WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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Author: search.filters.author.Ozbek, BernaSubject: search.filters.subject.Mobile Edge Computing (MEC)

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  • Article
    HAPS Assisted Cooperative Offloading for Space–Air–Ground Integrated Networks
    (Elsevier GmbH, 2025) Yilmaz, Saadet Simay; Ozbek, Berna; Erdogan, Eylem
    Mobile edge computing (MEC) has significantly enhanced computational capabilities at the network edge, enabling computation-intensive applications. However, traditional MEC implementations face significant challenges in areas without reliable terrestrial network infrastructure, such as rural regions or disaster-affected zones. To address this, we present a novel MEC-enabled space-air-ground integrated network (SAGIN) framework that combines high-altitude platform station (HAPS) and low Earth orbit (LEO) satellite to ensure comprehensive coverage and reduce execution delays for ground users (GUs) in areas lacking terrestrial infrastructure. By leveraging the complementary capabilities of HAPS, which provide wide-area coverage and reliable connectivity, and LEO satellites, which offer high-throughput communication, the proposed SAGIN framework enhances computation offloading. We propose a cooperative approach between GUs and the LEO satellite via the HAPS to maximize offloaded data while satisfying stringent delay constraints under a partial offloading mode. A nonlinear optimization problem is formulated to minimize execution delay while increasing offloaded data by jointly optimizing task offloading decisions and resource allocation between the HAPS and LEO satellite. Simulation results show that the proposed cooperative offloading scheme significantly outperforms random and non-cooperative schemes considering execution delay. These results highlight that the proposed cooperative, HAPS-assisted SAGIN framework effectively enables low-delay edge computing in infrastructure-limited regions.
  • Conference Object
    User Selection for Secure Massive Mimo Based Mobile Edge Computing With Delay-Sensitive Applications
    (IEEE, 2025) Yilmaz, Saadet Simay; Ozbek, Berna
    Mobile edge computing (MEC) has been a promising technology that leverages cloud computing capabilities at the network edge to address compute-intensive and delay-sensitive applications of mobile users with limited resources. Employing massive multiple-input multiple-output (mMIMO) and nonorthogonal multiple access (NOMA) in the MEC system facilitates simultaneous task offloading for multiple users, resulting in increased spectral efficiency and decreased offloading delay. Despite the great potential of the mMIMO-NOMA-based MEC system, offloading computation tasks to MEC servers can introduce inherent security concerns and vulnerabilities. We address a notable gap in the existing literature by investigating the effect of user selection to minimize the delay in MEC while enhancing the security of this framework. Specifically, this paper presents a user selection strategy for an uplink mMIMO-NOMA-based secure MEC system in the presence of a malicious eavesdropper (Eve) to minimize offloading and computing delays, subject to the transmit power, computing resource, and secrecy rate constraints with remote computing. We propose a two-step secure user selection algorithm and solve the optimization problem with the active-set algorithm. The simulation results demonstrate the effectiveness of the proposed user selection strategy on secure MEC with a malicious Eve by minimizing the task execution delay compared to the benchmark schemes.