RETRACTED: An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities (Retracted Article)

被引:0
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作者
Refaai, Mohamad Reda A. [1 ]
Dattu, Vinjamuri S. N. C. H. [2 ]
Murthy, H. S. Niranjana [3 ]
Kumar, P. Pramod [4 ]
Kannadasan, B. [5 ]
Diriba, Abdi [6 ]
机构
[1] Prince Sattam Bin Abdulaziz Univ, Coll Engn, Dept Mech Engn, Alkharj 16273, Saudi Arabia
[2] Aditya Engn Coll, Dept Mech Engn, Surampalem, Andhra Pradesh, India
[3] Ramaiah Inst Technol, Dept Elect & Instrumentat Engn, Bangalore 560054, Karnataka, India
[4] SR Univ, Dept Comp Sci & Artificial Intelligence, Warangal, Telangana, India
[5] BS Abdur Rahman Crescent Inst Sci & Technol, Dept Civil Engn, Chennai 600048, Tamil Nadu, India
[6] Mizan Tepi Univ, Dept Mech Engn, Tepi, Ethiopia
关键词
PERFORMANCE; INTERNET; STATION;
D O I
10.1155/2022/7387346
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Drones, the Internet of Things (IoT), and Artificial Intelligence (AI) could be used to create extraordinary responses to today's difficulties in smart city challenges. A drone, which would be effectively a data-gathering device, could approach regions that become complicated, dangerous, or even impossible to achieve for individuals. In addition to interacting with one another, drones must maintain touch with some other ground-based entities, including IoT sensors, robotics, and people. Throughout this study, an intelligent approach for predicting the signal power from a drone to IoT applications in smart cities is presented in terms of maintaining internet connectivity, offering the necessary quality of service (QoS), and determining the drone's transmission range offered. Predicting signal power and fading channel circumstances enables the adaptable transmission of data, which improves QoS for endpoint users/devices while lowering transmitting data power usage. Depending on many relevant criteria, an artificial neural network (ANN)-centered precise and effective method is provided to forecast the signal strength from such drones. The signal strength estimations are also utilized to forecast the drone's flight patterns. The results demonstrate that the proposed ANN approach has an excellent correlation with the verification data collected through computations, with the determination of coefficient R2 values of 0.97 and 0.98, correspondingly, for changes in drone height and distances from a drone. Furthermore, the finding shows that signal distortions could be considerably decreased and strengthened.
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页数:13
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