Abstract
The tremendous increase in the amount of GPS (GPS) data transferred by Internet of Things (IoT) devices emphasized the importance of GPS compression techniques. The compression techniques target decreasing the size of data representing the trajectory to minimize the cost of transferring and processing these data. Key aspects of compression techniques is to achieve highest possible accuracy with maximum compression ratio as possible in a reasonable time. This paper targets compression in IoT domain, prioritizing the complexity of the algorithm (performance) key aspect. An implementation for the batched Sliding Window (SW) technique is introduced in this paper, in addition to two novel approaches. A real time Fuzzy Sampler (FS) approach using fuzzy compression that resulted in a significant improvement in the running time compared to the offline and batched SW techniques, while resulting in a smaller compression ratio. Another approach Fuzzy Sampling Window Segmentation (FSW) is to add the FS as a filter to the batched SW technique which resulted in a significant improvement in performance compared to traditional techniques and small decrease in compression ratio. The FS technique is more suitable for low complexity hardware, where small computational power are available, while the FSW requires higher computational power compared to FS and results in higher compression ratios, thus lower transfer cost.
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ElZonkoly, M.E., Madbouly, M.M., Gurguis, S.K. (2023). Real Time Adaptive GPS Trajectory Compression. In: Hassanien, A.E., Snášel, V., Tang, M., Sung, TW., Chang, KC. (eds) Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022. AISI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-031-20601-6_32
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