Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://gcris.yasar.edu.tr/handle/123456789/11290

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Dynamic Automatic Forecaster Selection via Artificial Neural Network Based Emulation to Enable Massive Access for the Internet of Things
(ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 2022-05) Mert Nakip; Erdem Cakan; Volkan Rodoplu; Cuneyt Guzelis
The Massive Access Problem of the Internet of Things (IoT) occurs at the uplink Medium Access Control (MAC) layer when a massive number of IoT devices seek to transfer their data to an IoT gateway. Although recently proposed predictive access solutions that schedule the uplink traffic based on forecasts of IoT device traffic achieve high network performance these solutions depend heavily on the performance of forecasters. Hence the design and selection of forecasting schemes are key to enabling massive access for such predictive access solutions. To this end in this paper first we develop a framework that emulates the relationship between the IoT device class composition in the coverage area of an IoT gateway and the resulting network performance by virtue of an Artificial Neural Network (ANN). Second based on this framework we develop the Dynamic Automatic Forecaster Selection (DAFS) method which selects the best-performing forecasting scheme for predictive access in particular for Joint Forecasting-Scheduling (JFS) in a manner that adapts dynamically to a changing number of IoT devices in each device class in the coverage area. We evaluate the performance of DAFS via simulations and show that our method is able to achieve at least 80% of the best performance that can be attained for both throughput and energy consumption. Furthermore we demonstrate that DAFS is robust with respect to the selection of architectural parameters and has a reasonable computation time for real-time IoT applications. These results imply that DAFS holds the potential for practical implementation at IoT gateways in order to enable massive access under a dynamically changing composition of IoT devices.
Citation - WoS: 75
Citation - Scopus: 78
The impact of geopolitical risk- institutional governance and green finance on attaining net-zero carbon emission
(ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 2024-05) Ahmed Imran Hunjra; Muhammad Azam; Peter Verhoeven; Dilvin Taskin; Jiapeng Dai; Verhoeven, Peter; Taskin, Dilvin; Hunjra, Ahmed Imran; Azam, Muhammad; Dai, Jiapeng
This research investigates the impact of geopolitical risk institutional governance and green finance on environmental outcomes specifically focusing on carbon emissions and ecological footprint. Utilizing the dynamic CS-ARDL method and aggregated mean group analysis on a panel dataset covering 21 nations from 2000 to 2021 our findings reveal that heightened geopolitical risk leads to both short and long run increases in carbon emissions and the ecological footprint. Our study finds both a direct as well as indirect connection between governance green finance and environmental outcomes in both the short and long run highlighting the nuanced impact of governance on the formulation of environmental policies and regulatory frameworks. The results emphasize the need for targeted strategies including focused investments and incentives for sustainable finance particularly in conflict-affected regions. Furthermore our research underscores the enduring impact of historical events such as wars on contemporary environmental indicators emphasizing the importance of proactive conflict prevention measures. Our research suggests that policymakers should adopt comprehensive strategies that prioritize emission reduction during short-run spikes in geopolitical risk while maintaining a steadfast commitment to long-run sustainability.

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