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2000
Volume 18, Issue 9
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Background

WSNs are composed of various tiny low battery power Sensor Nodes (SNs). It is required to sense the dynamic network environments, which may fluctuate due to external factors or the system model itself. Furthermore, sensory data is transmitted over the communication channel to the destination. Integration of Artificial Neural Network (ANN) in WSNs provides the facility to learn SNs and networks from past experiences and make the predictions based on them. The computational ability of ANN may help to choose an efficient route for data transfer in WSNs. Also, it is able to improve the performance of the network w.r.t power consumption, latency, throughput, . This work explores an efficient way to manage the energy of the SNs using an ANN-based routing approach for the selection of Cluster Head (CH).

Methods

In this work, an ANN-based and algorithmic approach has been applied to select the CH. Various popular routing strategies such as LEACH-C, TEEN, SEP & DEEC have been used for simulation. LEACH-C and TEEN belong to the homogeneous category, while SEP and DEEC are categorized as heterogeneous. ANN based CH selection strategy has been proposed and compared with the other routing techniques.

Results

ANN based CH selection strategy has been simulated and compared. Results revealed that CH election based on ANN will enhance the network lifetime. It is clear from the simulation results that ANN-DEEC is much more stable than protocols of the same category.

Conclusion

ANN-DEEC outperforms five times more than LEACH-C, 2.5 times more than TEEN and SEP, twice DEEC and 10-20% more than EDDEC and Advanced-DEEC. ANN-DEEC transmits 71021 packets to BS more than any other protocol.

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2023-09-19
2026-01-08

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A Comparative Analysis of ANN-based Homogeneous and Heterogeneous Routing Protocols for Selection of Cluster Head in WSN
Recent Advances in Electrical & Electronic Engineering 18, 1541 (2025); https://doi.org/10.2174/2352096516666230206161307
/content/journals/raeeng/10.2174/2352096516666230206161307
/content/journals/raeeng/10.2174/2352096516666230206161307
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  • Article Type:     Research Article
Keyword(s): ANN; energy efficient; heterogeneous routing; homogeneous routing; ML; WSN

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