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Intelligent Routing in IOT: A Comparative Analysis of Glowworm Swarm Optimization and Shuffled Frog Leaping Algorithm

Author

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  • R. Yanitha
  • M. Logambal

Abstract

The efficiency of routing protocols in the Internet of Things (IoT) plays a critical role in ensuring reliable communication, energy efficiency, and Quality of Service (QoS). This paper presents a comparative analysis of two bio-inspired metaheuristic algorithms—Glowworm Swarm Optimization (GSO) and the Shuffled Frog Leaping Algorithm (SFLA)—for intelligent IoT routing. GSO, inspired by the luminescence-based behavior of glowworms, and SFLA, modeled on the memetic evolution of frog populations, are evaluated against key QoS parameters including end-to-end delay, packet delivery ratio (PDR), throughput, energy consumption, and routing overhead. Simulation results highlight the strengths and trade-offs of each algorithm under varying network conditions. The findings provide insights into the applicability of GSO and SFLA for optimizing IoT routing, contributing to the design of energy-efficient and high-performance IoT communication systems.

Suggested Citation

  • R. Yanitha & M. Logambal, 2026. "Intelligent Routing in IOT: A Comparative Analysis of Glowworm Swarm Optimization and Shuffled Frog Leaping Algorithm," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 12(2), pages 293-304, April.
    • Handle: RePEc:jbh:ijsrcs:v12:y2026:i2:id:1929
    DOI: 10.32628/CSEIT26121348
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT26121348
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