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  • 1.
    Ahmad, Sabtain
    et al.
    Vienna University of Technology, Vienna, Austria.
    Aral, Atakan
    Umeå University, Faculty of Science and Technology, Department of Computing Science. University of Vienna, Vienna, Austria.
    Hierarchical federated transfer learning: a multi-cluster approach on the computing continuum2023In: 2023 international conference on machine learning and applications (ICMLA), IEEE, 2023, p. 1163-1168Conference paper (Refereed)
    Abstract [en]

    Federated Learning (FL) involves training models over a set of geographically distributed users. We address the problem where a single global model is not enough to meet the needs of geographically distributed heterogeneous clients. This setup captures settings where different groups of users have their own objectives however, users based on geographical location or task similarity, can be grouped together and by inter-cluster knowledge they can leverage the strength in numbers and better generalization in order to perform more efficient FL. We introduce a Hierarchical Multi-Cluster Computing Continuum for Federated Learning Personalization (HC3FL) to cluster similar clients and train one edge model per cluster. HC3FL incorporates federated transfer learning to enhance the performance of edge models by leveraging a global model that captures collective knowledge from all edge models. Furthermore, we introduce dynamic clustering based on task similarity to handle client drift and to dynamically recluster mobile (non-stationary) clients. We evaluate the HC3FL approach through extensive experiments on real-world datasets. The results demonstrate that our approach effectively improves the performance of edge models compared to traditional FL approaches.

  • 2.
    Ahmad, Sabtain
    et al.
    Institute of Information Systems Engineering, Vienna University of Technology, Vienna, Austria.
    Uyanık, Halit
    Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey.
    Ovatman, Tolga
    Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey.
    Sandıkkaya, Mehmet Tahir
    Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey.
    De Maio, Vincenzo
    Institute of Information Systems Engineering, Vienna University of Technology, Vienna, Austria.
    Brandić, Ivona
    Institute of Information Systems Engineering, Vienna University of Technology, Vienna, Austria.
    Aral, Atakan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Sustainable environmental monitoring via energy and information efficient multi-node placement2023In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 10, no 24, p. 22065-22079Article in journal (Refereed)
    Abstract [en]

    The Internet of Things is gaining traction for sensing and monitoring outdoor environments such as water bodies, forests, or agricultural lands. Sustainable deployment of sensors for environmental sampling is a challenging task because of the spatial and temporal variation of the environmental attributes to be monitored, the lack of the infrastructure to power the sensors for uninterrupted monitoring, and the large continuous target environment despite the sparse and limited sampling locations. In this paper, we present an environment monitoring framework that deploys a network of sensors and gateways connected through low-power, long-range networking to perform reliable data collection. The three objectives correspond to the optimization of information quality, communication capacity, and sustainability. Therefore, the proposed environment monitoring framework consists of three main components: (i) to maximize the information collected, we propose an optimal sensor placement method based on QR decomposition that deploys sensors at information- and communication-critical locations; (ii) to facilitate the transfer of big streaming data and alleviate the network bottleneck caused by low bandwidth, we develop a gateway configuration method with the aim to reduce the deployment and communication costs; and (iii) to allow sustainable environmental monitoring, an energy-aware optimization component is introduced. We validate our method by presenting a case study for monitoring the water quality of the Ergene River in Turkey. Detailed experiments subject to real-world data show that the proposed method is both accurate and efficient in monitoring a large environment and catching up with dynamic changes.

