April 30, 2024IEIE Transactions on Smart Processing and Computing

Multi-objective Cluster-head-based Energy-aware Routing using a Separable Convolution Neural Network in a Wireless Sensor Network

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Energy consumption decreases by 22.3% and 25.04% compared to baseline routing protocols, thereby retaining critical node power across a wireless sensor network.
Simulation outcomes evaluate multi-objective pelican optimization algorithm routing alongside a separable convolution neural network for cluster head selection.
Supports prolonged lifespan for depleted sensor nodes, while resolving trust management challenges and shortening transmission distances in dynamic field deployments.

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Implication

Simulation study demonstrates improved energy consumption in wireless sensor networks using neural network routing, highlighting enhanced lifespan for energy-constrained sensor nodes.

Cite This Study

Ather et al. (2024) studied this question.

synapsesocial.com/papers/68e6ccfeb6db64358764a868 https://doi.org/10.5573/ieiespc.2024.13.2.105

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Implication

Simulation study demonstrates improved energy consumption in wireless sensor networks using neural network routing, highlighting enhanced lifespan for energy-constrained sensor nodes.

Multi-objective Cluster-head-based Energy-aware Routing using a Separable Convolution Neural Network in a Wireless Sensor Network

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Cite This Study

Multi-objective Cluster-head-based Energy-aware Routing using a Separable Convolution Neural Network in a Wireless Sensor Network

Key Points

Discussion

Implication

Cite This Study

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