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Updated: Jun 2, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Improved beluga whale optimization algorithm based cluster routing in wireless sensor networks.

Hao Yuan¹, Qiang Chen¹, Hongbing Li^2,3

¹Chongqing Key Laboratory of Geological Environmental Monitoring and Disaster Early Warning in the Three Gorges Reservoir Area, Chongqing Three Gorges University, Chongqing 404120, China.

Mathematical Biosciences and Engineering : MBE

|March 29, 2024

Summary

This summary is machine-generated.

This study introduces tCBWO-DPR, an improved routing strategy for wireless sensor networks (WSNs). It enhances cluster head selection and data transmission paths to extend network life and reduce energy consumption.

Keywords:

beluga whale optimization heuristic algorithm hierarchical routing minimum spanning tree wireless sensor networks

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Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Cluster routing in wireless sensor networks (WSNs) faces challenges with uneven energy depletion due to suboptimal cluster head distribution and data transmission paths.
Existing methods struggle to balance energy consumption and network stability, leading to premature network failure.

Purpose of the Study:

To propose a novel routing strategy, tCBWO-DPR, for clustered WSNs to address uneven energy depletion.
To enhance cluster head selection and optimize data transmission routes for improved network longevity and efficiency.

Main Methods:

Introduced a new excitation function for cluster head selection, correlating node energy with positional relationships.
Improved the beluga whale optimization (BWO) algorithm with a cosine factor and t-distribution (tCBWO-DPR) for better search capabilities and convergence.
Utilized Prim's algorithm for spanning tree construction and a novel DPR method for optimal inter-cluster routing based on correlation distances.

Main Results:

The tCBWO-DPR algorithm demonstrated significant improvements in extending the network's survival cycle.
Simulation results confirmed a notable reduction in the average energy consumption of the wireless sensor network.
Enhanced cluster head selection and optimized data transmission paths contributed to increased network stability.

Conclusions:

The proposed tCBWO-DPR routing strategy effectively mitigates uneven energy depletion in clustered WSNs.
The enhanced beluga whale optimization algorithm and optimized routing paths lead to superior network performance and longevity.
This approach offers a promising solution for energy-efficient and stable WSN operations.