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Vol. 12  No. 1 (2026) · Review Article

A Comprehensive Review on Swarm Intelligence-Based Routing Algorithms for Network Systems

Bhanumathi Velusamy
Department of Electronics and Communication Engineering, Anna University Regional Campus, Coimbatore, India , India

Sangeetha Chitteth Pushpan
Department of Electronics and Communication Engineering, Anna University Regional Campus, Coimbatore, India , India

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Received 10 October 2025; received in revised form 18 February 2026; accepted 26 February 2026

📅 Published: March 2026

Keywords: wireless sensor networks, routing algorithms, ant colony optimization, swarm intelligence, energy efficiency

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Abstract

The principles of bio-inspired or swarm intelligence algorithms can be effectively used to achieve optimal solutions in routing for complex and dynamic wireless sensor networks or body area networks. As the name indicates, it is a field that is inspired by natural living beings like ants, bees, fishes, etc. Studies have proved that the routing protocols based on such bio-inspired methods perform better in terms of energy efficiency, reliability, adaptivity, scalability, and robustness. The general classification of routing protocols is classical-based and swarm-based routing protocols, where both the protocols were specifically categorized as data-centric, location-aware, hierarchical and network flow, and QoS aware protocols. In this paper, an evocative taxonomy and comparison of various swarm-based routing algorithms are presented. A brief discussion about the sensor network design and the major factors that influence the routing is also discussed. The comparative analysis of the selected swarm-based protocols is also done with respect to routing characteristics like query based, route selection, energy efficiency, and path selection. From the review, it is observed that the selection of a routing protocol is application dependent. This paper will be helpful to the researchers as a reference on bio-inspired algorithms for new protocol designs and also for the proper selection of routing protocols according to the type of applications.

How to Cite

Bhanumathi Velusamy, Sangeetha Chitteth Pushpan, “A Comprehensive Review on Swarm Intelligence-Based Routing Algorithms for Network Systems,” International Journal of Technical Innovation in Modern Engineering & Science, vol. 12, no. 1, pp. 14-28, March 2026.

References

[1] G. Anatasi, M. Conti, M. D. Francesco, and A. Passarella, “Energy conservation in wireless sensor networks: a survey,” Ad hoc networks, vol. 7, no. 3, pp. 537-568, May 2009.

[2] M. Singh and S. Soni, “A comprehensive review of fuzzy-based clustering techniques in wireless sensor networks,” Sensor Review, vol. 37, no. 3, pp. 289-304, June 2017.

[3] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Transactions on Wireless Communications, vol. 1, no. 4, pp. 660-670, Oct. 2002.

[4] F. A. Aderohunmu, J. D. Deng, and M. K. Purvis, “A deterministic energy-efficient clustering protocol for wireless sensor networks,” Proc. IEEE Intelligent Sensors Networks and Information Processing, IEEE Press, Dec. 2011, pp. 341-346.

[5] S. Shi, X. Liu, and X. Gu, “An energy-efficiency optimized LEACH-C for wireless sensor networks,” Proc. IEEE Communications and Networking, IEEE Press, Aug. 2012, pp. 487-492.

[6] I. Gupta, D. Riordan, and S. Sampalli, “Cluster-head election using fuzzy logic for wireless sensor networks,” Proc. IEEE Communication Networks and Services Research, IEEE Press, May 2005, pp. 255-260.

[7] J. M. Kim, S. H. Park, Y. J. Han, and T. M. Chung, “CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks,” Proc. IEEE advanced communication technology, IEEE Press, Feb. 2008, pp. 654-659.

[8] T. M. Tashtoush and M. A. Okour, “Fuzzy self-clustering for wireless sensor networks,” Proc. IEEE Embedded and Ubiquitous Computing, IEEE Press, Dec. 2008, pp. 223-229.

[9] T. Haider and M. Yusuf, “A fuzzy approach to energy optimized routing for wireless sensor networks,” International Arab Journal of Information Technology, vol. 6, no. 2, pp. 179-185, Apr. 2009.

