Abstract:
Wireless sensor networks (WSNs) are an important means of collecting data in a variety of
situations, such as monitoring large or dangerous areas. To do so these wireless sensor networks
use a variety of sensors that are used to collect data locally. These battery-powered sensors
naturally make the battery a major problem in that type of network. WSN data retrieval can
produce better results using unmanned aerial vehicles (UAVs). Remotely controlled, the UAV
has the ability to reach everywhere and accomplish any costly task if done by humans.
Airless Vehicles (UAVs) have gained increasing popularity in WSNs which often use
more sensors in a relatively wide area. Since battery capacity is limited, sensors cannot transmit
long distances. It is necessary to design effective sensor data collection methods to extend the
life of WSNs and improve the efficiency of data collection. In this paper, we consider the state of
UAV-enabled data collection, in which the UAV can move parabolic near each SN when it
collects data from it and thus reduce the connection distance to maintain the transmission
capacity of SNs.
Our goal is to reduce the power consumption of the nodes by reducing the total distance of the
nodes from the UAV and avoiding data sales leading to additional power consumption by
considering the parabola axis. This work describes the parabolic trajectory of UAVs in WSN
data collection with a limited range of communication with a large axis of parabolic pathway
techniques. The results show that duplication of data was avoided which avoids the use of node
power during communication.
With our new approach we will test the effectiveness of our approach in comparison to the
existing line. Performance metrics are used to evaluate the power consumption of a node with a
variety of nodes. Functional simulation is performed using the MATLAB simulator. In our
proposed method, the total distance of nodes from the UAV up to 46.3% is shorter than other
alternatives (linear trajectory). As a result, this reduces global energy consumption by up to
49.2% when compared to our line-up work.
