DeepCham: Collaborative Edge-Mediated Adaptive Deep Learning for Mobile Object Recognition

By Dawei Li, Theodoros Salonidis, Nirmit V. Desai, Mooi Choo Choo Chuah. 2016

92 citations recorded by the source corpus.

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

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. Umair Mohammad
2. Sameh Sorour
3. Mohsen Guizani
4. Ala Al-Fuqaha
5. M. Mohammadi
6. Dusist Niyato
7. Bashir I. Morshed
8. Syed Kamrul Islam

In Computer Vision and Pattern Recognition

1. Umair Mohammad
2. Siwei Lyu
3. Bashir I. Morshed
4. Syed Kamrul Islam
5. Jianlin Cheng
6. Md Maruf Hossain Shuvo
7. Ser-Nam Lim
8. Longyin Wen

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “[168] proposed a system that leverages the collaboration of mobile and edge devices running CNN models for object recognition.”
• Adaptive RNN Tree for Large-Scale Human Action Recognition (2017, methodology): “Last, with the assistance of recent tracking algorithms [4, 15, 30, 33] and the mobile computing techniques [12, 13], we hope to apply our method to more complex action recognition in real life.”
• All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey (2018, background): “Comparably, an edge-assisted adaptive deep learning framework for mobile object recognition is introduced in [350].”
• Deep Learning With Edge Computing: A Review (2019, background): “[122] D. Li, T. Salonidis, N. V. Desai, and M. C. Chuah, “DeepCham: Collaborative edge-mediated adaptive deep learning for mobile object recognition,” in Proc.”
• Machine Learning at the Network Edge: A Survey (2019, methodology): “DeepCham C++ Java Linux Android Full Model object recognition [77]”
• Adaptive Task Allocation for Mobile Edge Learning (2018, background): “t the cloud level and over wired distributed computing environments . Extendingthe abovedistributedlearning paradigmto resourceconstrained edge servers and nodes was not explored until very recently. [9]–[11]. The work of [9] explore the possibility of connected mobile android devices in a distribut”
• Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, methodology): “recognition accuracy when used together with a domainconstrained deep model [104].”
• Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review (2023, background): “DeepCham [350] consists of one edge server connected to multiple mobile devices.”
• Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “[217] Mobile object recognition Edge-based CNN”