Smart City Gnosys

Smart city article details

Title	Pyramid: Enabling Hierarchical Neural Networks With Edge Computing
ID_Doc	43787
Authors	He Q.; Dong Z.; Chen F.; Deng S.; Liang W.; Yang Y.
Year	2022
Published	WWW 2022 - Proceedings of the ACM Web Conference 2022
DOI	http://dx.doi.org/10.1145/3485447.3511990
Abstract	Machine learning (ML) is powering a rapidly-increasing number of web applications. As a crucial part of 5G, edge computing facilitates edge artificial intelligence (AI) by ML model training and inference at the network edge on edge servers. Compared with centralized cloud AI, edge AI enables low-latency ML inference which is critical to many delay-sensitive web applications, e.g., web AR/VR, web gaming and Web-of-Things applications. Existing studies of edge AI focused on resource and performance optimization in training and inference, leveraging edge computing merely as a tool to accelerate training and inference processes. However, the unique ability of edge computing to process data with context awareness, a powerful feature for building the web-of-things for smart cities, has not been properly explored. In this paper, we propose a novel framework named Pyramid that unleashes the potential of edge AI by facilitating homogeneous and heterogeneous hierarchical ML inferences. We motivate and present Pyramid with traffic prediction as an illustrative example, and evaluate it through extensive experiments conducted on two real-world datasets. The results demonstrate the superior performance of Pyramid neural networks in hierarchical traffic prediction and weather analysis. © 2022 ACM.
Author Keywords	edge AI; edge computing; machine learning; Web of Things

Similar Articles

Id	Similarity	Authors	Title	Published
5251	0.9	Hoffpauir K.; Simmons J.; Schmidt N.; Pittala R.; Briggs I.; Makani S.; Jararweh Y.	A Survey On Edge Intelligence And Lightweight Machine Learning Support For Future Applications And Services	Journal of Data and Information Quality, 15, 2 (2023)
14842	0.891	Grzesik P.; Mrozek D.	Combining Machine Learning And Edge Computing: Opportunities, Challenges, Platforms, Frameworks, And Use Cases	Electronics (Switzerland), 13, 3 (2024)
21762	0.885	Chandrasekaran S.; Athinarayanan S.; Masthan M.; Kakkar A.; Bhatnagar P.; Samad A.	Edge Computing Revolution: Unleashing Artificial Intelligence Potential In The World Of Edge Intelligence	Edge of Intelligence: Exploring the Frontiers of AI at the Edge (2025)
60277	0.875	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)
32274	0.872	Haj Hussein D.; Ibnkahla M.	Intelligent And Autonomous Edge Slicing For Iot Systems	IEEE Internet of Things Journal, 12, 12 (2025)
21773	0.868	Parveen Banu S.; Patil Y.M.; Somasundaram R.; Santhosh C.; Singh D.P.; Manikandan G.	Edge Computing-Based Short-Term Traffic Flow Forecast For The Smart City Employing 5G Internet Vehicles	Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2024 (2024)
5117	0.867	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)
21815	0.867	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)
21784	0.865	Hou X.-P.; Lan L.; Tao C.-L.; Kou X.-Y.; Cong P.-J.; Deng Q.-X.; Zhou J.-L.	Edge Intelligence And Collaborative Computing: Frontiers And Advances; [边缘智能与协同计算: 前沿与进展]	Kongzhi yu Juece/Control and Decision, 39, 7 (2024)
21763	0.865	Alnoman A.	Edge Computing Services For Smart Cities: A Review And Case Study	2021 International Symposium on Networks, Computers and Communications, ISNCC 2021 (2021)