Faculty of Computer Science and Engineering

Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5

The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest faculties in that field in the region. The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.

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    Item type:Publication,
    Comparing the Use of Simu5G and 5G‐Sim‐V2I/N Modules When Analysing the Edge Computing Resource Management Efficiency
    (Wiley, 2025-04-29)
    Bernad, Cristina
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    Gilly, Katja
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    Thomas, Nigel
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    Roig, Pedro Juan
    Performance analysis of smart edge computing orchestration algorithms should be done using a realistic urban simulation environment wherein mobile users are accessing their edge services using a readily available 5G network. In this paper, we investigate the influence of using two different 5G simulation frameworks, which are provided as readily available possibilities to model the access network used to deliver edge computing services. The results show that although both frameworks aim to implement the 5G specifications and are deemed suitable choices for simulating a 5G smart city vehicular environment, there can be significant differences in the obtained macro results. The analysis of the simulation results from two identical studies where the only change is the choice of a 5G simulation framework shows that the obtained average end-to-end edge service latency as perceived by edge users can differ up to more than 21 times. The choice of 5G simulation framework is also reflected in the overall generated workload for the edge computing orchestration leading to over 25% more migrations when using 5G-Sim-V2I/N compared to Simu5G.
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    Item type:Publication,
    Performance comparison of LSTM-based handover prediction models for 6G MEC services
    (Elsevier BV, 2026-06)
    Bernad, Cristina
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    Gilly, Katja
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