Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model

Faris A. Almalki, Maha Aljohani, Merfat Algethami and Ben Othman Soufiene
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Faris A. Almalki: Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
Maha Aljohani: Software Engineering Department, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
Merfat Algethami: Physics Department, Faculty of Science, Taif University, Taif 21944, Saudi Arabia
Ben Othman Soufiene: PRINCE Laboratory Research, ISITcom, Hammam Sousse, University of Sousse, Sousse 4023, Tunisia

Sustainability, 2022, vol. 14, issue 7, 1-24

Abstract: In the recent digital age, information and communication technologies are rapidly contributing to remodel the media and journalism. Numerous technologies can be utilized by the media industry to capture news or events, taking footage and pictures of a breaking news. Technology and the media are interwoven, and neither can be detached from contemporary society in most nations. Unsurprisingly, technology has affected how and where information is shared. Nowadays, it is impractical to discuss media and the methods in which societies communicate without addressing the rapidity of technology change. Thus, the aerial journalism term has emerged, which refers to the ability of creating and conveying media content in a timely and efficient fashion. This work aims to integrate a drone with AI to empower aerial journalism via training a neural network to obtain an accurate channel using the NN-RBFN approach. The proposed work can enhance aerial media missions including investigative reporting (e.g., humanitarian crises), footage of news events (e.g., man-made and/or natural disasters), and livestreams for short-term, large-scale events (e.g., Olympic Games). In our digital media era, such a smart journalism approach would help to become far more sustainable and an eco-efficient process. Both MATLAB and 3D Remcom Wireless Insite tools have been used to carry out the simulation work. Simulated results indicate that the proposed NN-RBFN managed to obtain an accurate channel propagation model in a 3D scenario with a high accuracy rate reaching 99%. The proposed framework also could offer various media and journalism services (e.g., high data rate, wider coverage footprint) in timely and cost-effective manners in both normal scenarios or even in hard-to-reach zones and/or short-term, large-scale events.

Keywords: unmanned aerial vehicles; drones; digital media; aerial journalism; smart media; channel modeling; propagation model; artificial intelligence (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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