Abstract

The rise of embedded systems and wireless technologies led to the emergence of
the Internet of Things (IoT). Connected objects in IoT communicate with each
other by transferring data streams over the network. For instance, in Wireless
Sensor Networks (WSNs), sensor-equipped devices use sensors to capture
properties, such as temperature or accelerometer, and send 1D or nD data streams
to a host system. Power consumption is a critical problem for connected objects
that have to work for a long time without being recharged, as it greatly affects
their lifetime and usability. Data summarization is key for energy-constrained
connected devices, as transmitting fewer data can reduce energy usage during
transmission. Data compression, in particular, can compress the data stream
while preserving information to a great extent. Many compression methods have
been proposed in previous research. However, most of them are either not
applicable to connected objects, due to resource limitation, or only handle
one-dimensional streams while data acquired in connected objects are often
multi-dimensional. Lightweight Temporal Compression (LTC) is among the lossy
stream compression methods that provide the highest compression rate for the
lowest CPU and memory consumption. In this thesis, we investigate the extension
of LTC to multi-dimensional streams. 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, on Neblina, a module integrating multiple sensors developed by our
partner Motsai. Results show that the 3D implementation of LTC can save up to
20% in energy consumption for slow-paced activities, with a memory usage of
about 100 B. Finally, we compare our method with polynomial regression
compression methods in different dimensions. Our results show that our extension
of LTC gives a higher compression ratio than the polynomial regression method,
while using less memory and CPU.