An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities
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 imp...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/7387346 |
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| author | Mohamad Reda A. Refaai Vinjamuri S. N. C. H. Dattu H. S. Niranjana Murthy P. Pramod Kumar B. Kannadasan Abdi Diriba |
| author_facet | Mohamad Reda A. Refaai Vinjamuri S. N. C. H. Dattu H. S. Niranjana Murthy P. Pramod Kumar B. Kannadasan Abdi Diriba |
| author_sort | Mohamad Reda A. Refaai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2e24bdb7c8054b3e944208f05907e841 |
| institution | Kabale University |
| issn | 1687-8442 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-2e24bdb7c8054b3e944208f05907e8412025-02-03T01:24:08ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/7387346An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart CitiesMohamad Reda A. Refaai0Vinjamuri S. N. C. H. Dattu1H. S. Niranjana Murthy2P. Pramod Kumar3B. Kannadasan4Abdi Diriba5Department of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Electronics and Instrumentation EngineeringDepartment Computer Science and Artificial IntelligenceDepartment of Civil EngineeringDepartment of Mechanical EngineeringDrones, 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.http://dx.doi.org/10.1155/2022/7387346 |
| spellingShingle | Mohamad Reda A. Refaai Vinjamuri S. N. C. H. Dattu H. S. Niranjana Murthy P. Pramod Kumar B. Kannadasan Abdi Diriba An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities Advances in Materials Science and Engineering |
| title | An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities |
| title_full | An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities |
| title_fullStr | An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities |
| title_full_unstemmed | An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities |
| title_short | An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities |
| title_sort | artificial intelligence mechanism for the prediction of signal strength in drones to iot devices in smart cities |
| url | http://dx.doi.org/10.1155/2022/7387346 |
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