2 results listed
Wireless Sensor Networks (WSN) are used for the
monitoring of objects in various fields of application as well as the
monitoring of military and civilian environments. The energy
consumption of sensors and the optimization of network lifetime
in WSNs are among the important problems that are constantly
investigated and for which solutions are developed by linear
programming method. Furthermore, different algorithmic
solutions have been developed to perform the dynamic deployment
of the nodes efficiently for the solution of this problem. The
proposed solutions require that the targets in the network are
covered by a minimum number of sensors. k-coverage, that
determines the degrees of coverage of the targets in the area of
interest, is an important criterion in determining the number of
sensors covering each target after the deployment of the sensors.
Because the coverage of the targets by a minimum number of
sensors and the minimization of the intersection area of the sensor
increase the lifetime of the network by optimizing the energy
consumptions of the sensors.
In this study, the dynamic deployment approach based on the
Whale Optimization Algorithm was proposed to provide the
optimum solution to the k-coverage problem of WSNs by ensuring
that the targets in the area are covered by a minimum number of
sensors. This approach, that performs the effective dynamic
deployment of sensors by covering the maximum number of target
points and ensuring the minimum degree of k-coverage, was
compared with the MADA-EM in the literature. Simulation
results have shown that this approach is optimum and can be
recommended in the solution of the k-coverage problem by
ensuring that the targets are covered by a minimum number of
nodes.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Recep Özdağ
Murat Canayaz
Nowadays, it is tried to predict the future events through the data. Practical areas such as deep learning are primarily trying to regulate data, and then these data are used for estimation. There are many algorithms used in this area. Besides these algorithms, artificial neural networks are also widely used in this field. ANFIS is a special network that uses artificial neural network and fuzzy classifier. It computes the output by distributing the input data blurred by the membership functions with the fuzzy rules on the network. Some parameter values need to be set in ANFIS. In this study, ANFIS networks will be trained with the Whale Optimization Algorithm, one of the current swarm-based meta-heuristic algorithms to find suitable parameter values and evaluation will be made on sample problems.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Murat Canayaz
Recep Özdağ