The field of wireless communications has been experiencing tremendous growth with the ever-increasing dependence on wireless services. In the operation of a communication network, the network coverage and node placement are of profound importance. The network performance metrics can be modeled as nonlinear functions of inter-node distances. Therefore, a geometric abstraction of the distance between wireless devices becomes a prerequisite for accurate system modeling and analysis. A geometrical probability approach is presented in this dissertation to characterize the probabilistic distance properties, for analyzing the location-critical performance metrics through various spatial distance distributions. Ideally, the research in geometrical probability shall give results for the distance distributions 1) over elementary geometries such as a straight line, squares and rectangles, and 2) over complex geometries such as rhombuses and hexagons. Both 1) and 2) are the representative topological shapes for communication networks. The current probability and statistics literature has explicit results for 1), whereas the results for 2) are not in existence. In particular, the absence of the distance distributions for rhombuses and hexagons has posed challenges towards the analytical modeling of location-critical performance metrics in complex geometries. This dissertation is dedicated to the application of existing results in 1) elementary geometries to the networking area, and the development of a new approach to deriving the distance distributions for complex geometries in 2), bridging the gap between the geometrical probability and networking research. The contribution of this dissertation is twofold. First, the one-dimensional Poisson point process in 1) is applied to the message dissemination in vehicular ad-hoc networks, where the network geometry is constrained by highways and city blocks. Second, a new approach is developed to derive the closed-form distributions of inter-node distances associated with rhombuses and hexagons in 2), which are obtained for the first time in the literature. Analytical models can be constructed for characterizing the location-critical network performance metrics, such as connectivity, nearest/farthest neighbor, transmission power, and path loss in wireless networks. Through both analytical and simulation results, this dissertation demonstrates that this geometrical probability approach provides accurate information essential to successful network protocol and system design, and goes beyond the approximations or Monte Carlo simulations by gracefully eliminating the empirical errors. / Graduate