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Related Experiment Video

Updated: Mar 19, 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

Optimized topology control for large-scale IoT networks using graph-based localization.

Indrakshi Dey¹, Nicola Marchetti²

¹Walton Institute, South East Technological University, Waterford, Ireland. indrakshi.dey@waltoninstitute.ie.

Scientific Reports

|March 18, 2026

Summary

This summary is machine-generated.

This study introduces IoTNTop, a novel framework for reliable and energy-efficient Internet of Things (IoT) network topology control. IoTNTop optimizes device placement and communication links, significantly improving energy efficiency and performance in large-scale IoT deployments.

Keywords:

Eigenvector synchronization Graph realization Internet of things (IoT)Node localization Topology design and control

Related Experiment Videos

Last Updated: Mar 19, 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
Wireless Communication

Background:

Massive Internet of Things (IoT) deployments face challenges with energy constraints, interference, and unreliable links.
Traditional topology control methods are ineffective due to poor link quality, interference, and localization uncertainty in heterogeneous IoT networks.

Purpose of the Study:

To introduce IoTNTop, a unified graph-based framework for joint localization, graph embedding, and topology control in large-scale, resource-constrained IoT networks.
To address the limitations of conventional methods by directly coupling spatial structure with communication-aware topology optimization.

Main Methods:

Developed IoTNTop, a graph-based framework integrating localization and topology control using partial, noisy distance measurements.
Employed an error-centric objective to minimize end-to-end (E2E) error probability under practical constraints.
Utilized a scalable sub-graph stitching pipeline (EVS, LA, SDP) and a greedy SNR-guided edge selection for efficiency.

Main Results:

IoTNTop retains 60-80% of the per-node energy budget while keeping symbol error probability below 15% for most nodes.
The framework converges faster than Genetic Algorithm (GA) and brute-force baselines.
Achieved higher code rates at lower transmit power levels across various SNR regimes and network sizes.

Conclusions:

The IoTNTop framework offers a unified and effective solution for reliable and energy-efficient topology control in large-scale IoT networks.
Joint optimization of localization and topology control significantly enhances network performance and resource management.
The proposed methods demonstrate practical applicability and superior efficiency compared to existing approaches.