Predictive Estimation of Optimal Signal Strength From Drones Over IoT Frameworks in Smart Cities

被引:53
作者
Alsamhi, Saeed Hamood [1 ]
Almalki, Faris. A. [2 ]
Ma, Ou [3 ]
Ansari, Mohammad Samar [4 ]
Lee, Brian
机构
[1] Athlone Inst Technol, Athlone, Ireland
[2] Taif Univ, Coll Comp & Informat Technol, Dept Comp Engn, Taif, Saudi Arabia
[3] Univ Cincinnati, Coll Engn & Appl Sci, Cincinnati, OH 45219 USA
[4] Aligarh Muslim Univ, Aligarh, Uttar Pradesh, India
基金
爱尔兰科学基金会; 欧盟地平线“2020”;
关键词
Drones; Internet of Things; Predictive models; Quality of service; Data models; Computational modeling; Smart cities; Artificial neural network (ANN); drone; internet of things (IoT); signal strength prediction; quality of service (QoS); smart city; NEURAL-NETWORK; ALTITUDE; INTERNET; THINGS; MODEL; QOS;
D O I
10.1109/TMC.2021.3074442
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 $R<^>2$R2 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.
引用
收藏
页码:402 / 416
页数:15
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