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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Faris A. Almalki & Maha Aljohani & Merfat Algethami & Ben Othman Soufiene, 2022. "Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model," Sustainability, MDPI, vol. 14(7), pages 1-24, March.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:3758-:d:777178
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    References listed on IDEAS

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    1. Michail Niarchos & Marina Eirini Stamatiadou & Charalampos Dimoulas & Andreas Veglis & Andreas Symeonidis, 2022. "A Semantic Preprocessing Framework for Breaking News Detection to Support Future Drone Journalism Services," Future Internet, MDPI, vol. 14(1), pages 1-19, January.
    2. Jonas Harvard & Mats Hyvönen & Ingela Wadbring, 2020. "Journalism from Above: Drones and the Media in Critical Perspective," Media and Communication, Cogitatio Press, vol. 8(3), pages 60-63.
    3. Catherine Adams, 2020. "Dual Control: Investigating the Role of Drone (UAV) Operators in TV and Online Journalism," Media and Communication, Cogitatio Press, vol. 8(3), pages 93-100.
    4. John V. Pavlik, 2020. "Drones, Augmented Reality and Virtual Reality Journalism: Mapping Their Role in Immersive News Content," Media and Communication, Cogitatio Press, vol. 8(3), pages 137-146.
    5. James F. Hamilton, 2020. "Drone Journalism as Visual Aggregation: Toward a Critical History," Media and Communication, Cogitatio Press, vol. 8(3), pages 64-74.
    6. Faris A. Almalki & Ben Othman Soufiene & Saeed H. Alsamhi & Hedi Sakli, 2021. "A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs," Sustainability, MDPI, vol. 13(11), pages 1-26, May.
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    Cited by:

    1. Hongbin Hu & Yongbin Wang, 2022. "Research on Convergence Media Consensus Mechanism Based on Blockchain," Sustainability, MDPI, vol. 14(17), pages 1-27, September.
    2. Shen, Guorong, 2022. "AI-enabled talent training for the cross-cultural news communication talent," Technological Forecasting and Social Change, Elsevier, vol. 185(C).

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