Title
Scalable and Cost Efficient Maximum Concurrent Flow over IoT using Reinforcement Learning.
Abstract
The Internet of Things (IoT) is a network of billion of objects. Data streaming over IoT network is a tedious task that requires intelligent flow management and steering. In this paper, we propose a Distributed Maximum Concurrent Flow (DMCF) algorithm to solve the problem of distributing massive IoT video/data to large consumers over IP/data-centric networks. We propose two approaches based on graph theories, and using reinforcement learning techniques. The proposed approaches are implemented and evaluated over different complex graphs. Results show that in large graphs, reinforcement learning methods outperform classical graph theoretic ones.
Year
DOI
Venue
2020
10.1109/IWCMC48107.2020.9148257
IWCMC
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Abou-Bakr Djaker100.34
Kechar Bouabdellah200.34
Hatem Ibn-Khedher382.23
Hassine Moungla46722.76
Hossam Afifi542869.12