A load balancing scheme based on deep-learning in IoT

By Hye-young Kim, Jong-Min Kim. 2016

52 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

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. Syed Ali Hassan
2. Ekram Hossain
3. Rasheed Hussain
4. Fatima Hussain
5. Mohsen Guizani
6. Ala Al-Fuqaha
7. M. Mohammadi
8. Sameh Sorour

In Computer Vision and Pattern Recognition

1. Aunn Raza
2. Ala Al-Fuqaha
3. Hunain Arif
4. Ekram Hossain
5. Paul Patras
6. Amir Hussain
7. Mohsen Guizani
8. Syed Ali Hassan

In Electrical and Electronic Engineering

1. Ala Al-Fuqaha
2. M. Mohammadi
3. Hamed Haddadi
4. Paul Patras
5. Chaoyun Zhang
6. Ekram Hossain
7. Amir Hussain
8. Mohsen Guizani

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “Kim and Kim [168] used DBNs to perform load”
• Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “An MLP is also adopted in [306], where Kim and Song model the relationship between human mobility and personality, and achieve high prediction accuracy.”
• Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, background): “For instance, the authors in [76] developed DL-based load prediction and loadbalancing algorithms for IoT networks.”
• Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, methodology): “Given the scale of the IoT, a large amount of data is expected in the network and therefore requires a load balancing method, a load balancing algorithm based on restricted Boltzmann machine is proposed in [302].”