Abstract
The flying networks provide an efficient solution for a wide range of military and commercial purposes. The demand for portable and flexible communication is directed towards a quick growth in interaction among unmanned aerial vehicles (UAVs). Due to the frequent change in topology and high mobility of vehicles, routing and coordination becomes a challenging task. To maximize the throughput of the network, this study addresses the UAV swarm’s problems related to the coordination and routing and defines the proposed solution to solve these issues. For this, a network is assumed which contains an equal number of dynamic vehicles. It also presents the communication graph concept of UAVs and designs a fixed-wing UAV model to improve the efficiency of the network in terms of throughput. Furthermore, the proposed algorithm based on Cauchy particle swarm optimization (CPSO) aims towards the better performance of UAV swarms and aims to solve the combinatorial problem. The simulation results show and confirm the performance of the proposed algorithm.
Highlights
In recent years, unmanned aerial vehicles (UAVs) have gained interest and have been applied in the area of search and rescue, agriculture defence, transportation, monitoring, and surveillance [1]. ese vehicles can cover wider areas in the field. e coordination and monitoring system is needed for the fleet to determine their route and utilize this resource in a very efficient manner [2]. e UAV technology is known as utilizing a network of UAVs
Many vehicles communicate via air to ground or air to air along with their control stations [3,4,5]. is technology provides flexible configuration, improvement in the communication performance, and line-of-sight communication link on demand
Is study presents the Cauchy particle swarm optimization (CPSO) algorithm for UAV network communication which includes the coordination and routing among them. is algorithm is applied to solve node localization of UAVs in space and to solve combinational issues. It reduces the combinational time and improves the performance of the swarms. e proposed algorithm has a convergence ability, reduces the localization error and endto-end delay, and improves the accuracy and information delivery ratio. e simulation results show the performance of the proposed algorithm along with particle swarm optimization (PSO)
Summary
In recent years, unmanned aerial vehicles (UAVs) have gained interest and have been applied in the area of search and rescue, agriculture defence, transportation, monitoring, and surveillance [1]. ese vehicles can cover wider areas in the field. e coordination and monitoring system is needed for the fleet to determine their route and utilize this resource in a very efficient manner [2]. e UAV technology is known as utilizing a network of UAVs. E UAV network information sharing is a more challenging problem limited to the throughput especially in disaster areas It affects the overall effectiveness of the operation. For UAV swarms, different methods and techniques are designed for an optimal solution. In [13], the study provides the solution for the routing problem of the vehicle. In [14], the improved version of PSO is called combinatorial particle swarm optimization which is used to find the number of clusters. In [16], clustering particle swarm optimization (CPSO) is proposed for the efficiency of search. In [18], particle swarm optimization (PSO) with software-defined networking-based communication protocol is proposed which guarantees network management.
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