Cloudlets: bringing the cloud to the mobile user
By Tim Verbelen, Pieter Simoens, F. de Turck, Bart Dhoedt. International Workshop on Multiple Classifier Systems, 2012
388 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/22d41328530154267c54d436d2d59d33fd9bacdc
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
- Koustabh Dolui
- Soumya Kanti Datta
- Yuichi Kawamoto
- Nei Kato
- Zubair Muhammad Fadlullah
- Tor Skeie
- Bart Dhoedt
- Martin Reisslein
In Computer Vision and Pattern Recognition
- Koustabh Dolui
- Soumya Kanti Datta
- Mooi Choo Choo Chuah
- Nei Kato
- Bart Dhoedt
- Martin Reisslein
- Steven Bohez
- Zubair Muhammad Fadlullah
Citations in the corpus
- A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues (2017, background): “Moreover, edge analytics research is evolving in various forms such as Fog computing, cloudlets [146], mobile edge computing [147], and so forth.”
- Software Defined Optical Networks (SDONs): A Comprehensive Survey (2015, background): “Also, the exploration of the benefits of FiWi networks for decentralized cloudlets [356]–[359] that support mobile wireless network services is an important future research direction [360].”
- Optimal Cloudlet Placement and User to Cloudlet Allocation in Wireless Metropolitan Area Networks (2017, neutral): “In [16], [17], Verbelen et al.”
- Mobile Edge Computing: A Survey (2018, background): “The primary focus of cloudlet is to bring cloud technologies closer to the end-user, providing support to resource-and latency-sensitive applications [26].”
- Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing (2017, methodology): “Authors of [11] propose a Cloudlet based architecture which attempts to leverage the computational abilities of devices in a LAN network by handling applications”
- DeepCham: Collaborative Edge-Mediated Adaptive Deep Learning for Mobile Object Recognition (2016, background): “Thus an edge-based solution is required which ideally would perform adaptation training on the mobile phones.”
- The cascading neural network: building the Internet of Smart Things (2017, background): “Local computation nodes (cloudlets [29]) can be used as a substitute for remote cloud servers.”