follows a simple processing approach. We use a general workflow that
has been extracted by observing several different solutions for
information abstraction. The existing solutions either follow the steps
shown in the figure below or implement some parts of it. We identified
the following main steps: Pre-processing to bring the data into shape
for further processing, Dimensionality Reduction to either compress the
data or reduce its feature vectors, Feature Extraction to find
low-level Abstractions in local sensor data, Abstraction from raw data
to higher-level Abstractions and finally semantic representations to
make the abstracted data available for the end-user and/or machines
that interpret the data.
(Source: Frieder Ganz, Daniel Puschmann, Payam Barnaghi, Francois Carrez, "A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things", IEEE Internet of Things Journal, 2015.)
Pre-ProcessingThe raw sensory data is pre-processed stage to prepare the data for information acquisition. Pre-processing can be done on sensor nodes to reduce transmission cost or filter unwanted data and can include mathematical/statistical methods to smooth the data by applying moving average windows, or methods from signal processing such as band-, low-, high pass filtering to focus on a certain frequency spectrum.
Transmission cost can be reduced by only sending aggregated information or a selection of data to a base-station or a gateway; e.g. sending minimum and/or maximum values or the mean value of the current window. The pre-processing is not only limited to a single sensor node; some solutions use in-network processing to aggregate the data before further processing it by finding the minimum, mean or maximum value in a set of sensor nodes and before transmitting the data to a base station. In addition to data aggregation, in-network techniques can also be used to improve the accuracy of the data by calculating correlation with data from neighbouring nodes.
To handle the large amount of data that has to be processed and stored.
Dimensionality reduction can decrease the size and length of samples by
applying different methods on the data while preserving the important
features and patterns.
Institute for Communication Systems (ICS),
University of Surrey