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Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor

Feras Mohammed A-Matarneh1, Bassam A Y Alqaralleh2, Fahad Aldhaban2

  • 1Department of Computer Science, University College of Duba, University of Tabuk, Tabuk 71491, Saudi Arabia.

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Summary

This study introduces a novel routing protocol for wireless sensor networks (WSNs) using swarm intelligence and an adaptive neuro-fuzzy inference system (ANFIS). The SI-ANFISR protocol enhances network lifetime and performance by optimizing cluster head selection and routing paths.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) face challenges in maximizing network lifetime due to energy constraints of sensor nodes.
  • Existing clustering and routing techniques aim for energy efficiency but often treat multihop routing as an NP-hard problem.
  • Computational intelligence, including fuzzy logic and swarm intelligence (SI), offers potential solutions for complex routing decisions.

Purpose of the Study:

  • To design a novel routing protocol, Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-based Routing (SI-ANFISR), for clustered WSNs.
  • To enhance energy efficiency and prolong the operational lifetime of WSNs through optimized cluster head selection and multihop routing.
  • To introduce a new approach by integrating the Squirrel Search Algorithm (SSA) for tuning the ANFIS model in WSN routing.

Main Methods:

  • A weighted clustering algorithm is employed for electing cluster heads (CHs) and forming clusters.
  • An Adaptive Neuro-Fuzzy Inference System (ANFIS) model is designed for optimal route selection, utilizing residual energy, node degree, and node history as inputs.
  • The Squirrel Search Algorithm (SSA) is utilized to fine-tune the membership functions (MFs) of the ANFIS model for improved routing performance.

Main Results:

  • The proposed SI-ANFISR technique effectively determines cluster heads and optimal multihop routes.
  • Experimental validation demonstrates significant performance improvements compared to existing WSN routing techniques.
  • The SI-ANFISR technique achieved a maximum throughput of 43838 kbps and a residual energy of 0.4800 J.

Conclusions:

  • The SI-ANFISR protocol offers a novel and effective solution for enhancing WSN lifetime and performance.
  • The integration of ANFIS with SSA presents a unique contribution to WSN routing strategies.
  • The optimized routing approach leads to substantial gains in network throughput and residual energy conservation.