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A Comprehensive Review of Metaheuristic Algorithms for Node Placement in UAV Communication Networks.

S A Temesheva1, D A Turlykozhayeva1, S N Akhtanov1

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This review explores meta-heuristic algorithms for optimizing Unmanned Aerial Vehicle Communication Network (UAVCN) node placement. It analyzes algorithms to enhance coverage and connectivity while reducing latency and energy use in UAV networks.

Keywords:
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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Wireless Communication

Background:

  • Unmanned Aerial Vehicle Communication Networks (UAVCNs) offer resilient, infrastructure-independent wireless solutions for diverse environments.
  • Optimal placement of Unmanned Aerial Vehicle (UAV) nodes is crucial for network performance but is an NP-hard problem.
  • Existing reviews often overlook UAV-specific node placement challenges, focusing on terrestrial networks.

Purpose of the Study:

  • To provide a comprehensive review of meta-heuristic algorithms (MHAs) for UAVCN node placement.
  • To systematically analyze the strengths, weaknesses, and future research directions of these algorithms.
  • To offer practical insights for selecting effective strategies in various UAVCN deployment scenarios.

Main Methods:

  • Systematic literature review of MHAs applied to UAVCN node placement.
  • Critical analysis of algorithmic performance, including coverage, connectivity, latency, and energy efficiency.
  • Evaluation of selected hybrid algorithms using a Python framework with computational time and coverage metrics.

Main Results:

  • Identified a gap in comprehensive reviews focused on UAVCN node placement using MHAs.
  • Analyzed the trade-offs and applicability of various MHAs for optimizing UAV network performance.
  • Demonstrated the practical effectiveness of hybrid algorithms in achieving multi-objective optimization.

Conclusions:

  • MHAs are essential for efficiently solving the NP-hard UAVCN node placement problem.
  • Further research is needed to address specific UAVCN challenges and explore novel algorithmic approaches.
  • This review provides valuable guidance for researchers and practitioners in UAVCN design and optimization.