Mehmood, H., Khalid, A., Kostakos, P., Gilman, E., & Pirttikangas, S. (2024). A novel Edge architecture and solution for detecting concept drift in smart environments. Future Generation Computer Systems, 150, 127–143. https://doi.org/10.1016/j.future.2023.08.023
A novel edge architecture and solution for detecting concept drift in smart environments
|Author:||Mehmood, Hassan1; Khalid, Ahmed2; Kostakos, Panos1;|
1University of Oulu, Pentti Kaiteran katu 1, 90570 Oulu, Finland
2DELL Technologies, Cork, Ireland
|Online Access:||PDF Full Text (PDF, 2.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20231011139765
|Publish Date:|| 2023-10-11
The proliferation of the Internet of Things (IoT), artificial intelligence (AI), the adoption of 5G, and progress towards 6G technology have led to the accumulation of massive amounts of real-world data; however, a significant portion of the data generated by smart cities and smart buildings remains unused. A notable problem is the shift of statistical properties in real-world streaming over time caused by unexpected factors, referred to as concept drift, which results in less efficient predictive models. To address this problem, the latest research leverages the cloud–edge continuum paradigm for the deployment of AI and general smart city applications while utilising the available resources optimally. In this article, we propose a computing architecture for different smart city applications in edge micro data centre (EMDC) settings over a hybrid cloud–edge continuum to support the deployment of AI workloads. We implement a feedback-driven automated concept drift detection and adaptation methodology, combining base learner long short-term memory (LSTM) with Page–Hinkley test (PHT), adaptive windowing (ADWIN) and the Kolmogorov–Smirnov windowing (KSWIN). Real-world data streams are utilised to forecast from various environmental sensors installed at the University of Oulu Smart Campus. The feedback-based concept drift detection and adaption process is first evaluated using synthetic datasets with known concept drift points and then employed in the real-world data. Subsequently, the implementation is evaluated using the state-of-the-art MAE, RMSE, and MAPE methods. The results showed a reduction in MAPE from 8.5% to 3.88% when concept drift detection was applied. Additionally, the challenges faced and the effectiveness of the suggested solutions are explored.
Future generation computer systems
|Pages:||127 - 143|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
119 Other natural sciences
This study was funded by the Academy of Finland, Finland (grants 337614, 323630, 318927), European Commission grants IDUNN (grant no. 101021911), ECSEL Joint Undertaking under grant agreement No 876967, and KDT JU under grant agreement No. 101097560 (CLEVER project).
|EU Grant Number:||
(101021911) IDUNN - A Cognitive Detection System for Cybersecure Operational Technologies
|Academy of Finland Grant Number:||
337614 (Academy of Finland Funding decision)
323630 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).