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Updated: May 17, 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

A self-optimizing scheme for energy balanced routing in Wireless Sensor Networks using SensorAnt.

Ahmed M Shamsan Saleh¹, Borhanuddin Mohd Ali, Mohd Fadlee A Rasid

¹Department of Computer and Communication Systems Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. ah_almshreqy@yahoo.com

Sensors (Basel, Switzerland)

|November 1, 2012

Summary

This study introduces a self-optimization scheme for Wireless Sensor Networks (WSNs) using Ant Colony Optimization (ACO) to balance energy consumption. The proposed method enhances network lifetime and reduces packet loss by optimizing sensor node resources.

Keywords:

WSNs ant colony battery lifetime energy balancing energy consumption

Related Experiment Videos

Last Updated: May 17, 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

Area of Science:

Computer Science
Network Engineering

Background:

Wireless Sensor Networks (WSNs) face critical energy constraints for sensor nodes.
Energy-efficient routing protocols are essential for uniform power dissipation to the sink node.

Purpose of the Study:

To present a self-optimization scheme for WSNs to achieve balanced energy consumption.
To optimize sensor node resources, particularly batteries, for extended network lifetime.

Main Methods:

Utilized the Ant Colony Optimization (ACO) metaheuristic for path enhancement.
Employed a quality function based on multi-criteria metrics: minimum residual battery power, hop count, and average energy.
Distributed traffic load across the WSN to reduce individual node energy usage.

Main Results:

Demonstrated superior performance compared to Energy Efficient Ant-Based Routing (EEABR).
Achieved significant improvements in energy consumption, balancing, and overall network efficiency.
Reduced packet loss and extended the overall network lifetime.

Conclusions:

The proposed ACO-based self-optimization scheme effectively balances energy consumption in WSNs.
This approach enhances network efficiency and longevity by optimizing resource utilization and traffic distribution.