ICTACT Journal on Communication Technology

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Dynamic Routing Algorithm for Efficient Wireless Traffic Management Using Evolutionary Algorithm


Affiliations
1 Department of Information Technology, St. Joseph College of Engineering, India
2 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, India
3 Department of Computer Science and Engineering, Knowledge Institute of Technology, India
4 Department of Electronics and Computer Engineering, Sanjivani College of Engineering, India
     

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Efficient traffic management in wireless networks is crucial for optimizing resource utilization and enhancing overall network performance. This paper introduces a novel approach to dynamic routing algorithms utilizing evolutionary algorithms for effective wireless traffic management. The proposed system leverages the adaptability and optimization capabilities of evolutionary algorithms to dynamically adjust routing paths based on real-time network conditions. Our algorithm employs a genetic programming framework to evolve and refine routing strategies, considering factors such as network congestion, link quality, and traffic load. This dynamic approach enables the network to autonomously adapt to changing conditions, ensuring optimal route selection for data transmission. The evolutionary nature of the algorithm allows it to continually learn and improve, making it well-suited for the dynamic and unpredictable nature of wireless environments. The effectiveness of the proposed algorithm is evaluated through extensive simulations, demonstrating significant improvements in terms of throughput, latency, and overall network efficiency compared to traditional static routing approaches. The system ability to handle diverse traffic patterns and adapt to varying network scenarios positions it as a robust solution for next-generation wireless networks.

Keywords

Dynamic Routing, Evolutionary Algorithms, Wireless Networks, Traffic Management, Genetic Programming.
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  • S. Chen, C. Zhao and M. Wu, “Compressive Network Coding for Wireless Sensor Networks: Spatio-Temporal Coding and Optimization Design”, Computer Networks, Vol. 108, No. 1, pp. 345-356, 2016.

  • W. Chen and I.J. Wassell, “Cost-Aware Activity Scheduling for Compressive Sleeping Wireless Sensor Networks”, IEEE Transactions on Signal Processing, Vol. 64, No. 9, pp. 2314-2323, 2016.

  • Z. Abbas and W. Yoon, “A Survey on Energy Conserving Mechanisms for the Internet of Things: Wireless Networking Aspects”, Sensors, Vol. 15, No. 10, pp. 24818- 24847, 2015.

  • W. Twayej and H.S. Al-Raweshidy, “M2M Routing Protocol for Energy Efficient and Delay Constrained in IoT Based on an Adaptive Sleep Mode”, Proceedings of SAI Conference on Intelligent Systems, pp. 306-324, 2016.

  • Q. Nadeem, M.B. Rasheed, N. Javaid, Z.A Khan, Y. Maqsood and A. Din, “M-GEAR: Gateway-Based EnergyAware Multi-Hop Routing Protocol for WSNs”, Proceedings of IEEE International Conference on Broadband, Wireless Computing, Communication and Applications, pp. 164-169, 2013.

  • S. Faisal, N. Javaid, A. Javaid and M.A. Khan, “Z-SEP: Zonal Stable Election Protocol for Wireless Sensor Networks”, Journal of Basic and Applied Scientific Research, Vol. 3, No. 5, pp. 132-139, 2013.

  • A.M. Mikaeil, B. Guo and Z. Wang, “Machine Learning to Data Fusion Approach for Cooperative Spectrums Sensing”, Proceedings of 6th International Conference on Cyber Enabled Distributed Computing and Knowledge Discovery, pp. 429-434, 2014.

  • T. Lathies Bhasker, “A Scope for MANET Routing and Security Threats”, ICTACT Journal on Communication Technology, Vol. 4, No. 4, pp. 840-848, 2013.

  • T. Karthikeyan and K. Praghash, “An Improved Task Allocation Scheme in Serverless Computing using Gray Wolf Optimization (GWO) based Reinforcement Learning (RIL) Approach”, Wireless Personal Communications, Vol. 117, No. 3, pp. 1-19, 2020.

  • A. Dorri and S. Reza, “A Fuzzy Congestion Controller to Detect and Balance Congestion in WSN”, International Journal of Wireless and Mobile Networks, Vol. 7, No. 1, pp. 137-145, 2015.

  • E. Hossain and V.K. Bhargava, “Cognitive Wireless Communication Networks”, Springer Publisher, 2007.


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  • Dynamic Routing Algorithm for Efficient Wireless Traffic Management Using Evolutionary Algorithm

Abstract Views: 61  |  PDF Views: 1

Authors

A. Tamizhselvi
Department of Information Technology, St. Joseph College of Engineering, India
P. Kavitha Rani
Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, India
P. Vijayalakshmi
Department of Computer Science and Engineering, Knowledge Institute of Technology, India
Sachin Vasant Chaudhari
Department of Electronics and Computer Engineering, Sanjivani College of Engineering, India

Abstract


Efficient traffic management in wireless networks is crucial for optimizing resource utilization and enhancing overall network performance. This paper introduces a novel approach to dynamic routing algorithms utilizing evolutionary algorithms for effective wireless traffic management. The proposed system leverages the adaptability and optimization capabilities of evolutionary algorithms to dynamically adjust routing paths based on real-time network conditions. Our algorithm employs a genetic programming framework to evolve and refine routing strategies, considering factors such as network congestion, link quality, and traffic load. This dynamic approach enables the network to autonomously adapt to changing conditions, ensuring optimal route selection for data transmission. The evolutionary nature of the algorithm allows it to continually learn and improve, making it well-suited for the dynamic and unpredictable nature of wireless environments. The effectiveness of the proposed algorithm is evaluated through extensive simulations, demonstrating significant improvements in terms of throughput, latency, and overall network efficiency compared to traditional static routing approaches. The system ability to handle diverse traffic patterns and adapt to varying network scenarios positions it as a robust solution for next-generation wireless networks.

Keywords


Dynamic Routing, Evolutionary Algorithms, Wireless Networks, Traffic Management, Genetic Programming.

References