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• W836690559 abstract "In mobile computing, a technology is said to be ubiquitous, if it is integrated in everyday life to such an extend that it is taken for granted instead of actually being recognized as technology. There are already numerous examples for ubiquitous technologies, such as navigation systems, cell phones, or notebook computers. Many of these enable the users to participate in mobile networks and to benefit from location-aware applications. A prominent use case of ubiquitous computing and networking is the research area of inter-vehicular communication: road vehicles are planned to be equipped with computing units that have access to the vehicles' sensors and to wireless communication interfaces, so that vehicles can share information about experienced situations among each other. In doing so, the technique can be used to make road traffic safer, more efficient, and to provide a higher level of convenience to the passengers. To achieve these goals, a number of efficiency and convenience applications record and transmit the vehicles' trajectories, i.e., the sequence of positions over time. However, the transmission of trajectories can induce a significant load to the communication channel and may waste resources that are required by safety applications to work properly. To avoid this situation, a number of lossy trajectory compression algorithms have been proposed in literature that allow for an adjustable compression error bound. In this thesis, we examine compression algorithms for trajectory data. Though these algorithms are suitable for all sorts of movements, we focus on the use case of vehicular trajectory compression. We take a close look at state-of-the-art solutions that are based on geometric operations, namely line simplification and linear dead reckoning. We argue that though achieving good results, these models can not be the best possible approach with respect to the achieved compression ratio, because vehicular movements are not linear. Instead, we motivate the use of nonlinear algorithms and propose two geometric compression algorithms based on clothoid spline sketching and cubic spline interpolation, respectively. By means of an evaluation based on real-world trajectory data, we show that nonlinear algorithms can provide a better compression ratio than the optimal line simplification solution. While our spline interpolation based algorithm provides the best compression ratios, it implements a heuristic and therefore merely finds a locally optimal solution. We therefore turn to an information-theoretic approach and aim at finding an algorithm that provides the optimal compression ratio for a trajectory. We therefore use the Shannon information content to define the information content of a trajectory and propose a method to measure it, using a movement estimator, a discretization technique and a probability distribution. We suggest and evaluate different component implementations and find out that implementing an arithmetic coder based on this method yields significantly better compression ratios than the geometric approaches. We finally turn to lossless compression algorithms and consider the use of conventional byte string compression algorithms, such as several algorithms from the LZ family, the bzip2 algorithm and arithmetic coding. A plain application of these algorithms to the trajectories would not produce meaningful results, though, because subsequently measured trajectory positions that are close to each other are not necessarily represented by similar byte sequences that would be easy to compress. We therefore propose a byte encoder that preprocesses a trajectory into a form that can be compressed more effectively by the selected conventional compression algorithms. We evaluate the byte coder based on the same trajectory set and see that the achieved compression ratio nearly matches the results from the lossy arithmetic coder for the lowest selected error tolerance. With these results, we provide an answer to the question of how spatio-temporal trajectories can be compressed so that a maximum compression ratio can be achieved with respect to the application's accuracy requirements." @default.
• W836690559 created "2016-06-24" @default.
• W836690559 creator A5001793558 @default.
• W836690559 date "2013-01-01" @default.
• W836690559 modified "2023-09-27" @default.
• W836690559 title "A Long Movement Story Cut Short - On the Compression of Trajectory Data" @default.
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