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Atomic Force Microscopy01:08

Atomic Force Microscopy

Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...

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A Low Complexity Persistent Reconnaissance Algorithm for FANET.

Yuan Guo1,2, 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
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Summary

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:
FANETPSO-baseddynamic threat avoidancelow complexitypersistent reconnaissancerelay node placementunmanned 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.