Smart City Gnosys

Smart city article details

Title	Urbanedge: Deep Learning Empowered Edge Computing For Urban Iot Time Series Prediction
ID_Doc	60277
Authors	Fan X.; Xiang C.; Gong L.; He X.; Chen C.; Huang X.
Year	2019
Published	ACM International Conference Proceeding Series
DOI	http://dx.doi.org/10.1145/3321408.3323089
Abstract	The revolution of smart city has led to rapid development and proliferation of Internet of Things (IoT) technologies, with the focus on transmitting raw sensory data into valuable knowledge. Meanwhile, the ubiquitous deployments of IoT are raising the importance of processing data in real-time at the edge of networks rather than in remote cloud data centers. Based on above, edge computing has been proposed to exploit the capabilities of edge devices in providing in-proximity computing services for various IoT applications. In this paper, we present UrbanEdge, a conceptual edge computing architecture empowered by deep learning for urban IoT time series prediction. We design a hierarchical architecture to process correlated IoT time series and illustrate the work-flow of UrbanEdge in data collection, data transmission and data processing. As a core component of UrbanEdge, a deep learning model is developed with attention-based recurrent neural networks. Composed with multiple processing layers, the deep learning model can extract feature representations from raw IoT data for monitoring and prediction. We evaluate the designed deep learning model of UrbanEdge on real-world datasets, evaluation results show that the UrbanEdge outperforms other baseline methods in time series prediction. © 2019 Association for Computing Machinery.
Author Keywords	Deep learning; Edge computing; Internet of Things; Time series prediction

Similar Articles

Id	Similarity	Authors	Title	Published
22927	0.894	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)
23181	0.891	Lee S.H.; Lee T.; Kim S.; Park S.	Energy Consumption Prediction System Based On Deep Learning With Edge Computing	2019 2nd International Conference on Electronics Technology, ICET 2019 (2019)
8433	0.888	Udayakumar R.; Mahesh B.; Sathiyakala R.; Thandapani K.; Choubey A.; Khurramov A.; Alzubaidi L.H.; Sravanthi J.	An Integrated Deep Learning And Edge Computing Framework For Intelligent Energy Management In Iot-Based Smart Cities	International Conference for Technological Engineering and its Applications in Sustainable Development, ICTEASD 2023 (2023)
57735	0.886	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)
38702	0.885	Papastefanopoulos V.; Linardatos P.; Panagiotakopoulos T.; Kotsiantis S.	Multivariate Time-Series Forecasting: A Review Of Deep Learning Methods In Internet Of Things Applications To Smart Cities	Smart Cities, 6, 5 (2023)
12088	0.883	Balicki, J; Balicka, H; Dryja, P	Big Data From Sensor Network Via Internet Of Things To Edge Deep Learning For Smart City	COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT, CISIM 2021, 12883 (2021)
14842	0.882	Grzesik P.; Mrozek D.	Combining Machine Learning And Edge Computing: Opportunities, Challenges, Platforms, Frameworks, And Use Cases	Electronics (Switzerland), 13, 3 (2024)
32139	0.881	Elhanashi A.; Dini P.; Saponara S.; Zheng Q.	Integration Of Deep Learning Into The Iot: A Survey Of Techniques And Challenges For Real-World Applications	Electronics (Switzerland), 12, 24 (2023)
28224	0.878	Khalfi M.F.; Tabbiche M.N.	Gpthingsim: A Iot Simulator Based Gpt Models Over An Edge-Cloud Environments	International Journal of Networked and Distributed Computing, 13, 1 (2025)
35057	0.876	Atitallah S.B.; Driss M.; Boulila W.; Ghezala H.B.	Leveraging Deep Learning And Iot Big Data Analytics To Support The Smart Cities Development: Review And Future Directions	Computer Science Review, 38 (2020)