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Start date: 2020End date: 2026Type: search.filters.itemtype.Proceedings PaperSubject: search.filters.subject.Computer Science, Theory & MethodsSubject: search.filters.subject.Telecommunications

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Now showing 1 - 7 of 7
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    SCORING: Towards Smart Collaborative cOmputing, caching and netwoRking paradIgm for Next Generation communication infrastructures
    (IEEE, 2022) Hmitti, Zakaria Ait; Ben Ammar, Hamza; Soyak, Ece Gelal; Kardjadja, Youcef; Malektaji, Sepideh; Ali, Soukaina Ouledsidi; Rayani, Marsa; Saqib, Muhammad; Taghizadeh, Seyedreza; Ajib, Wessam; Elbiaze, Halima; Ercetin, Ozgur; Ghamri-Doudane, Yacine; Glitho, Roch; University of Quebec; University of Quebec Montreal; Sabanci University; Concordia University - Canada; Bahcesehir University
    The unprecedented increase of heterogeneous devices connected to the Internet, along with tight requirements of future networks, including 5G and beyond, poses new design challenges to network infrastructures. Collaborative computing, caching and communication paradigm together with artificial intelligence have the potential to enable the Next-Generation Networking Infrastructure (NGNI) that is needed to fulfill the stringent requirements of emerging applications. In this paper, we propose the SCORING project vision for reshaping the current network infrastructure towards an NGNI acting as a truly distributed, collaborative, and pervasive system that enables the execution of application-specific tasks and the storage of the related data contents in the Cloud-Edge-Mist continuum with high QoS/QoE guarantees.
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    Throughput-maximizing OFDMA Scheduler for IEEE 802.11ax Networks
    (IEEE, 2020) Kuran, Mehmet Sukru; Dilmac, A.; Topal, Omer; Yamansavascilar, Baris; Avallone, Stefano; Tugcu, Tuna; Bahcesehir University; Bogazici University; University of Naples Federico II
    In this paper, we develop a novel throughput-maximizing OFDMA scheduler for the multi-user MAC framework for the IEEE 802.11ax networks. The scheduler works both in the downlink and uplink directions and assigns resource units to stations using a linear programming technique considering load of each client, possible resource unit configurations, modulation-coding scheme of each client, and ageing factor of each client's load. The performance of the proposed scheduler has been evaluated using the NS3 simulator and compared against the legacy MAC layer mechanism of IEEE 802.11 protocol (i.e., DCF/EDCA). Simulation results show that our proposed throughput-maximizing scheduler increases the total throughput in the network as well as decrease the average end-to-end delay regardless of the number of stations connected to the access point by prioritizing the traffic of clients connected via high modulation-coding schemes.
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    Multivariate and Multi-step Traffic Prediction for NextG Networks with SLA Violation Constraints
    (IEEE, 2023) Tuna, Evren; Soysal, Alkan; Bahcesehir University; Virginia Polytechnic Institute & State University
    This paper focuses on predicting downlink (DL) traffic volume in mobile networks while minimizing overprovisioning and meeting a given service-level agreement (SLA) violation rate. We present a multivariate, multi-step, and SLA-driven approach that incorporates 20 different radio access network (RAN) features, a custom feature set based on peak traffic hours, and handover-based clustering to leverage the spatiotemporal effects. In addition, we propose a custom loss function that ensures the SLA violation rate constraint is satisfied while minimizing overprovisioning. We also perform multi-step prediction up to 24 steps ahead and evaluate performance under both single-step and multi-step prediction conditions. Our study makes several contributions, including the analysis of RAN features, the custom feature set design, a custom loss function, and a parametric method to satisfy SLA constraints.
