Kolomvatsos, K., Anagnostopoulos, C. , Koziri, M. and Loukopoulos, T. (2022) Proactive & time-optimized data synopsis management at the edge. IEEE Transactions on Knowledge and Data Engineering, 34(7), pp. 3478-3490. (doi: 10.1109/TKDE.2020.3021377)
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222780.pdf - Accepted Version 1MB |
Abstract
Internet of Things offers the infrastructure for smooth functioning of autonomous context-aware devices being connected towards the Cloud. Edge Computing (EC) relies between the IoT and Cloud providing significant advantages. One advantage is to perform local data processing (limited latency, bandwidth preservation) with real time communication among IoT devices, while multiple nodes become hosts of the collected data (reported by IoT devices). In this work, we provide a mechanism for the exchange of data synopses (summaries of extracted knowledge) among EC nodes that are necessary to give the knowledge on the data present in EC environments. The overarching aim is to intelligently decide on when nodes should exchange data synopses in light of efficient execution of tasks. We enhance such a decision with a stochastic optimization model based on the Theory of Optimal Stopping. We provide the fundamentals of our model and the relevant formulations on the optimal time to disseminate data synopses to network edge nodes. We report a comprehensive experimental evaluation and comparative assessment related to the optimality achieved by our model and the positive effects on EC.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Kolomvatsos, Dr Kostas and Anagnostopoulos, Dr Christos |
| Authors: | Kolomvatsos, K., Anagnostopoulos, C., Koziri, M., and Loukopoulos, T. |
| College/School: | College of Science and Engineering > School of Computing Science |
| Journal Name: | IEEE Transactions on Knowledge and Data Engineering |
| Publisher: | IEEE |
| ISSN: | 1041-4347 |
| ISSN (Online): | 1041-4347 |
| Published Online: | 02 September 2020 |
| Copyright Holders: | Copyright © 2020 IEEE |
| First Published: | First published in IEEE Transactions on Knowledge and Data Engineering 34(7): 3478-3490 |
| Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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