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Updated: Jul 2, 2025

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 energy efficiency using adaptive ant colony distributed intelligent based clustering in wireless sensor

K A Sharada¹, T R Mahesh², Saravanan Chandrasekaran³

¹Department of Computer Science and Engineering, HKBK College of Engineering, Visvesvaraya Technological University, Bengaluru, India.

Scientific Reports

|February 22, 2024

Summary

The adaptive ant colony distributed intelligent based clustering algorithm (AACDIC) enhances cognitive radio spectrum sensing by optimizing cluster counts for improved energy efficiency and faster convergence. This advanced method significantly reduces power consumption and detection errors in dynamic user environments.

Keywords:

Adaptive ant colony distributed intelligent based clustering algorithm (AACDIC)Convergence time Energy efficiency Network capacity performance Node power Spectrum sensing

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Area of Science:

Wireless Communication
Optimization Algorithms
Cognitive Radio Networks

Background:

Optimization algorithms aim to reduce energy consumption and interference in data transmission.
Cognitive radio (CR) systems rely on spectrum sensing for efficient spectrum utilization.
Existing methods face challenges in dynamic environments with unpredictable user numbers.

Purpose of the Study:

To present the adaptive ant colony distributed intelligent based clustering algorithm (AACDIC) for enhanced spectrum sensing in CR systems.
To improve energy efficiency and reduce sensing errors using distributed cluster-based sensing.
To address systems with unpredictable numbers of primary and secondary users.

Main Methods:

The AACDIC method determines optimal cluster counts using connectedness and distributed sensing.
It utilizes multi-user clustered communication to accelerate solution convergence.
The algorithm is designed to handle dynamic variations in primary and secondary user populations.

Main Results:

AACDIC reduces node power usage by 9.646% and average Secondary User node power by 24.23% compared to other algorithms.
It achieves a low Signal-to-Noise Ratio (SNR) of 2 dB, increasing detection likelihood.
The algorithm demonstrates the lowest false positive rate among compared primary detection optimization strategies.

Conclusions:

AACDIC optimizes network capacity by effectively solving multimodal optimization challenges.
SNR significantly impacts detection probability, crucial for realistic, dynamic environments.
The proposed algorithm enhances detection reliability in energy-constrained wireless sensor networks (WSNs).