Performance optimization of UAV-based IoT communications using a novel constrained gravitational search algorithm

Autor: Ming Ding, Sepehr Ebrahimi Mood, Zihuai Lin, Mohammad Masoud Javidi
Rok vydání: 2021
Předmět:
Zdroj: Neural Computing and Applications. 33:15557-15568
ISSN: 1433-3058
0941-0643
DOI: 10.1007/s00521-021-06178-1
Popis: In the recent years, unmanned aerial vehicles (UAVs) because of their ability to be used as aerial base stations for collecting data from IoT devices have attracted substantial interest in Internet of Things (IoT) systems. In this paper, a novel method has been proposed to increase the quality of the uplink IoT communications and to decrease the transmission power of IoT devices. For this purpose, to compute the UAVs’ trajectory, association of device-to-UAV and IoT transmission power, a novel objective function has been defined to optimize the link quality and energy consumption in the considered UAV-based IoT system. Then, for optimizing this objective function, a novel constrained version of gravitational search algorithm is proposed, which is an NP-hard problem. In this algorithm, to handle the constraints, a multiple constraint ranking method is used. Moreover, to calculate the value of the parameter of this method, a fuzzy logic controller is used to control the exploitation and exploration abilities and improve the performance of this algorithm. To evaluate the performance of the proposed method, simulations have been performed and the results were compared with those of the other methods. the experimental results show that an increase in system throughput and a decrease in the energy consumption of the considered UAV-based IoT system can be achieved simultaneously using the proposed optimization algorithm.
Databáze: OpenAIRE
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