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    Neural Architecture Search Using Differential Evolution in MAML Framework for Few-Shot Classification Problems
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2023) Gulcu, Ayla; Kus, Zeki; DiGaspero, L; Festa, P; Nakib, A; Pavone, M; Bahcesehir University
    Model-Agnostic Meta-Learning (MAML) algorithm is an optimization based meta-learning algorithm which aims to find a good initial state of the neural network that can then be adapted to any novel task using a few optimization steps. In this study, we take MAML with a simple four-block convolution architecture as our baseline, and try to improve its few-shot classification performance by using an architecture generated automatically through the neural architecture search process. We use differential evolution algorithm as the search strategy for searching over cells within a predefined search space. We have performed our experiments using two well-known few-shot classification datasets, mini-ImageNet and FC100 dataset. For each of those datasets, the performance of the original MAML is compared to the performance of our MAML-NAS model under both 1-shot 5-way and 5-shot 5-way settings. The results reveal that MAML-NAS results in better or at least comparable accuracy values for both of the datasets in all settings. More importantly, this performance is achieved by much simpler architectures, that is architectures requiring less floating-point operations.
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    QoE Estimation for the Wi-Fi Edge with Gradient Boosting-based Machine Learning
    (IEEE, 2023) Argin, Berke; Demir, Mehmet Ozgun; Onalan, Aysun Gurur; Salik, Elif Dilek; Soyak, Ece Gelel; Bahcesehir University
    An integral part of the Intent-Based Networking paradigm is estimating and improving the end-user quality of experience (QoE). Estimating user experience from the (wide-area) network data alone does not accurately represent the performance at customer premises since Wi-Fi at the edge also significantly affects the perceived QoE. We propose machine learning-based estimation of the end-users' perceived QoE for web browsing and video streaming applications, based on Wi-Fi statistics. We implement support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), XGBoost, and CatBoost algorithms and compare their performance. To the best of our knowledge, our CatBoost-based model yields the highest accuracy to date, 0.92 R-2, in estimating the QoE for web browsing based on Wi-Fi statistics. Our experiments also show that the XGBoost-based QoE estimator outperformed the neural network-based model in estimating the QoE for video streaming. Our work demonstrates that network operators can infer the user-perceived QoE in a Wi-Fi network through telemetry data obtained by passive measurements.
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    Analysis of Transfer Learning Models for Face Mask Detection
    (IEEE, 2022) Kasap, Ozge Yucel; Cakir, Duygu; Bahcesehir University
    The covid-19 outbreak caused a global health crisis, and it still continues to spread rapidly today. Considering that it is very difficult to manually control the use of masks in public places, it is inevitable to turn the process into an automatic detection system. In this study, various Transfer Learning networks in detecting the usage of face masks are analyzed using a large dataset. As a consequence, it can be determined whether the face images recorded in-the-wild and low resolution from various angles are masked / unmasked and whether they are wearing the mask correctly or not. The architecture depths and execution times are also examined to see whether they have a positive effect on the accuracy or not. The results indicate that ResNet50 has a significant success over the rest of the models and the depth of the used model does not guarantee the success nor does it determine the time it takes for training.
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    An Energy-Harvesting Aware Cluster Head Selection Policy in Underwater Acoustic Sensor Networks
    (IEEE, 2023) Eris, Cigdem; Gul, Omer Melih; Boluk, Pinar Sarisaray; Bahcesehir University; Bahcesehir University
    Underwater wireless sensor networks (UWSNs) have the potential to prevent disasters by providing access to environmental data, exploited in global warming studies, early warning systems, and industrial monitoring applications. It is of vital importance to sustain the delivery of information in these applications. Underwater wireless sensor networks are main components of these applications, providing continuous delivery of data regarding to the area of interest. However, the harsh nature of underwater environment makes battery replacement impracticable and limits sensor nodes to rely on their battery supply. Therefore, energy consumption is a major concern in underwater sensor networks. To minimize the energy consumption of the network, clustering is an extensively studied technology in underwater sensor applications. Cluster heads (CHs) are selected aiming towards data aggregation and reduction of energy consumption of the network hence increasing network lifetime. In this UWSN, we consider stochastical energy harvesting processes at each sensor node for the energy-aware routing problem in wireless sensor networks. In this paper, a novel cluster head (CH) selection technique is proposed, and CHs are chosen by considering the nodes' not only position, and residual energy but also expected harvested energy. Numerical results show that the proposed technique reduces energy consumption and extends the network lifetime considerably.