Smart City Gnosys
Smart city article details
| Title | A Novel Edge Architecture And Solution For Detecting Concept Drift In Smart Environments |
|---|---|
| ID_Doc | 3327 |
| Authors | Mehmood H.; Khalid A.; Kostakos P.; Gilman E.; Pirttikangas S. |
| Year | 2024 |
| Published | Future Generation Computer Systems, 150 |
| DOI | http://dx.doi.org/10.1016/j.future.2023.08.023 |
| Abstract | 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. © 2023 The Author(s) |
| Author Keywords | Big data; Cloud–edge continuum; Concept drift; Data analysis; Data centres; Decision-making; Edge computing; Machine learning; Smart buildings; Smart cities |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
15473  | 0.9 | Etehadi S.S.; Yaghmaee M.H.; Noorani N.; Hosseinpour M.; Seno S.A.H. | Concept Drift Challenges In The Internet Of Things (Iot) Era Of Smart Cities: A Preliminary Investigation | 7th International Conference on Internet of Things and Applications, IoT 2023 (2023) |
| 55982 | 0.875 | Hasan M.M.; Sultana T.; Hossain M.D.; Mandal A.K.; Ngo T.-T.; Lee G.-W.; Huh E.-N. | The Journey To Cloud As A Continuum: Opportunities, Challenges, And Research Directions | ICT Express (2025) |
| 6747 | 0.866 | Assis D.N.; Souza V.M.A. | Adwin-U: Adaptive Windowing For Unsupervised Drift Detection On Data Streams | Knowledge and Information Systems (2025) |
| 6225 | 0.864 | Wu F.-J.; Zhao Y.; Chen L.-J. | Adaptive Creation And Migration Of Time-Series City Profiles Based On Edge Computing | IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2021-September (2021) |
| 22927 | 0.863 | Nizam M.K.; Goyal S.B.; Verma C.; Illés Z. | Empowering Smart Cities With Edge Computing-Based Iot Systems: A Focus On Data Analytics And Machine Learning Techniques | Lecture Notes in Electrical Engineering, 1194 (2024) |
| 21828 | 0.86 | Belcastro L.; Marozzo F.; Orsino A.; Talia D.; Trunfio P. | Edge-Cloud Continuum Solutions For Urban Mobility Prediction And Planning | IEEE Access, 11 (2023) |
| 57735 | 0.86 | Zaid M.A.; Faizal M.; Maheswar R.; Abdullaziz O.I. | Toward Smart Urban Development Through Intelligent Edge Analytics | EAI/Springer Innovations in Communication and Computing (2020) |
| 60277 | 0.86 | Fan X.; Xiang C.; Gong L.; He X.; Chen C.; Huang X. | Urbanedge: Deep Learning Empowered Edge Computing For Urban Iot Time Series Prediction | ACM International Conference Proceeding Series (2019) |
| 21815 | 0.859 | Murthy V.S.N.; Kumari R.; Goyal M.; Dubey P.; Meenakshi; Manikandan S.; Ramesh P. | Edge-Ai In Iot: Leveraging Cloud Computing And Big Data For Intelligent Decision-Making | Journal of Information Systems Engineering and Management, 10 (2025) |
| 34793 | 0.858 | Sfaxi H.; Lahyani I.; Yangui S.; Torjmen M. | Latency-Aware And Proactive Service Placement For Edge Computing | IEEE Transactions on Network and Service Management, 21, 4 (2024) |