[10] G. Ran, H. Zhang, and S. Gong, “Improving on LEACH protocol of wireless sensor networks using fuzzy logic,” Journal of Information & Computational Science, vol. 7, no. 3, pp. 767-775, Mar. 2010.

[11] Y. Shen and H. Ju, “Energy-efficient cluster-head selection based on a fuzzy expert system in wireless sensor networks,” Proc. IEEE/ACM Green Computing and Communications, IEEE Press, Aug. 2011, pp. 110-113.

[12] E. Saeedian, M. N. Torshiz, M. Jalali, G. Tadayon, and M. M. Tajari, “CFGA: Clustering wireless sensor network using fuzzy logic and genetic algorithm,” Proc. IEEE Wireless Communications, Networking, and Mobile Computing, IEEE Press, Sep. 2011, pp. 1-4.

[13] J. S. Lee and W. L. Cheng, “Fuzzy-logic-based clustering approach for wireless sensor networks using energy prediction,” IEEE Sensors Journal, vol. 12, no. 9, pp. 2891-2897, Sep. 2012.

[14] S. B. Alla SB, A. Ezzati, and A. Mohsen, “Gateway and cluster head election using fuzzy logic in heterogeneous wireless sensor networks,” Proc. IEEE Multimedia Computing and Systems, IEEE Press, May 2012, pp. 761-766.

[15] R. Mhemed, N. Aslam, W. Phillips, and F. Comeau, “An energy-efficient fuzzy logic cluster formation protocol in wireless sensor networks,” Procedia Computer Science, vol. 10, pp. 255-262, Dec. 2012.

[16] D. Izadi, J. Abawajy, and S. Ghanavati, “An alternative clustering scheme in wsn,” IEEE Sensors Journal, vol. 15, no. 7,

pp. 4148-4155, July 2015.

[17] A. Devasena and B. Sowmya, “Fuzzy-Based BEENISH Protocol for Wireless Sensor Network,” Circuits and Systems, vol. 7, no. 8, pp. 1893-1905, Jun. 2016.

[18] H. E. Alami and A. Najid, “Energy-efficient fuzzy logic cluster head selection in wireless sensor networks,” Proc. IEEE Information Technology for Organizations Development, IEEE Press, Apr. 2016, pp. 1-7.

[19] R. K. Shakya, Y. N. Singh, and N. K. Verma, “Generic correlation model for wireless sensor network applications,” IET Wireless Sensor Systems, vol. 3, no. 4, pp. 266-276, Dec. 2013.

[20] J. Xu, W. Liu, F. Lang, Y. Zhang, and C. Wang, “Distance measurement model based on RSSI in WSN,” Wireless Sensor Network, vol. 2, no. 8, pp. 606-611, Aug. 2010.

[21] Z. Liu, W. Xing, B. Zeng, Y. Wang, and D. Lu, “Distributed spatial correlation-based clustering for approximate data collection in WSNs,” Proc. IEEE International Conference on Advanced Information Networking and Applications (AINA), IEEE Press, Mar 2013, pp. 56-63.

[22] M. Shen and S. Chen, “Unequal distributed spatial correlation-based tree clustering for approximate data collection,” Proc. International Conference on Soft Computing in Information Communication Technology (SCICT), Atlantis Press, May 2014, pp. 93-97.

[23] H. T. Bhavana and J. K. Murthy, “Spatial correlation based clustering algorithm for random and uniform topology in WSNs,” International Journal of Research in Engineering and Technology, vol. 3, no. 6, pp. 83-87, June 2014.

[24] F. D. Rango, N. Palmieri, and S. Ranieri, “Spatial correlation based Low Energy Aware Clustering (LEACH) in a wireless sensor networks,” Advances in Electrical and Electronic Engineering, vol. 13, no. 4, pp. 350-358, 2015.

[25] G. Pau, “Power consumption reduction for wireless sensor networks using a fuzzy approach,” International Journal of Engineering & Technology Innovations, vol. 6, no. 1, pp. 55-67, Jan. 2016.

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