  Sensor nodes in a wireless sensor network (WSN) for security surveillance
applications should preferably be small, energy-efficient, and inexpensive with
in-sensor computational abilities. An appropriate data processing scheme in the
sensor node reduces the power dissipation of the transceiver through the
compression of information to be communicated. This study attempted a
simulation-based analysis of human footstep sound classification in natural
surroundings using simple time-domain features. The spiking neural network
(SNN), a computationally low-weight classifier derived from an artificial
neural network (ANN), was used to classify acoustic sounds. The SNN and
required feature extraction schemes are amenable to low-power subthreshold
analog implementation. The results show that all analog implementations of the
proposed SNN scheme achieve significant power savings over the digital
implementation of the same computing scheme and other conventional digital
architectures using frequency-domain feature extraction and ANN-based
classification. The algorithm is tolerant of the impact of process variations,
which are inevitable in analog design, owing to the approximate nature of the
data processing involved in such applications. Although SNN provides low-power
operation at the algorithm level itself, ANN to SNN conversion leads to an
unavoidable loss of classification accuracy of ~5%. We exploited the low-power
operation of the analog processing SNN module by applying redundancy and
majority voting, which improved the classification accuracy, taking it close to
the ANN model.
