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Distributed neuro-fuzzy routing for energy-efficient IoT smart city applications in WSN

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Abstract

Wireless sensor networks (WSNs) enable seamless data gathering and communication, facilitating efficient and real-time decision-making in IoT monitoring applications. However, the energy required to maintain communication in WSN-based IoT networks poses significant challenges, such as packet loss, packet drop, and rapid energy depletion. These issues reduce network life and performance, increasing the risk of delayed packet delivery. To address these challenges, this work presents a novel energy-efficient distributed neuro-fuzzy routing model executed in two stages to enhance communication efficiency and energy management in WSN-based IoT applications. In the first stage, nodes with high energy levels are predicted using a fusion of distributed learning with neural networks and fuzzy logic. In the second stage, clustering and routing are performed based on the predicted eligible nodes, incorporating thresholds for energy and distance with two combined metrics. The cluster head (CH) combined metric optimizes cluster head selection, while the next-hop combined metric facilitates efficient multi-hop communication. Extensive simulation results demonstrate that the proposed model significantly enhances network lifetime compared to EANFR, RBFNN T2F, and TTDFP by 9.48%, 25%, and 31.5%, respectively.

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Data availibility statement

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

ACK:

Acknowledgment

ADV:

Advertisement

ANFIS:

Adaptive neuro fuzzy inference system

BS:

Base station

CH:

Cluster head

CCHs:

Candidate cluster heads

DNN:

Deep neural network

EANFR:

Energy-aware neuro fuzzy routing

EEDC:

Energy efficient dynamic clustering

FedAVG:

Federated average

FND, HND, LND:

First node died, half node died, last node died

LEACH:

Low energy adaptive clustering hierarchical

SEP:

Stable election protocol

I-SEP:

Improved stable election protocol

GA:

Genetics algorithm

MF:

Membership function

NN:

Neural network

NFL:

Neuro-fuzzy learning

PDR:

Packet delivery ratio

RBFNN:

Radial basis function neural network

RFCM-GA:

Rough fuzzy c means and genetic algorithm

ReLU:

Rectified linear unit

T2F:

Type 2 fuzzy

TTDFP:

Two tier distributed fuzzy protocol

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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  1. Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, Tamilnadu, 620015, India

    S. Jeevanantham, C. Venkatesan & B. Rebekka

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Contributions

All authors contributed to the study conception and design. Methodology, Software, analysis and original draft preparation were performed by SJ. The final draft of the manuscript was supervised, reviewed and edited by BR. All the authors read and approved the final manuscript.

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Correspondence to S. Jeevanantham.

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Jeevanantham, S., Venkatesan, C. & Rebekka, B. Distributed neuro-fuzzy routing for energy-efficient IoT smart city applications in WSN. Telecommun Syst 87, 497–516 (2024). https://doi.org/10.1007/s11235-024-01195-6

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