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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog

Md Muzakkir Hussain1, Ahmad Taher Azar2,3, Rafeeq Ahmed4

  • 1Department of Computer Science and Engineering, SRM University, Amaravati 522502, India.

Sensors (Basel, Switzerland)
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

Vehicular Fog Computing (VFC) addresses challenges in vehicular applications by offloading computation. A new SONG algorithm optimizes VFC network design for reduced service delay and energy consumption.

Keywords:
Genetic AlgorithmSDG11SDG12SDG7SDG9intelligent transportation systemsvehicular ad hoc networkvehicular fog computing

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Area of Science:

  • * Vehicular Fog Computing (VFC) and intelligent transportation systems.
  • * Network optimization and facility location problems.
  • * Evolutionary computation and multi-objective optimization.

Background:

  • * Traditional cloud computing is infeasible for delay- and energy-critical vehicular applications.
  • * Vehicular Fog Computing (VFC) offers a solution by utilizing roadside units (RSUs) and vehicles as fog nodes.
  • * Capacity planning and dimensioning of VFC networks are complex due to dynamic traffic and mobile nodes.

Purpose of the Study:

  • * To propose a multi-objective optimization model for facility location in VFC networks.
  • * To achieve an optimal trade-off between service delay and energy consumption.
  • * To introduce a hybrid Evolutionary Multi-Objective (EMO) algorithm, SONG, for solving the VFC optimization problem.

Main Methods:

  • * Development of a multi-objective optimization model for VFC network topology.
  • * Hybridization of Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO) into the SONG algorithm.
  • * Evaluation of the SONG algorithm using real-world vehicular traces and quality indicators (HV, IGD, CPU delay gap).

Main Results:

  • * The SONG algorithm effectively illustrates delay-energy solution frontiers and optimal VFC topologies.
  • * Empirical results demonstrate superior performance of SONG compared to NSGA-II and SMPSO.
  • * SONG shows improved solution quality in terms of Hyper-Volume, Inverted Generational Distance, and CPU delay.

Conclusions:

  • * The proposed SONG algorithm is a viable tool for optimizing VFC network design.
  • * SONG facilitates efficient capacity planning and dimensioning of VFC networks.
  • * Service providers can leverage SONG for designing delay- and energy-aware vehicular communication systems.