Distributed Analytics and Edge Intelligence: Pervasive Health Monitoring at the Era of Fog Computing

By Yu Cao, Peng Hou, Donald Brown, Jie Wang, Songqing Chen. 2015

91 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Surgery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Rongxing Lu
2. Cong Xie
3. Oluwasanmi Koyejo
4. Indranil Gupta
5. Xiaodong Lin
6. Xuemin Shen
7. Saad Khan
8. Zheng Yan

In Computer Vision and Pattern Recognition

1. Indranil Gupta
2. Oluwasanmi Koyejo
3. Saad Khan
4. Cong Xie
5. Rongxing Lu
6. Zheng Yan
7. Xuemin Shen
8. Xiaodong Lin

Citations in the corpus

• Practical Distributed Learning: Secure Machine Learning with Communication-Efficient Local Updates (2019, background): “More and more big-data applications are moving from cloud to edge, including for machine-learning tasks (Cao et al., 2015; Mahdavinejad et al., 2018; Zeydan et al., 2016).”
• Asynchronous Federated Optimization (2019, background): “At the same time, machine-learning applications are also moving from cloud to edge [3, 18, 21].”
• Securing Fog Computing for Internet of Things Applications: Challenges and Solutions (2018, methodology): “With the proposed architectures and frameworks, the applications of fog computing have been explored to support various IoT systems and services, including augmented reality [16], pervasive health monitoring [40], energy management [41] and website categories [42].”
• Fog computing security: a review of current applications and security solutions (2017, methodology): “Two fall detection systems have been implemented using Fog platform, named U-FALL [59] and FAST [60].”
• SLSGD: Secure and Efficient Distributed On-device Machine Learning (2019, background): “e and fog computing [10,14] in various scenarios such as smart home or sensor networks. More and more big-data applications are moving from the cloud to the edge, including for machine-learning tasks [5,21,30]. Although computational power is growing, edge devices are still much weaker than the work”
• From Cloud to Fog Computing: A Review and a Conceptual Live VM Migration Framework (2017, methodology): “[47] proposed a real-time fall detection algorithm, U-Fall, which consists of three major modules, front-end, back-end and communication module.”
• Fog Computing: Platform and Applications (2015, methodology): “Cao et al. [22] have explored to use fog computing in health monitoring such as real-time fall detection.”