Smart City Gnosys

Smart city article details

Title	Automatic Distributed Deep Learning Using Resource-Constrained Edge Devices
ID_Doc	11308
Authors	Gutierrez-Torre A.; Bahadori K.; Baig S.-U.-R.; Iqbal W.; Vardanega T.; Berral J.L.; Carrera D.
Year	2022
Published	IEEE Internet of Things Journal, 9, 16
DOI	http://dx.doi.org/10.1109/JIOT.2021.3098973
Abstract	Processing data generated at high volume and speed from the Internet of Things, smart cities, domotic, intelligent surveillance, and e-healthcare systems require efficient data processing and analytics services at the Edge to reduce the latency and response time of the applications. The fog computing edge infrastructure consists of devices with limited computing, memory, and bandwidth resources, which challenge the construction of predictive analytics solutions that require resource-intensive tasks for training machine learning models. In this work, we focus on the development of predictive analytics for urban traffic. Our solution is based on deep learning techniques localized in the Edge, where computing devices have very limited computational resources. We present an innovative method for efficiently training the gated recurrent-units (GRUs) across available resource-constrained CPU and GPU Edge devices. Our solution employs distributed GRU model learning and dynamically stops the training process to utilize the low-power and resource-constrained Edge devices while ensuring good estimation accuracy effectively. The proposed solution was extensively evaluated using low-powered ARM-based devices, including Raspberry Pi v3 and the low-powered GPU-enabled device NVIDIA Jetson Nano, and also compared them with Single-CPU Intel Xeon machines. For the evaluation experiments, we used real-world Floating Car Data. The experiments show that the proposed solution delivers excellent prediction accuracy and computational performance on the Edge when compared to the baseline methods. © 2014 IEEE.
Author Keywords	Analytics; big data; cloud computing; edge computing; fog computing; Internet of Things (IoT); resource management

Similar Articles

Id	Similarity	Authors	Title	Published
13852	0.897	Prashanthi S.K.; Kesanapalli S.A.; Simmhan Y.	Characterizing The Performance Of Accelerated Jetson Edge Devices For Training Deep Learning Models	Proceedings of the ACM on Measurement and Analysis of Computing Systems, 6, 3 (2022)
13850	0.886	Prashanthi S.K.; Kesanapalli S.A.; Simmhan Y.	Characterizing The Performance Of Accelerated Jetson Edge Devices For Training Deep Learning Models	SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (2023)
13851	0.886	Prashanthi S.K.; Kesanapalli S.A.; Simmhan Y.	Characterizing The Performance Of Accelerated Jetson Edge Devices For Training Deep Learning Models	Performance Evaluation Review, 51, 1 (2023)
6307	0.877	Zhou L.; Samavatian M.H.; Bacha A.; Majumdar S.; Teodorescu R.	Adaptive Parallel Execution Of Deep Neural Networks On Heterogeneous Edge Devices	Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, SEC 2019 (2019)
42888	0.876	Perez S.; Perez J.; Arroba P.; Blanco R.; Ayala J.L.; Moya J.M.	Predictive Gpu-Based Adas Management In Energy-Conscious Smart Cities	5th IEEE International Smart Cities Conference, ISC2 2019 (2019)
21859	0.864	Xue F.; Fang W.; Xu W.; Wang Q.; Ma X.; Ding Y.	Edgeld: Locally Distributed Deep Learning Inference On Edge Device Clusters	Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020 (2020)
5117	0.859	Bellavista P.; Chatzimisios P.; Foschini L.; Paradisioti M.; Scotece D.	A Support Infrastructure For Machine Learning At The Edge In Smart City Surveillance	Proceedings - IEEE Symposium on Computers and Communications, 2019-June (2019)
43787	0.858	He Q.; Dong Z.; Chen F.; Deng S.; Liang W.; Yang Y.	Pyramid: Enabling Hierarchical Neural Networks With Edge Computing	WWW 2022 - Proceedings of the ACM Web Conference 2022 (2022)
28224	0.857	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)
20553	0.855	Li Q.; Huang L.; Tong Z.; Du T.-T.; Zhang J.; Wang S.-C.	Dissec: A Distributed Deep Neural Network Inference Scheduling Strategy For Edge Clusters	Neurocomputing, 500 (2022)