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

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.

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

“…Thus, the term aerojournalism was born, which refers to the ability to create and deliver media content in a timely and efficient manner through aerial devices. Almalki et al [214] integrated a drone with ML to enable aerojournalism by training a neural network using the Radial Basis Function Network (RBFN) approach.…”

Research on Convergence Media Consensus Mechanism Based on Blockchain

“…(4) The efficiency of system consensus should be considered; (5) The overall security of the system should be considered.…”

“…(2) Operation and maintenance characteristics of convergence media ecology, such as participation, credit, and fairness of nodes in the ecology, should be considered; (3) The generation of relevant functional nodes in the system should have unpredictability; (4) The efficiency of system consensus should be considered; (5) The overall security of the system should be considered.…”

“…In this context, the recombination of various media forms and resources become the main form of media formation. At the same time, with artificial intelligence, big data, blockchain and other technologies gradually applied in the media industry, it finally realized the adjustment of the media industry, forming a diversified and sustainable business model and profit model, and it also realized the intelligent matching information with the user requirements, which greatly improved the operational efficiency of the convergence media [5,6].…”

Optimization Imposition upon Drone Gimbal Control Electronics

“…The drone is becoming increasingly important in a wide range of industries, including agriculture construction [6,7], journalism, [8] and filmmaking [9], among others; and drones of various sizes and shapes have been designed for a wide range of applications proving drones to be a growingly important technological advancement and an increasingly vital tool for modern society.…”