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Smart city article details
| Title | Predictive Estimation Of Optimal Signal Strength From Drones Over Iot Frameworks In Smart Cities |
|---|---|
| ID_Doc | 42886 |
| Authors | Alsamhi S.H.; Almalki F.A.; Ma O.; Ansari M.S.; Lee B. |
| Year | 2023 |
| Published | IEEE Transactions on Mobile Computing, 22, 1 |
| DOI | http://dx.doi.org/10.1109/TMC.2021.3074442 |
| Abstract | The integration of drones, the Internet of Things (IoT), and Artificial Intelligence (AI) domains can produce exceptional solutions to today complex problems in smart cities. A drone, which essentially is a data-gathering robot, can access geographical areas that are difficult, unsafe, or even impossible for humans to reach. Besides, communicating amongst themselves, such drones need to be in constant contact with other ground-based agents such as IoT sensors, robots, and humans. In this paper, an intelligent technique is proposed to predict the signal strength from a drone to IoT devices in smart cities in order to maintain the network connectivity, provide the desired quality of service (QoS), and identify the drone coverage area. An artificial neural network (ANN) based efficient and accurate solution is proposed to predict the signal strength from a drone based on several pertinent factors such as drone altitude, path loss, distance, transmitter height, receiver height, transmitted power, and signal frequency. Furthermore, the signal strength estimates are then used to predict the drone flying path. The findings show that the proposed ANN technique has achieved a good agreement with the validation data generated via simulations, yielding determination coefficient R^2R2 to be 0.96 and 0.98, for variation in drone altitude and distance from a drone, respectively. Therefore, the proposed ANN technique is reliable, useful, and fast to estimate the signal strength, determine the optimal drone flying path, and predict the next location based on received signal strength. © 2002-2012 IEEE. |
| Author Keywords | Artificial neural network (ANN); drone; internet of things (IoT); quality of service (QoS); signal strength prediction; smart city |
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