  Lightweight Temporal Compression (LTC) is among the lossy stream compression
methods that provide the highest compression rate for the lowest CPU and memory
consumption. As such, it is well suited to compress data streams in
energy-constrained systems such as connected objects. The current formulation
of LTC, however, is one-dimensional while data acquired in connected objects is
often multi-dimensional: for instance, accelerometers and gyroscopes usually
measure variables along 3 directions. In this paper, we investigate the
extension of LTC to higher dimensions. First, we provide a formulation of the
algorithm in an arbitrary vectorial space of dimension n. Then, we implement
the algorithm for the infinity and Euclidean norms, in spaces of dimension 2D+t
and 3D+t. We evaluate our implementation on 3D acceleration streams of human
activities. 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 about
100 B.
