  Anomaly detection has various applications including condition monitoring and
fault diagnosis. The objective is to sense the environment, learn the normal
system state, and then periodically classify whether the instantaneous state
deviates from the normal one or not. A flexible and cost-effective way of
monitoring a system state is to use a wireless sensor network. In the
traditional approach, the sensors encode their observations and transmit them
to a fusion center by means of some interference avoiding channel access
method. The fusion center then decodes all the data and classifies the
corresponding system state. As this approach can be highly inefficient in terms
of energy consumption, in this paper we propose a transmission scheme that
exploits interference for carrying out the anomaly detection directly in the
air. In other words, the wireless channel helps the fusion center to retrieve
the sought classification outcome immediately from the channel output. To
achieve this, the chosen learning model is linear support vector machines.
After discussing the proposed scheme and proving its reliability, we present
numerical examples demonstrating that the scheme reduces the energy consumption
for anomaly detection by up to 53% compared to a strategy that uses time
division multiple-access.
