Energy-efficient clustering protocol in wireless sensor networks based on multi-objective binary dragonfly optimization algorithm and Mamdani fuzzy inference system

Behzadi, Mohammadhosein; Motameni, Homayun; Mohamadi, Hosein; Barzegar, Behnam

doi:10.22075/ijnaa.2025.38291.5502

Energy-efficient clustering protocol in wireless sensor networks based on multi-objective binary dragonfly optimization algorithm and Mamdani fuzzy inference system

Articles in Press

Document Type : Research Paper

Authors

Mohammadhosein Behzadi ¹

Homayun Motameni ¹

Hosein Mohamadi ²

Behnam Barzegar ³

¹ Department of Computer Engineering, Sar.C., Islamic Azad University, Sari, Iran

² Department of Computer Engineering, Az.C., Islamic Azad University, Azadshahr, Iran

³ Department of Computer Engineering, Bab.C., Islamic Azad University, Babol, Iran

10.22075/ijnaa.2025.38291.5502

Abstract

Clustering is an efficient method for designing routing algorithms in wireless sensor networks (WSNs) that enhances the network's lifespan and scalability. Considering the limited capabilities of sensor nodes, such as energy, processing power, and communication range, clustering-based routing protocols adapt network performance to these constraints. Recent related works have demonstrated that energy consumption in sensor nodes can be minimized using clustering methods. In the clustering approach, the selection of cluster heads and the formation of clusters play a critical role in data transmission. This article proposes a hybrid approach based on a multi-objective binary dragonfly optimization algorithm and Mamdani fuzzy inference system for clustering protocols in homogeneous wireless sensor networks. In this approach, the multi-objective binary dragonfly optimization algorithm is initially employed for cluster head selection, followed by the Mamdani-type fuzzy inference system for cluster formation. Finally, a multi-hop routing process is utilized for data packet transmission. The proposed clustering protocol has been simulated on WSNs with various topologies. Its performance has been evaluated based on multiple criteria, including average energy consumption, the first node to die, 50% of nodes dead, the last node to die, and the number of packets transmitted to the base station. The evaluation results indicate that the proposed clustering protocol outperforms previous protocols.

Keywords

Wireless sensor network

clustering

network lifetime

dragonfly optimization algorithm

fuzzy inference system

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