Citation: Younus, H.A.; Koçak, C.
Optimized Routing by Combining Grey
Wolf and Dragonfly Optimization for
Energy Efficiency in Wireless Sensor
Networks. Appl. Sci. 2022, 12, 10948.
https://doi.org/10.3390/app122110948
Academic Editor: Christos Bouras
Received: 26 September 2022
Accepted: 26 October 2022
Published: 28 October 2022
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Article
Optimized Routing by Combining Grey Wolf and Dragonfly
Optimization for Energy Efficiency in Wireless
Sensor Networks
Hiba Apdalani Younus
1
and Cemal Koçak
2,
*
1
Faculty of Technology, Institute of Science and Computer Engineering, Gazi University, Ankara 06560, Turkey
2
Faculty of Technology Computer Engineering, Gazi University, Ankara 06560, Turkey
* Correspondence: ccckocak@gazi.edu.tr; Tel.: +90-312-202-85-69
Abstract:
The rapid development of technology has resulted in numerous sensors and devices for
performing measurements in an environment. Depending on the scale and application, the coverage
and size of a wireless sensor network (WSN) is decided. During the implementation, the energy
consumption and life of the nodes in the WSN are affected by the continuous usage. Hence, in
this study, we aimed to improve the lifespan of the WSN and reduce energy consumption by the
nodes during the data transfer using a hybrid approach. The hybrid approach combines Grey
Wolf Optimization (GWO) and Dragonfly Optimization (DFO) for exploring a global solution and
optimizing the local solution to find the optimum route for the data transfer between the target node
and the control center. The results show that the proposed approach has effective energy consumption
corresponding to the load applied. Our proposed system scored high in the average residual energy
by the number of rounds compared to other methods such as k-means, LEACH-C, CHIRON, and
Optimal-CBR. The first dead node was found after 500 rounds, showing that the proposed model
has nodes with better reliability. It also showed a comparative analysis of the transmission rate of a
packet concerning mobility speed among various methods. The proposed method has the highest
ratio at all mobility speeds, i.e., 99.3, 99.1, 99, 98.8, and 98.6, and our proposed system has the lowest
computational time of all the evaluated methods, 6 s.
Keywords:
energy efficient; wireless sensor networks; grey wolf optimization; dragonfly optimization (DFO)
1. Introduction
A wireless sensor network (WSN) is one of the widely adopted network systems in
industry health care and in many intrusion detection areas. It comprises a network of
devices defined as nodes. The nodes have the ability to sense the environment from the
area under monitoring, gather information, and communicate the collected data by means
of wireless links. The final destination node is called the destination node or sink node. The
data from the initial node are forwarded through multi-hop routing to the destination node
and the collected data are utilized by other networks through the gateway. The structure of
the nodes can be moving or stationary, and they must be aware of their identity location.
Since a WSN does not require cables or wires, it is flexible and may add new nodes or
devices to the network at any time.
The recent developments in WSN extended its application to sophisticated systems.
The challenges in WSNs persist even with the modern developments [
1
]. A WSN is
interconnected with various nodes and forms a network within a range. The expansion
of coverage is determined by the type of application as well as the number of nodes and
devices. The energy used by these nodes depends on the establishment and the remote
areas that require an improved, energy efficient system [
2
]. The data collected by the
sensor nodes must be transmitted consistently over a long period of time. The modern
approaches and recent studies have shown that the data transfer happens through WSN and
Appl. Sci. 2022, 12, 10948. https://doi.org/10.3390/app122110948 https://www.mdpi.com/journal/applsci
