater. Our error analysis also shows the distance error can be minimized by exploiting the distribution knowledge. Based on this model, we propose a Maximum Likelihood decision to decide to the number of hops given the distance between two nodes. Due to the computational complexity of conditional pdf of the number of hops given the distance, we also propose an attenuated Gaussian approximation for the conditional pdf. We show that the approximation visibly simplifies the decision process and the error analysis. The latency and energy consumption estimation are also included as application examples. Simulations show that our approximation model can predict the latency and energy consumption with less than half RMSE, compared to the linear models. In this dissertation, we also study the optimal cluster size in Underwater Acoustic networks. Due to the sparse deployment and channel property, the clustering characteristics of UA is different from that of aerial sensor networks. We show that the optimal cluster size is also relevant to the working frequency of the acoustic transmission.