Modern computing: Vision and challenges

Gill, Sukhpal Singh; Wu, Huaming; Patros, Panos; Ottaviani, Carlo; Arora, Priyansh; Pujol, Victor Casamayor; Haunschild, David; Parlikad, Ajith Kumar; Cetinkaya, Oktay; Lutfiyya, Hanan; Stankovski, Vlado; Li, Ruidong; Ding, Yuemin; Qadir, Junaid; Abraham, Ajith; Ghosh, Soumya K.; Song, Houbing Herbert; Sakellariou, Rizos; Rana, Omer; Rodrigues, Joel J.P.C.; Kanhere, Salil S.; Dustdar, Schahram; Uhlig, Steve; Ramamohanarao, Kotagiri; Buyya, Rajkumar

doi:10.1016/j.teler.2024.100116

Cited by 51 publications

(1 citation statement)

References 255 publications

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“…The rapid development of computing power over the last two decades has made it possible to solve problems at speeds that were previously unimaginable [1,2]. This is particularly true for computations related to various phenomena in the atmosphere, especially at local scale and microscale [3,4].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Modelled Air Pollutant Distribution around Buildings under Different Meteorological Conditions

Petrov,

Georgieva,

Hristova

2024

Atmosphere

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The distribution of air pollutants in urban areas is significantly influenced by the presence of various geometric structures, including buildings, bridges, and tunnels. In built-up environments, meteorological conditions may influence the accumulation or dispersion of air pollutants in specific zones. This study examines the impact of wind and atmospheric stability on the dispersion of air pollutants around an apartment building situated in close proximity to a busy boulevard in a residential district of Sofia, Bulgaria. A series of dispersion simulations were conducted using the Graz Lagrangian Model (GRAL v.22.09) for a range of meteorological conditions, defined as combinations of the direction and velocity of the approaching flow, and of stability conditions within the study area of 1 × 1 km, with a horizontal resolution of 2 m. The resulting spatial distribution revealed the presence of hotspots and strong gradients in the concentration field. A simulation with meteorological data was also conducted, which was aligned with a campaign to monitor vehicular traffic. The sensitivity tests indicate that GRAL is capable of reproducing high-resolution pollutant fields, accounting for building effects at relatively low computational costs. This makes the model potentially attractive for city-wide simulations as well as for air pollution exposure estimation.

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Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Modelled Air Pollutant Distribution around Buildings under Different Meteorological Conditions

Petrov,

Georgieva,

Hristova

2024

Atmosphere

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Network simulation tools for unmanned aerial vehicle communications: A survey

Jiang,

Han,

et al. 2024

Int J Communication

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SummaryUnmanned aerial vehicle (UAV) communication has been proposed as an effective solution in both military and civilian scenarios, with low cost, high efficiency, high flexibility, and on‐demand deployment. Network simulation is an economically efficient method for validating new ideas in UAV communication. While some simulation tools have been proposed for UAV communications, there is a lack of a state‐of‐the‐art review to guide newcomers in this research field. In existing surveys, the discussion of network simulators is not comprehensive and the content discussed is outdated. There has also been no discussion of open‐source UAV simulation tools. To fill these research gaps, this survey presents an updated and comprehensive survey of UAV network simulation tools, including a unique collection of open‐source tools. Research challenges and opportunities have been proposed to inspire follow‐up studies.

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An AIoT‐driven smart healthcare framework for zoonoses detection in integrated fog‐cloud computing environments

Verma,

Gupta,

Jain

et al. 2024

Softw Pract Exp

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The escalating threat of easily transmitted diseases poses a huge challenge to government institutions and health systems worldwide. Advancements in information and communication technology offer a promising approach to effectively controlling infectious diseases. This article introduces a comprehensive framework for predicting and preventing zoonotic virus infections by leveraging the capabilities of artificial intelligence and the Internet of Things. The proposed framework employs IoT‐enabled smart devices for data acquisition and applies a fog‐enabled model for user authentication at the fog layer. Further, the user classification is performed using the proposed ensemble model, with cloud computing enabling efficient information analysis and sharing. The novel aspect of the proposed system involves utilizing the temporal graph matrix method to illustrate dependencies among users infected with the zoonotic flu and provide a nuanced understanding of user interactions. The implemented system demonstrates a classification accuracy of around 91% for around 5000 instances and reliability of around 93%. The presented framework not only aids uninfected citizens in avoiding regional exposure but also empowers government agencies to address the problem more effectively. Moreover, temporal mining results also reveal the efficacy of the proposed system in dealing with zoonotic cases.

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Modern computing: Vision and challenges

Cited by 51 publications

References 255 publications

Sensitivity Analysis of Modelled Air Pollutant Distribution around Buildings under Different Meteorological Conditions

Sensitivity Analysis of Modelled Air Pollutant Distribution around Buildings under Different Meteorological Conditions

Network simulation tools for unmanned aerial vehicle communications: A survey

An AIoT‐driven smart healthcare framework for zoonoses detection in integrated fog‐cloud computing environments

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