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A neural networks-based hybrid routing protocol for wireless mesh networks
Nenad Kojić1, Irini Reljin, Branimir Reljin
1Digital Image Processing, Telemedicine and Multimedia Laboratory, Faculty of Electrical Engineering, University of Belgrade, Belgrade 11000, Serbia. nenad.kojic@ict.edu.rs
Sensors (Basel, Switzerland)
|September 13, 2012
Summary
This study introduces a novel hybrid routing protocol for wireless mesh networks (WMNs) using neural networks and mobile agents. The protocol optimizes network performance by minimizing delay and packet loss, enhancing resource utilization.
Area of Science:
- Computer Science
- Networking
- Artificial Intelligence
Background:
- Wireless Mesh Networks (WMNs) offer decentralized, high-bandwidth connectivity but face challenges with dynamic topology and link quality.
- Effective routing protocols are crucial for maintaining stable performance in WMNs.
- Existing proactive and reactive protocols have limitations that this research addresses.
Purpose of the Study:
- To propose a novel hybrid routing protocol for WMNs.
- To enhance network performance by minimizing delay and blocking probability.
- To optimize resource utilization through artificial intelligence.
Main Methods:
- Development of a hybrid routing protocol combining proactive and reactive strategies.
- Integration of mobile agent technologies controlled by a Hopfield neural network.
- Implementation of a multicriteria optimization routing metric considering delay and blocking probability.
Main Results:
- The proposed protocol avoids network flooding and introduces a new routing metric.
- It leverages artificial intelligence, specifically neural networks, for intelligent routing decisions.
- The protocol is designed to adapt to real-time network parameters and environments.
Conclusions:
- The novel hybrid routing protocol effectively addresses WMN challenges.
- Artificial intelligence, particularly neural networks, enhances WMN routing efficiency.
- The protocol demonstrates potential for maximizing resource usage and optimizing overall network performance.
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