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dc.contributor.advisorHeydari, Shahram S.
dc.contributor.authorIslam, Z.M. Faizul
dc.date.accessioned2012-03-09T20:56:43Z
dc.date.accessioned2022-03-29T16:33:49Z
dc.date.available2012-03-09T20:56:43Z
dc.date.available2022-03-29T16:33:49Z
dc.date.issued2012-01-01
dc.identifier.urihttps://hdl.handle.net/10155/211
dc.description.abstractIn this thesis, we propose a high level design for connectivity visualization of OLSRbased MANET topology based on local topology databases available in an OLSR node. Two different scenarios are considered: a central (full view) topology from a command and control location, or a nodal (partial) view from an ad-hoc node. A simulation-based analysis is conducted to calculate total number of active links at a particular time in full and nodal topology views. Also the error rate of network topology discovery based on total undiscovered link both mobile and static scenario is considered and reported. We also come up with an analytical model to analyse the network bandwidth and overhead of using TC, HELLO and custom NIM message to evaluate the performance of centralized visualization to build full map of the network with respect to situational awareness system. This thesis also presents a multi-node, 2-dimensional, distributed technique for coarse (approximate) localization of the nodes in a tactical mobile ad hoc network. The objective of this work is to provide coarse localization information based on layer-3 connectivity information and a few anchor nodes or landmarks, and without using traditional methods such as signal strength, Time of Arrival (ToA) or distance information. We propose a localization algorithm based on a Force-directed method that will allow us to estimate the approximate location of each node based on network topology information from a local OLSR database. We assume the majority of nodes are not equipped with GPS and thus do not have their exact location information. In our proposed approach we make use of the possible existence of known landmarks as reference points to enhance the accuracy of localization.en
dc.description.sponsorshipUniversity of Ontario Institute of Technologyen
dc.language.isoenen
dc.subjectTMANETen
dc.subjectSituational awarenessen
dc.subjectTopology discoveryen
dc.titleOLSR-based network discovery in situational awareness system for tactical MANETsen
dc.typeThesisen
dc.degree.levelMaster of Applied Science (MASc)en
dc.degree.disciplineElectrical and Computer Engineeringen


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