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Lightweight Temporal Compression (LTC) is among the lossy stream compressionmethods that provide the highest compression rate for the lowest CPU and memoryconsumption. As such, it is well suited to compress data streams inenergy-constrained systems such as connected objects. The current formulationof LTC, however, is one-dimensional while data acquired in connected objects isoften multi-dimensional: for instance, accelerometers and gyroscopes usuallymeasure variables along 3 directions. In this paper, we investigate theextension of LTC to higher dimensions. First, we provide a formulation of thealgorithm in an arbitrary vectorial space of dimension n. Then, we implementthe algorithm for the infinity and Euclidean norms, in spaces of dimension 2D+tand 3D+t. We evaluate our implementation on 3D acceleration streams of humanactivities. Results show that the 3D implementation of LTC can save up to 20%in energy consumption for low-paced activities, with a memory usage of about100 B.

A multi-dimensional extension of the Lightweight Temporal Compression method