Studying Vehicle Movements on Highways and their Impact on Ad-Hoc Connectivity

While Mobile Ad-Hoc Networks are generally studied using a randomized node movement model such as the Random Way-Point model [8], Vehicular Ad-Hoc Networks deal with street-bound vehicles following a completely different movement pattern. This results - among other things - in a completely different connectivity situation and new challenges for data dissemination or routing/forwarding algorithms. Thus, researchers need a) suitable movement patterns for simulation, and b) a solid statistical understanding of the connectivity situation independent of the protocols utilized. In this work, we present a set of movement traces derived from typical situations on German Autobahns and an elaborate statistical analysis with respect to movement and connectivity relevant parameters. In addition, we present HWGui, a visualization, transformation, and evaluation package developed to study these scenarios. Beside the analysis capabilities HWGui is able, among other things, to generate movement files suitable for simulation with ns-2 [10]

A decomposition approach for the Frequency Assignment Problem

The Frequency Assignment Problem (FAP) is an important optimization problem that arises in operational cellular wireless networks. Solution techniques based on meta-heuristic algorithms have been shown to be successful for some test problems but they have not been usually demonstrated on large scale problems that occur in practice. This thesis applies a problem decomposition approach in order to solve FAP in stances with standard meta-heuristics. Three different formulations of the problem are considered in order of difficulty: Minimum Span (MS-FAP), Fixed Spectrum (MS-FAP), and Minimum Interference FAP (MI-FAP). We propose a decomposed assignment technique which aims to divide the initial problem into a number of subproblems and then solves them either independently or in sequence respecting the constraints between them. Finally, partial subproblem solutions are recomposed into a solution of the original problem. Standard implementations of meta-heuristics may require considerable run times to produce good quality results whenever a problem is very large or complex. Our results, obtained by applying the decomposed approach to a Simulated Annealing and a Genetic Algorithm with two different assignment representations (direct and order-based), show that the decomposed assignment approach proposed can improve their outcomes, both in terms of solution quality and runtime. A number of partitioning methods are presented and compared for each FAP, such as clique detection partitioning based on sequential orderings and novel applications of existing graph partitioning and clustering methods adapted for this problem