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Aral, Atakan
Publications (4 of 4) Show all publications
Kimovski, D., Saurabh, N., Jansen, M., Aral, A., Al-Dulaimy, A., Bondi, A. B., . . . Prodan, R. (2023). Beyond von neumann in the computing continuum: architectures, applications, and future directions. IEEE Internet Computing, 1-11
Open this publication in new window or tab >>Beyond von neumann in the computing continuum: architectures, applications, and future directions
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2023 (English)In: IEEE Internet Computing, ISSN 1089-7801, E-ISSN 1941-0131, p. 1-11Article in journal (Refereed) Published
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.

Keywords
Artificial intelligence, Computational modeling, Computer architecture, Distributed databases, Internet, Neurons, Quantum computing
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-212820 (URN)10.1109/MIC.2023.3301010 (DOI)2-s2.0-85166765027 (Scopus ID)
Available from: 2023-08-16 Created: 2023-08-16 Last updated: 2023-09-28Bibliographically approved
Sari, T. T., Ahmad, S., Aral, A. & Seçinti, G. (2023). Collaborative smart environmental monitoring using flying edge intelligence. In: Proceedings - IEEE global communications conference, GLOBECOM: . Paper presented at 2023 IEEE Global Communications Conference, GLOBECOM 2023, Kuala Lumpur, Malaysia, 4-8 December 2023. (pp. 5336-5341). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Collaborative smart environmental monitoring using flying edge intelligence
2023 (English)In: Proceedings - IEEE global communications conference, GLOBECOM, Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 5336-5341Conference paper, Published 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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Series
IEEE Global Communications Conference, ISSN 23340983, E-ISSN 25766813
Keywords
Age of Information, Flying Ad-Hoc Networks, Flying Edge Intelligence, Smart Environmental Monitoring, Value of Information
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-222573 (URN)10.1109/GLOBECOM54140.2023.10436927 (DOI)2-s2.0-85187318759 (Scopus ID)9798350310900 (ISBN)
Conference
2023 IEEE Global Communications Conference, GLOBECOM 2023, Kuala Lumpur, Malaysia, 4-8 December 2023.
Available from: 2024-04-08 Created: 2024-04-08 Last updated: 2024-04-08Bibliographically approved
Luger, D., Aral, A. & Brandic, I. (2023). Cost-aware neural network splitting and dynamic rescheduling for edge intelligence. In: EdgeSys '23: Proceedings of the 6th International Workshop on Edge Systems, Analytics and Networking. Paper presented at 6th International Workshop on Edge Systems, Analytics and Networking, EdgeSys 2023, in conjunction with ACM EuroSys 2023, Rome, Italy, May 8, 2023 (pp. 42-47). ACM Digital Library
Open this publication in new window or tab >>Cost-aware neural network splitting and dynamic rescheduling for edge intelligence
2023 (English)In: EdgeSys '23: Proceedings of the 6th International Workshop on Edge Systems, Analytics and Networking, ACM Digital Library, 2023, p. 42-47Conference paper, Published 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.

Place, publisher, year, edition, pages
ACM Digital Library, 2023
Keywords
cost-Awareness, DNN splitting, edge computing, edge intelligence
National Category
Computer Sciences Computer Systems
Identifiers
urn:nbn:se:umu:diva-209279 (URN)10.1145/3578354.3592871 (DOI)2-s2.0-85159359631 (Scopus ID)9798400700828 (ISBN)
Conference
6th International Workshop on Edge Systems, Analytics and Networking, EdgeSys 2023, in conjunction with ACM EuroSys 2023, Rome, Italy, May 8, 2023
Available from: 2023-06-08 Created: 2023-06-08 Last updated: 2023-06-08Bibliographically approved
Ahmad, S., Uyanık, H., Ovatman, T., Sandıkkaya, M. T., De Maio, V., Brandić, I. & Aral, A. (2023). Sustainable environmental monitoring via energy and information efficient multi-node placement. IEEE Internet of Things Journal, 10(24), 22065-22079
Open this publication in new window or tab >>Sustainable environmental monitoring via energy and information efficient multi-node placement
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2023 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 10, no 24, p. 22065-22079Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Environmental monitoring, sensor placement, gateway configuration, wireless sensor networks, LoRaWAN, energy efficiency, multi-objective optimization, QR decomposition
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-213028 (URN)10.1109/jiot.2023.3303124 (DOI)2-s2.0-85167805834 (Scopus ID)
Available from: 2023-08-19 Created: 2023-08-19 Last updated: 2024-01-09Bibliographically approved
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