A Geometrical probability approach to location-critical network performance metrics

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dc.contributor.authorZhuang, Yanyan
dc.contributor.supervisorPan, Jianping
dc.date.accessioned2012-03-23T18:55:01Z
dc.date.available2012-03-23T18:55:01Z
dc.date.copyright2012en_US
dc.date.issued2012-03-23
dc.degree.departmentDepartment of Computer Science
dc.degree.levelDoctor of Philosophy Ph.D.en_US
dc.description.abstractThe 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.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.bibliographicCitationY. Zhuang, Y. Luo, L. Cai, and J. Pan. A geometric probability model for capacity analysis and interference estimation in wireless mobile cellular systems. In Proceedings of IEEE Global Telecommunications Conference (GLOBECOM 2011), Houston, TX, USA, December 2011.en_US
dc.identifier.bibliographicCitationY. Zhuang and J. Pan. Probabilistic energy optimization in wireless sensor networks with variable size griding. In Proceeding of IEEE International Conference on Communications (ICC 2010), pages 1–5, Cape Town, South Africa, May 2010. IEEE.en_US
dc.identifier.bibliographicCitationY. Zhuang and J. Pan. Random distances associated with hexagons. Arxiv preprint arXiv:1106.2200, 2011.en_US
dc.identifier.bibliographicCitationY. Zhuang and J. Pan. Random distances associated with rhombuses. Arxiv preprint arXiv:1106.1257, 2011.en_US
dc.identifier.bibliographicCitationY. Zhuang and J Pan. A geometrical probability approach to location-critical network performance metrics. In Proceedings of IEEE INFOCOM 2012, pages 1–9, Orlando, FL, USA, March 2012.en_US
dc.identifier.bibliographicCitationY. Zhuang, J. Pan, and L. Cai. Minimizing energy consumption with probabilistic distance models in wireless sensor networks. In Proceedings of IEEE INFOCOM 2010, pages 1–9, San Diego, CA, USA, March 2010. IEEE.en_US
dc.identifier.bibliographicCitationY. Zhuang, J. Pan, and L. Cai. A probabilistic model for message propagation in two-dimensional vehicular ad-hoc networks. In Proceedings of the seventh ACM international workshop on VehiculAr InterNETworking (VANET 2010), pages 31–40, Chicago, IL, USA, 2010.en_US
dc.identifier.bibliographicCitationY. Zhuang, J. Pan, Y. Luo, and L. Cai. Time and location-critical emergency message dissemination for vehicular ad-hoc networks. IEEE Journal on Selected Areas in Communications, 29(1):187–196, 2011.en_US
dc.identifier.bibliographicCitationY. Zhuang, V. Viswanathan, J. Pan, and L. Cai. Upload capacity analysis for drive-thru internet. Engine, 2010.en_US
dc.identifier.bibliographicCitationY. Zhuang, V. Viswanathan, J. Pan, and L. Cai. On the uplink mac performance of a drive-thru internet. IEEE Transactions on Vehicular Technology, 2012.en_US
dc.identifier.bibliographicCitationL. He, Y. Zhuang, J. Pan, and J. Xu. Evaluating on-demand data collection with mobile elements in wireless sensor networks. In Proceedings of IEEE Vehicular Technology Conference Fall (VTC 2010-Fall), pages 1–5, Ottawa, ON, Canada, September 2010.en_US
dc.identifier.urihttp://hdl.handle.net/1828/3856
dc.languageEnglisheng
dc.language.isoenen_US
dc.rights.tempAvailable to the World Wide Weben_US
dc.subjectGeometrical Probabilityen_US
dc.subjectLocation-Critical Performance Metricsen_US
dc.titleA Geometrical probability approach to location-critical network performance metricsen_US
dc.typeThesisen_US

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