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Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

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Related Experiment Video

Updated: Jun 13, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

A Reliable and Secure Cluster-Routing Framework for Drone-Assisted Disaster Management in Smart Cities.

Bader Alwasel1, Ahmed Salim1,2, Pravija Raj Patinjare Veetil3

  • 1Unit of Scientific Research, Applied College, Qassim University, Buraydah 52571, Saudi Arabia.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces the Weighted Average Yo-Yo-based Clustering and Routing (WAY-CR) scheme for resilient drone networks in smart cities after disasters. WAY-CR enhances security, energy efficiency, and network stability for critical emergency response communications.

Keywords:
disaster managementflying ad hoc networks (FANETs)post-disaster recoveryroutingtrustunmanned aerial vehicles (drones)

Related Experiment Videos

Last Updated: Jun 13, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Terrestrial communication infrastructure is vulnerable to disasters, hindering smart city emergency response.
  • Existing solutions lack adaptiveness, security, and energy efficiency for post-disaster scenarios.

Purpose of the Study:

  • To introduce the Weighted Average Yo-Yo-based Clustering and Routing (WAY-CR) scheme for adaptive, secure, and energy-efficient drone-assisted post-disaster network recovery.
  • To enhance emergency response coordination in smart cities through a robust communication network.

Main Methods:

  • Developed a novel WAY-based metaheuristic optimizer integrating Yo-Yo Motion for improved cluster head selection.
  • Implemented a secure communication model using Paillier Homomorphic Cryptosystem (PHC) and trust evaluation for drone authentication.
  • Introduced Trust-Aware Boltzmann Path Selection for adaptive, secure, and energy-efficient routing paths.
  • Formulated a multi-objective optimization model to balance cost, energy, trust, stability, and coverage.

Main Results:

  • WAY-CR demonstrated superior performance over state-of-the-art Flying Ad Hoc Network (FANET) baselines.
  • Achieved significant improvements in energy efficiency, cluster stability, trust accuracy, and end-to-end packet delivery.
  • Validated the scheme's resilience and scalability in simulated post-disaster smart-city environments.

Conclusions:

  • The WAY-CR scheme offers a resilient, scalable, and secure solution for drone-assisted emergency response networks in smart cities.
  • The proposed methods effectively address challenges posed by disaster-induced communication disruptions.
  • WAY-CR enhances network performance critical for post-disaster coordination and recovery efforts.