Model of functioning of the centralized wireless information ecosystem focused on multimedia streaming

Kovtun, Viacheslav; Izonin, Ivan; Greguš, Michal

doi:10.1016/j.eij.2022.06.009

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…In the background of all-encompassing info-communication systems (Kovtun et al 2022), mediatization (Hepp 2020;Feher 2022), platformization (Van Dijck 2021) and artificial intelligence technologies (AI) develop and spread to extend human-machine communication (HMC, Guzman et al 2023), AI-mediated communication (AI-MC, Hancock et al 2020), generative AI (Pavlik 2023) algorithmic and proxy media (Blanchett et al 2022) or super-human intelligence for interactivity (Guzman et al 2023). These trends engender diverse computational and AI agent roles encompassing senders, receivers, communicators, mediators, producers, editors, authors, creators, designers, analysts, distributors, moderators, and fact-checkers. Although it is useful to delimit the research focus (Hancock et al 2020), expansive research allows the exploration of a vision of a distant future for free, abstract, or specific associations.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Modeling AI Trust for 2050: perspectives from media and info-communication experts

Feher,

Vicsek,

Deuze

2024

AI & Soc

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The study explores the future of AI-driven media and info-communication as envisioned by experts from all world regions, defining relevant terminology and expectations for 2050. Participants engaged in a 4-week series of surveys, questioning their definitions and projections about AI for the field of media and communication. Their expectations predict universal access to democratically available, automated, personalized and unbiased information determined by trusted narratives, recolonization of information technology and the demystification of the media process. These experts, as technology ambassadors, advocate AI-to-AI solutions to mitigate technology-driven misuse and misinformation. The optimistic scenarios shift responsibility to future generations, relying on AI-driven solutions and finding inspiration in nature. Their present-based forecasts could be construed as being indicative of professional near-sightedness and cognitive dissonance. Visualizing our findings into a Glasses Model of AI Trust, the study contributes to key debates regarding AI policy, developmental trajectories, and academic research in media and info-communication fields.

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Section: Theoretical Frameworkmentioning

confidence: 99%

Modeling AI Trust for 2050: perspectives from media and info-communication experts

Feher,

Vicsek,

Deuze

2024

AI & Soc

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“…Однією з головних проблем безпровідного зв'язку є розподіл ресурсів спільного фізичного середовища між активними учасниками. В мобільних мережах 5G актуальним напрямком досліджень на сьогодні є аналіз і оптимізація розподілу ресурсів для передачі різних типів трафіку [1,2], в локальних -аналіз і вдосконалення методів доступу до фізичного середовища [3]. Існує кілька категорій доступу до спільного фізичного середовища, серед яких найчастіше використовується метод на основі конкуренції.…”

Section: постановка проблемиunclassified

“…Для дослідження МАС-підрівня використовують різні математичні апарати, наприклад, марківські процеси [2,8], машинне навчання [6,7], аналітичне та імітаційне моделювання [1-3, 7, 8]. Використовують як власні розроблені програми [3], так і спеціальний інструментарій, такий як Network Simulator, OMNET+, OPNET, Graphical Network Simulator, Matlab/Simulink, Maple, CISCO Packet Tracer та інші.…”

Section: постановка проблемиunclassified

Analysis of the Csma/Ca Scheme for Wireless Local Area Networks

OBELOVSKA,

PELEKH,

PELEKH

et al. 2022

Herald KhmNU.TS

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The current stage of telecommunication technologies development is characterized by the rapid growth use of wireless technologies in access networks and the growth requirements for its quality. The bottleneck that significantly reduces the bandwidth of each subscriber connection is that the total bandwidth of the wireless local network is shared among its active members by the shared technology used. The access management sub-layer to the shared physical environment is responsible for this in the network architecture. The main method used at this sub-layer is the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) scheme. This work is devoted to the analysis of CSMA/CA scheme and the development of an analytical model for its further study and use for the improvement of wireless local networks. The input data is a transition state diagram of a local area wireless network station in data transmission mode. According to the CSMA/CA scheme, stations cannot change their state after starting frame transmission until a confirmation frame of successful reception arrives from the addressee or until the time to wait for it expires. Considering this, it is proposed to use a simplified transition state diagram for the analysis. The operation of the station by the simplified transition state diagram is described using a system of differential equations. As a result of the solution of this system, expressions representing the probability of the station being in each of its states as a function of the intensities of the station’s transitions from each state to another are derived. The values of these intensities depend on both the outgoing traffic of the station and the traffic in the network. The resulting expressions can be used in further work on the analysis of the CSMA/CA scheme, as they make it possible to estimate the operation of the station at different ratios of the station’s output traffic and external traffic generated by other stations of the network. This can be used to develop an enhanced algorithm station operation of wireless local area networks by adapting the parameters of the CSMA/CA scheme to specific current loads of both the station and the network.

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“…Most of the data generated in the modern world are video [1,2]. The task of video compression has become an integral part of modern multimedia communication systems [3]. The ability to compress videos without losing quality allows for efficient storage, transfer, and playback of videos.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning-Based and Conventional Technologies for Video Compression

Mochurad

2024

Technologies

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The growing demand for high-quality video transmission over bandwidth-constrained networks and the increasing availability of video content have led to the need for efficient storage and distribution of large video files. To improve the latter, this article offers a comparison of six video compression methods without loss of quality. Particularly, H.255, VP9, AV1, convolutional neural network (CNN), recurrent neural network (RNN), and deep autoencoder (DAE). The proposed decision is to use a dataset of high-quality videos to implement and compare the performance of classical compression algorithms and algorithms based on machine learning. Evaluations of the compression efficiency and the quality of the received images were made on the basis of two metrics: PSNR and SSIM. This comparison revealed the strengths and weaknesses of each approach and provided insights into how machine learning algorithms can be optimized in future research. In general, it contributed to the development of more efficient and effective video compression algorithms that can be useful for a wide range of applications.

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Model of functioning of the centralized wireless information ecosystem focused on multimedia streaming

Cited by 8 publications

References 27 publications

Modeling AI Trust for 2050: perspectives from media and info-communication experts

Modeling AI Trust for 2050: perspectives from media and info-communication experts

Analysis of the Csma/Ca Scheme for Wireless Local Area Networks

A Comparison of Machine Learning-Based and Conventional Technologies for Video Compression

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