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

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A Low Complexity Persistent Reconnaissance Algorithm for FANET.

Yuan Guo^1,2, Hongying Tang¹, Ronghua Qin¹

¹Science and Technology on Micro-System Laboratory, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.

Sensors (Basel, Switzerland)

|December 11, 2022

Summary

This summary is machine-generated.

This study introduces new algorithms for flying ad hoc networks (FANETs) that help unmanned aerial vehicles (UAVs) avoid dynamic threats while maintaining connectivity. These methods significantly reduce threat exposure compared to traditional approaches.

Keywords:

FANET PSO-based dynamic threat avoidance low complexity persistent reconnaissance relay node placement unmanned aerial vehicles

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Area of Science:

Computer Science
Electrical Engineering
Robotics

Background:

Unmanned aerial vehicle (UAV) technology is rapidly advancing, leading to widespread civilian and military applications.
Flying ad hoc networks (FANETs) face challenges due to UAVs' high mobility and dynamic environmental threats.
Existing research often overlooks the dynamic nature of threats, complicating connectivity maintenance.

Purpose of the Study:

To develop algorithms for UAVs to dynamically adapt to and avoid high-risk areas while ensuring FANET connectivity.
To address the challenge of maintaining network connectivity amidst dynamic threats and high UAV mobility.
To introduce novel concepts for quantifying and mitigating threats in FANETs.

Main Methods:

Proposed a particle swarm optimization (PSO)-based threat avoidance and reconnaissance FANET construction algorithm (TARFC).
Introduced the concept of threat probability density function (threat PDF) to model dynamic threats.
Defined total edit distance (TED) to capture changes in FANETs and threat factors over time.
Developed a dynamic threat avoidance and continuous reconnaissance FANET operation algorithm (TA&CRFO) for semi-distributed control.

Main Results:

Both TARFC and TA&CRFO effectively maintained network connectivity and avoided threats in dynamic scenarios.
TARFC and TA&CRFO reduced the average threat value of UAVs by 3.99–27.51% and 3.07–26.63%, respectively, compared to the standard PSO algorithm.
TA&CRFO demonstrated significantly lower computational complexity (20.08% of TARFC) with limited distributed moderation.

Conclusions:

The proposed TARFC and TA&CRFO algorithms offer robust solutions for threat avoidance and connectivity maintenance in dynamic FANET environments.
These algorithms enable UAVs to navigate and operate more safely and efficiently in complex, threat-laden scenarios.
TA&CRFO presents a more computationally efficient approach for semi-distributed control in dynamic FANETs.