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RETRACTED: Ndaguba et al. Operability of Smart Spaces in Urban Environments: A Systematic Review on Enhancing Functionality and User Experience. <i>Sensors</i> 2023, <i>23</i>, 6938.

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Updated: Aug 10, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Multi-Objective Message Routing in Electric and Flying Vehicles Using a Genetics Algorithm.

Muhammad Alolaiwy1, Mohamed Zohdy1

  • 1Electrical and Computer Engineering Department, Oakland University, Rochester, MI 48309, USA.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary

Electric and flying vehicles (EnFVs) require advanced communication protocols. A novel modified genetics algorithm optimizes routing for EnFVs, improving packet delivery and energy efficiency.

Keywords:
EnFVsUAVselectric vehiclesgenetics algorithmmulti-objective optimization

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Area of Science:

  • Intelligent Transportation Systems
  • Wireless Communication Networks
  • Optimization Algorithms

Background:

  • The proliferation of electric vehicles (EVs) and unmanned aerial vehicles (UAVs) necessitates intelligent communication protocols for seamless integration into transportation systems.
  • Current communication strategies face challenges in efficiently routing messages between these diverse battery-powered devices.
  • A unified communication paradigm for electric and flying vehicles (EnFVs) is crucial for future intelligent transportation.

Purpose of the Study:

  • To address the challenges in message routing for a unified paradigm of electric and flying vehicles (EnFVs).
  • To propose a novel multi-objective optimization scheme for EnFVs message routing.
  • To enhance communication reliability, data rate, and energy efficiency in EnFV networks.

Main Methods:

  • Development of a novel modified genetics algorithm for multi-objective EnFVs message routing.
  • Identification of all possible routing solutions and Pareto-front delineation.
  • Integration of vehicle reliability, data rate, and residual energy as key routing parameters.

Main Results:

  • The proposed scheme significantly outperforms existing routing solutions in a New York City geographical trace simulation.
  • Achieved over 90% packet delivery ratio, indicating high communication success rates.
  • Demonstrated longer connectivity times, shortest average hop distances, and efficient energy consumption.

Conclusions:

  • The modified genetics algorithm provides an effective solution for multi-objective message routing in EnFV networks.
  • The proposed routing scheme enhances overall communication performance and energy efficiency for integrated electric and flying vehicles.
  • This approach lays the groundwork for more robust and efficient intelligent transportation systems.