A network of small,
low cost sensor nodes with basic functionality, spread out in an area for
sensing specific parameters of the environment and with wireless links is
generally termed as a Wireless sensor network (WSN). Each node in a WSN usually
consists of an independent power source, a sensor unit, a transceiver unit and
a processing unit making each wireless sensor node autonomous. This enables
large scale distribution of these nodes over larger areas for better and faster
data gathering. Recent technological advances have made manufacture of small,
low cost sensors nodes technologically and economically feasible 1.

Characteristics of WSN, like mobility of nodes, ease of addition
or removal nodes to the network, heterogeneity of the nodes and ability to cope
with node failure, have ensured a wide range of application of wireless sensor
networks. Precision Agriculture,
Environmental Monitoring, Vehicle Tracking, Health care Monitoring, Smart
Buildings, Military Applications, Animal Tracking are few applications of WSNs
2. The information gathered by the wireless sensor nodes, in certain
applications, becomes useful only when locations from where the data has been
collected is known. Hence, Localisation of wireless sensor nodes is equally
important as sensing, especially for applications like tracking intruders in
battlefield, locating objects in building, self-driven cars and emergency
response applications 3.

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Several
algorithms have been developed for localization of nodes in a WSN. Global
Positioning System (GPS) is currently the most commonly used positioning
systems in the world 4. However, GPS cannot be used in applications where the
wireless sensor nodes are small and it is not feasible to configure each node
with GPS and in applications where the nodes are spread out in indoors and are
heavily dense. Hence many other algorithms are used for such situations. These
algorithms can be broadly classified into Range based and Range free algorithms.
Time of Signal Arrival (TOA), Time Difference of Signal Arrival (TDOA), Angle
of Arrival and Received Signal Strength Indicator (RSSI) are few range based
localization algorithms. RSSI based localisation is more popular because it is
simple and does not require any sophisticated hardware 5.

 

Unlike in outdoor,
localization parameters would not remain constant in complex indoor
environments. The wireless signal is more severely affected by multipath error,
diffraction, obstacles, the direction of antennas and other factors 6.
Because of these, a huge amount of noise would be added to RSSI and would result
in error in localization. To reduce the error, feedback error control using
multiplicative distance-correction factor, Time Window Statistics, UWB Ranging and other techniques are used
789. However, all these techniques increase the computational time
required for localization.

 

               In this paper, we
discuss how the error in localization of nodes can be reduced with help of
neural networks. Neural networks do not take much additional computational time
or power, all they require is training prior to deployment. Neural networks are
able to reduce the error significantly as they are non-linear. They are able to
compute the output quickly as they compute the output parallelly. In section II
of the paper gives a brief about some related works – RSSI and regression in
neural networks. In section III, we would be discussing how to train a neural
network and how can they be used in controlling error in localization results.  Simulation and results are discussed in section
IV and the summary and conclusion are in section V.

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