RETRACTED: An efficient three-dimensional node localization using recurrent neural networks in unmanned aerial vehicle-assisted wireless networks: an optimization perspective
International Journal of Intelligent Unmanned Systems
ISSN: 2049-6427
Article publication date: 17 September 2024
Issue publication date: 25 November 2024
Retraction notice
The publisher of International Journal of Intelligent Unmanned Systems wishes to retract the article Negassa, W.G., Gelmecha, D.J., Singh, R.S. and Rathee, D.S. (2024), “An efficient three-dimensional node localization using recurrent neural networks in unmanned aerial vehicle-assisted wireless networks: an optimization perspective”, International Journal of Intelligent Unmanned Systems, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJIUS-04-2024-0112.
It has come to our attention that a large portion of this article is taken, without full and proper attribution, from an earlier original work by Visalakshi Annepu, Kalapraveen Bagadi, Naga Raju Challa, Vaegae Naveen Kumar, Yamarthi Narasimha Rao (2024), “An Efficient Localization for Resource Management in UAV-Assisted Wireless Networks Using Deep Learning”, in Bhowmick, A., Kumar Choukiker, Y., Singh, I., & Nallanathan, A. (Eds.). (2024). 5G and Beyond Wireless Communications: Fundamentals, Applications, and Challenges (1st ed.). CRC Press. https://doi.org/10.1201/9781032625034 The submission guidelines for International Journal of Intelligent Unmanned Systems make it clear that articles must be original and not infringe any existing copyright. The authors of this paper would like to note that they do not agree with the content of this notice.
The publisher of the journal sincerely apologizes to the readers.
The retracted article is available at: https://doi.org/10.1108/IJIUS-04-2024-0112
Abstract
Purpose
Unlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.
Design/methodology/approach
The paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.
Findings
Simulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.
Originality/value
Conventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.
Keywords
Citation
Negassa, W.G., Gelmecha, D.J., Singh, R.S. and Rathee, D.S. (2024), "RETRACTED: An efficient three-dimensional node localization using recurrent neural networks in unmanned aerial vehicle-assisted wireless networks: an optimization perspective", International Journal of Intelligent Unmanned Systems, Vol. 12 No. 4, pp. 473-490. https://doi.org/10.1108/IJIUS-04-2024-0112
Publisher
:Emerald Publishing Limited
Copyright © 2024, Emerald Publishing Limited