TY  - JOUR
T1  - Hybrid Optimized Multi Sink Network for Optimal Data Aggregation in Wireless Sensor
Network Using Genetic Algorithm and Invasive Weed Optimization
AU - Mummana, Satyanarayana AU - Nageswara Rao, Kuda 
JO  - Asian Journal of Information Technology
VL  - 18
IS  - 8
SP  - 193
EP  - 202
PY  - 2019
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2019.193.202
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2019.193.202
KW  - Low Energy Adaptive Clustering Hierarchy (LEACH)
KW  -Cluster Head (CH) selection
KW  -clustering
KW  -Wireless Sensor Network (WSN)
KW  -Genetic Algorithm (GA)
KW  -Invasive Weed Optimization (IWO)
AB  - Wireless Sensor Network (WSN) is a network
that is formed using many sensor nodes which are
positioned inside an application based environment for
monitoring the physical entities within a target area. A
primary challenge in the organizing of such networks is
its efficacy of energy. Clustering is found to be an
efficient technique that can prolong the WSN lifetime. It
includes the grouping of the sensor nodes into various
clusters and also the electing of the Cluster Heads (CH)
for the clusters. The CHs will collect data from their
respective cluster’s and their nodes to forward all
aggregated data to the Base Station (BS). The CH
selection is a Non-deterministic Polynomial (NP)-hard
problem. This study proposes a very energy efficient CH
algorithm for selection that has been based on the Genetic
Algorithm (GA) and the Invasive Weed Optimization
(IWO) algorithm. The sensor network and its lifetime will
be extended using the power based clustering protocols.
The operations in clustering will be optimized using the
optimizations of swarm and their Evolutionary
Algorithms (EA). Here, there is an improved IWO based
upon the hybrid genetic (GA-IWO) has been presented. In
this new algorithm, the inertial weight is adaptively
adjusted for improving the speed of convergence and the
weeds are multipled by means of selection and
hybridization of the GA. This import of such hybrid genes
will improve the t performance of weeds and will further
reduce the likelihood of getting into the local optima. The
results of the experiment prove that the method proposed
can achieve better performance compared to the other
methods.
ER  -