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Volume 18, Issue 8
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Introduction

This paper introduces two meta-heuristic approaches utilizing Swarm Intelligence and ant colony optimization techniques. The strategy comprises applying smart routing technology to optimize a dynamic IoT network computed path.

Methods

The issue of route selection to achieve the target and critical factors such as network energy, left energy in each gadget, run out IoT nodes count has been explored. After rigorous iterations extending up to 1000, the simulation has yielded results for two distinctive routing approaches. The ABED (ACO- Breadth first search- Euclidean- Dynamic) and the ADED (ACO- Dijkstra algorithm -Euclidean- Dynamic) have simulated and compared their network efficiencies using MATLAB.

Results

At node 200, ABED exhibits a performance advantage over ADED of 1.6%. This efficiency differential between ABED and ADED expands to 2.9% at 300 nodes and further to 2.6% at 400 nodes. Furthermore, ABED showcases superior network stability in routing techniques compared to ADED. Specifically, ABED's routing technique achieves a more consistent network compared to ADED.

Conclusion

In networks comprising 500 nodes, ABED surpasses ADED by 13.33% in the context of HND (Half Node Dead) and by 6.7% in the case of LND (Last Node Dead).

© 2025 Bentham Science Publishers
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2024-07-19
2025-11-15

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Intelligent Routing Approaches Based on Ant Colony Optimization for Dynamic Internet of Things Network
Recent Advances in Electrical & Electronic Engineering 18, 1348 (2025); https://doi.org/10.2174/0123520965316826240705072158
/content/journals/raeeng/10.2174/0123520965316826240705072158
/content/journals/raeeng/10.2174/0123520965316826240705072158
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  • Article Type:     Research Article
Keyword(s): ant colony optimization; Breadth first search- euclidean- dynamic; dijkstra algorithm-euclidean-dynamic; internet of things; last node dead; swarm intelligence

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