Implementation of deep belief neural network on energy efficient Routing Algorithm in WSN

• Autorzy: Mohammad Habibulla

Identyfikatory

DOI

10.24425/ijet.2025.153575

• Języki publikacji: EN

Abstrakty

EN

WSNs (Wireless sensor networks) have lately gained popularity such as remote tracking, a wide range of applications where information transfer through nodes to base stations necessitates a substantial amount of power. As a consequence, efficient routing methods for forwarding data to the base station should be used to decrease energy utilization and thus prolong the network's life span. Thus, Deterministic energy efficient protocol enhanced with deep learning model is proposed to obtain optimal routing that indirectly improves the life span of WSN. Ant Colony (AC) Environment is considered for system optimization with the goal of picking the proper conceivable clusters in the shortest amount of duration in a feasible cluster. Furthermore, in routing optimization to increase the effectiveness of service, a Newly Designed Enhanced Ant Colony has been suggested, where premier operators were used to increase speed of iteration and choose the shortest path. Ultimately, a Deep Convolution Classifier is used to discover the optimal path. Consequently, compared to other existing methods, our suggested model enhances QoS, reduces energy consumption, and provides superior routing to the sensor nodes, all of which mobile nodes increase their lifespan.

Słowa kluczowe

EN

Wireless Sensor Network; WSNs; Ant Colony; AC; Deep belief Neural Network; Cluster head; Sink

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2025
• Tom: Vol. 71, No. 1
• Strony: 309--315
• Opis fizyczny: Bibliogr. 31 poz., rys.

Twórcy

autor

Mohammad Habibulla

• PVP Siddhartha Institute of Technology

• https://orcid.org/0000-0002-0697-1540

Bibliografia

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• [23] Habibulla Mohammad, A. S. Chandrasekhara Sastry ,Implementation of Three-Tier Multihop Technique in Advance Coupling Network Model-Deterministic Energy-Efficient Clustering of Wireless Sensor Networks, Journal of Computational and Theoretical Nanoscience, 16(5-6), 2019: 2581-2589 https://doi.org/10.1166/jctn.2019.7934
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Uwagi

1. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).

2. This work was supported by Research & Development Cell-ECE Department, PVP Siddhartha Institute of Technology.