Background: According to nodes’ participating style, routing protocols can be classified into
three categories, namely, direct communication, flat and clustering protocols. In direct communication
protocols, a sensor node sends data directly to the sink. Under this protocol, if the diameter of the network
is large, the power of sensor nodes will be drained very quickly. Furthermore, as the number of
sensor nodes increases, collision becomes a significant factor which defeats the purpose of data transmission.
Under flat protocols, all nodes in the network are treated equally. When a node needs to send
data, it may find a route consisting of several hops to the sink. Normally, the probability of participating
in the data transmission process is higher for the nodes around the sink than those nodes far away from
the sink. So, the nodes around the sink could run out of their power soon. In the clustered routing architecture,
nodes are grouped into clusters, and a dedicated cluster head node collects, processes, and forwards
the data from all the sensor nodes within its cluster. One of the most critical issues in wireless
sensor networks is represented by the limited availability of energy on network nodes thus, making good
use of energy is necessary to increase network lifetime.
Methods: Review of literature survey and patents has been conducted on the use of artificial neural networks
with wireless sensor networks. While working with wireless sensor network the key concern is
energy conservation. It is quite important to extend the network’s lifetime and to reduce the energy consumption
as the nodes used are battery powered and provided with a fixed amount of initial energy. So
for such a target, we are localizing the base-station of the network. The base-station of the network
should be placed in such a way that the energy consumption of nodes is reduced to some extent. We are
using the artificial neural network approach for finding the optimized position of the base station.
Results: The proposed base-station localization algorithm using artificial neural network is when embedded
to LEACH protocol, then it gives better result than the LEACH protocol in which the basestation
is positioned at the corners of area where sensor nodes are deployed. The results are better as
shown in above figures, in terms of – number of nodes dead to number of rounds, energy consumption
(in Joules) to number of rounds, number of nodes dying in terms of number of rounds and the number of
packets that are being transmitted to the base-station and the cluster heads. It can be concluded that
LEACH with ANN provides an energy efficient scheme.
Conclusion: In this work, we have applied ANN in base-station localization and embedded in LEACH
protocol. But in future, we can further enhance network lifetime by applying ANN in cluster head selection
mechanism of LEACH and LEACH-C protocol.