scholarly journals Intelligent Routing Mechanisms in IoT

2020 ◽  
Author(s):  
Gowrishankar Subhrahmanyam ◽  
Ishwari Ginimav
Keyword(s):  
Intelligent Routing

Value Iteration Architecture Based Deep Learning for Intelligent Routing Exploiting Heterogeneous Computing Platforms

2019 ◽  
Vol 68 (6) ◽  
pp. 939-950 ◽  
Author(s):  
Zubair Md. Fadlullah ◽  
Bomin Mao ◽  
Fengxiao Tang ◽  
Nei Kato
Keyword(s):  
Deep Learning ◽  
Heterogeneous Computing ◽  
Value Iteration ◽  
Intelligent Routing ◽  
Computing Platforms

MPSICA: An intelligent routing recovery scheme for heterogeneous wireless sensor networks

2015 ◽  
Vol 308 ◽  
pp. 49-60 ◽  
Author(s):  
Yongsheng Ding ◽  
Yifan Hu ◽  
Kuangrong Hao ◽  
Lijun Cheng
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Heterogeneous Wireless Sensor Networks ◽  
Recovery Scheme ◽  
Intelligent Routing

Implementation of an Emergency Evacuation System Using Intelligent Routing Using QualNet Simulator

2016 ◽  
Author(s):  
Praveen Kumar ◽  
M. Ruthwik ◽  
Manisha K. Sree ◽  
Misba Saad ◽  
Annapurna P. Patil
Keyword(s):  
Emergency Evacuation ◽  
Intelligent Routing ◽  
Evacuation System

scholarly journals Intelligent Routing Control for MANET Based on Reinforcement Learning

2018 ◽  
Vol 232 ◽  
pp. 04002
Author(s):  
Fang Dong ◽  
Ou Li ◽  
Min Tong
Keyword(s):  
Reinforcement Learning ◽  
Control Algorithm ◽  
Rapid Development ◽  
Selection Strategy ◽  
Performance Simulation ◽  
Multiple Parameters ◽  
Intelligent Routing ◽  
Routing Control ◽  
The Cost

With the rapid development and wide use of MANET, the quality of service for various businesses is much higher than before. Aiming at the adaptive routing control with multiple parameters for universal scenes, we propose an intelligent routing control algorithm for MANET based on reinforcement learning, which can constantly optimize the node selection strategy through the interaction with the environment and converge to the optimal transmission paths gradually. There is no need to update the network state frequently, which can save the cost of routing maintenance while improving the transmission performance. Simulation results show that, compared with other algorithms, the proposed approach can choose appropriate paths under constraint conditions, and can obtain better optimization objective.

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