Authors:
Amal Alharbi
;
Abdullah Al-Dhalaan
and
Miznah Al-Rodhaan
Affiliation:
King Saud University, Saudi Arabia
Keyword(s):
Cognitive Packet Network (CPN), Mobile Ad Hoc Network (MANET), Q-Routing, Reinforcement Learning, Stability-based Routing.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Architectures and Mechanisms
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computational Neuroscience
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Mobile Ad hoc Networks (MANET) are self-organized networks which are characterized by dynamic topologies in time and space. This creates an instable environment, where classical routing approaches cannot achieve high performance. Thus, adaptive routing is necessary to handle the random changing network topology. This research uses Reinforcement Learning approach with Q-Routing to introduce our MANET routing algorithm: Stability-Aware Cognitive Packet Network (CPN). This new algorithm extends the work on CPN to adapt it to the MANET environment with focus on path stability metric. CPN is a distributed adaptive routing protocol that uses three types of packets: Smart Packets for route discovery, Data Packets for carrying data payload, and Acknowledgments to bring back feedback information for the Reinforcement Learning reward function. The research defines a reward function as a combination of high stability and low delay path criteria to discover long-lived routes without disrupting t
he overall delay. The algorithm uses Acknowledgment-based Q-routing to make routing decisions which adapt on line to network changes allowing nodes to learn efficient routing policies.
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