  Wireless Sensor Networks (WSNs) is a promising technology with enormous
applications in almost every walk of life. One of the crucial applications of
WSNs is intrusion detection and surveillance at the border areas and in the
defense establishments. The border areas are stretched in hundreds to thousands
of miles, hence, it is not possible to patrol the entire border region. As a
result, an enemy may enter from any point absence of surveillance and cause the
loss of lives or destroy the military establishments. WSNs can be a feasible
solution for the problem of intrusion detection and surveillance at the border
areas. Detection of an enemy at the border areas and nearby critical areas such
as military cantonments is a time-sensitive task as a delay of few seconds may
have disastrous consequences. Therefore, it becomes imperative to design
systems that are able to identify and detect the enemy as soon as it comes in
the range of the deployed system. In this paper, we have proposed a deep
learning architecture based on a fully connected feed-forward Artificial Neural
Network (ANN) for the accurate prediction of the number of k-barriers for fast
intrusion detection and prevention. We have trained and evaluated the
feed-forward ANN model using four potential features, namely area of the
circular region, sensing range of sensors, the transmission range of sensors,
and the number of sensor for Gaussian and uniform sensor distribution. These
features are extracted through Monte Carlo simulation. In doing so, we found
that the model accurately predicts the number of k-barriers for both Gaussian
and uniform sensor distribution with correlation coefficient (R = 0.78) and
Root Mean Square Error (RMSE = 41.15) for the former and R = 0.79 and RMSE =
48.36 for the latter. Further, the proposed approach outperforms the other
benchmark algorithms in terms of accuracy and computational time complexity.
