In this dissertation, firstly, we propose a geographic location free double-ruling based approach for general 3D sensor networks with possibly complicated topology and geometric shapes. Without the knowledge of the geographic location and the distance bound, a query simply travels along a simple curve with the guaranteed success to retrieve aggregated data through time and space with one or different types across the network. Extensive simulations and comparisons show the proposed scheme with low cost and a balanced traffic load.
Secondly, we explore 3D surface network localization with terrain model. A digital terrain model (DTM), available to public with a variable resolution up to one meter, is a 3D representation of a terrain's surface. It is commonly built using remote sensing technology or from land surveying and can be easily converted to a triangular mesh. Given a sensor network deployed on the surface of a 3D terrain with one-hop distance information available, we can extract a triangular mesh from the connectivity graph of the network. The constraint that the sensors must be on the known 3D terrain's surface ensures that the triangular meshes of the network and the DTM of the terrain's surface approximate the same geometric shape and overlap. We propose a fully distributed algorithm to construct a well-aligned mapping between the two triangular meshes. Based on this mapping, each sensor node of the network can easily locate reference grid points from the DTM to calculate its own geographic location. We carry out extensive simulations under various scenarios to evaluate the overall performance of the proposed localization algorithm. We also discuss the possibility of 3D surface network localization with mere connectivity and the results are promising.
|Commitee:||Bayoumi, Magdy, Tzeng, Nianfeng, Wu, Hongyi|
|School:||University of Louisiana at Lafayette|
|School Location:||United States -- Louisiana|
|Source:||DAI-B 75/10(E), Dissertation Abstracts International|
|Keywords:||Geometry, In-network processing, Information processing, Localization, Wireless sensor networks|
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