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  • 3.
    Kimovski, Dragi
    et al.
    University of Klagenfurt, Austria.
    Saurabh, Nishant
    Utrecht University, The Netherlands.
    Jansen, Matthijs
    Vrije Universiteit Amsterdam, The Netherlands.
    Aral, Atakan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Al-Dulaimy, Auday
    Mälardalen University and Dalarna University, Sweden.
    Bondi, Andre B.
    Software Performance and Scalability Consulting LLC, USA.
    Galletta, Antonino
    University of Messina, Italy.
    Papadopoulos, Alessandro V.
    Mälardalen University, Sweden.
    Iosup, Alexandru
    Vrije Universiteit Amsterdam, The Netherlands.
    Prodan, Radu
    University of Klagenfurt, Austria.
    Beyond von neumann in the computing continuum: architectures, applications, and future directions2023In: IEEE Internet Computing, ISSN 1089-7801, E-ISSN 1941-0131, p. 1-11Article in journal (Refereed)
    Abstract [en]

    The article discusses the emerging non-von Neumann computer architectures and their integration in the computing continuum for supporting modern distributed applications, including artificial intelligence, big data, and scientific computing. It provides a detailed summary of the available and emerging non-von Neumann architectures, which range from power-efficient single-board accelerators to quantum and neuromorphic computers. Furthermore, it explores their potential benefits for revolutionizing data processing and analysis in various societal, science, and industry fields. The paper provides a detailed analysis of the most widely used class of distributed applications and discusses the difficulties in their execution over the computing continuum, including communication, interoperability, orchestration, and sustainability issues.

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    fulltext
  • 4.
    Luger, Daniel
    et al.
    University of Vienna, Vienna, Austria.
    Aral, Atakan
    Umeå University, Faculty of Science and Technology, Department of Computing Science. University of Vienna, Vienna, Austria.
    Brandic, Ivona
    Vienna University of Technology, Vienna, Austria.
    Cost-aware neural network splitting and dynamic rescheduling for edge intelligence2023In: EdgeSys '23: Proceedings of the 6th International Workshop on Edge Systems, Analytics and Networking, ACM Digital Library, 2023, p. 42-47Conference paper (Refereed)
    Abstract [en]

    With the rise of IoT devices and the necessity of intelligent applications, inference tasks are often offloaded to the cloud due to the computation limitation of the end devices. Yet, requests to the cloud are costly in terms of latency, and therefore a shift of the computation from the cloud to the network's edge is unavoidable. This shift is called edge intelligence and promises lower latency, among other advantages. However, some algorithms, like deep neural networks, are computationally intensive, even for local edge servers (ES). To keep latency low, such DNNs can be split into two parts and distributed between the ES and the cloud. We present a dynamic scheduling algorithm that takes real-Time parameters like the clock speed of the ES, bandwidth, and latency into account and predicts the optimal splitting point regarding latency. Furthermore, we estimate the overall costs for the ES and cloud during run-Time and integrate them into our prediction and decision models. We present a cost-Aware prediction of the splitting point, which can be tuned with a parameter toward faster response or lower costs.

    Download full text (pdf)
    fulltext
  • 5.
    Sari, T. Tolga
    et al.
    Istanbul Technical University, Department of Computer Engineering, Istanbul, Turkey.
    Ahmad, Sabtain
    Institute of Information Systems Engineering, Vienna University of Technology, Vienna, Austria.
    Aral, Atakan
    Umeå University, Faculty of Science and Technology, Department of Computing Science. University of Vienna, Faculty of Computer Science, Vienna, Austria.
    Seçinti, Gökhan
    Istanbul Technical University, Department of Computer Engineering, Istanbul, Turkey.
    Collaborative smart environmental monitoring using flying edge intelligence2023In: Proceedings - IEEE global communications conference, GLOBECOM, Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 5336-5341Conference paper (Refereed)
    Abstract [en]

    Smart environmental monitoring is crucial for public health and ecological balance as it enables us to monitor and react to environmental hazards. However, effective environmental monitoring can be hindered by the lack of infrastructure and high monetary costs. These challenges are even more pronounced in remote areas, where networking and energy sources are often limited or nonexistent. To address these challenges, we utilize UAVs to form a FANET which can provide effective communication infrastructure suitable for environment monitoring. Moreover, we utilize Edge Intelligence at these UAVs to increase the processing speed and reduce the data size that needs to be transmitted. Our results show that, compared to statically placed gateways, our solution is able to attain similar average age of information for monitoring results while also significantly increasing system capacity.

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