Sensor Network (WSN) are said to be a complex homogeneous telecommunication
system that has numerous tiny distributed battery controlled devices named as sensors.
The sensor nodes collaborate very well with each other in order to do sensing
and computing tasks, they communicate and interact wirelessly. The lifetime of
WSN is mostly affected by the confinements of its sensors/nodes devices. Thus
far, power insufficiency is a major challenge of research in the area of WSN’s
lifetime. The way to optimize WSN’s lifetime clustering approaches and
hierarchal routing protocols have been proposed. This enables the transformation from homogeneous
to heterogeneous deployment. It is known that heterogeneous networks are more
useful in WSN. Therefore, it gathers the sensor nodes into many groups named
clusters. Each of these clusters has only a single connected node of
centralization called cluster head.
challenge of WSN is performance optimization of its sensor nodes in order to
reduce energy scarcity. Consequently, lengthening the network lifetime. For the
purpose of extending the lifetime, WSN clustering algorithm based flower
pollination optimization algorithm was proposed used. Another important
enhancement for the WSN lifetime is by connecting the cluster nodes in
consideration to the suitable cluster head. Flower pollination optimizes the
creation of clusters, it is the aim of the intra-cluster distances and based on
that optimization (fitness) function it optimally can connect the cluster’s
nodes to each cluster. Furthermore, it chooses the best cluster heads
distribution that makes sure an optimized route with the least communication
links’ cost between nodes in every cluster.
for optimizing the WSN are as follows: Network model; where it first deploys a
WSN environment with a sized area (M x N). Then in the WSN deployed
field which is a static deployment, the energy controlled stationary sensor
devices are scattered arbitrarily. Additionally, assuming that the sensors are
sending data to the intended node regularly and that these nodes are located
near to each other with having correlated data. Then Energy Model; according
to the first order radio energy model the nodes are set with their initialized energies
of all the nodes. In symmetrical communication channels the nodes are transferring
message with k bits with a distance of d, thus, consume energy. According
to the distance between sender and receiver the calculation of energy was done.
After that the CH selection process starts; in which each cluster taken from the
flower pollination clustering a cluster head is candidate to be selected, the
one with the most remaining energy. Next the cluster formation; as for this
part, the cluster formation completely depends on flower pollination
optimization algorithm as it searches to get the best distribution of nodes on
clusters. The aim of the fitness function is utilized to reduce the
intra-cluster density with least distance between nodes in the same cluster. Last
procedure is the Data Transmission; whereas the sink node is directly connected
with each CH, the sink node is a base station that is a high-energy node,
placed farther from the sensor nodes in the points (Xsink, Ysink).
The sensor nodes transfer their data packets straight to CH and each of these
CH get data from all of its cluster nodes. Hence, to do important iterations
for compression and it is directly associated with the sink node in order to
forward the accumulated data packets. Therefore, the nodes die when they no
longer have energy.