Optimization Approaches for Cluster Formation in Mobile Ad-Hoc Networks: A Comprehensive Survey

  • Authors

    • Yudhveer Kandukuri G2 Web Services - Open-Source DBA & DevOps, USA. Author
    • Sojy John Recreational Equipment, Inc - Technology Lead, DevOps Engineer & Cloud Migration, USA. Author
    • Naveen Shrivastava T-Mobile Salesforce Lead Developer, USA. Author
    • Bose babu Ravula John Wiley & Sons, Inc., Principal, USA. Author
    • Sai Mahesh Marri Amazon Web Services, Senior Electrical Engineer, USA. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2022PI5V8C

    Published 03-05-2022

  • Mobile Ad-Hoc Networks (MANETs), Cluster Formation, Cluster head Selection, Routing Overhead, Scalability, Energy Efficiency

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

  • Abstract

    A new generation of wireless networks, known as mobile ad hoc networks (MANETs), is quickly becoming the backbone of truly autonomous and ever-changing wireless networks.  Important issues with these networks, like as energy efficiency, load balancing, routing stability, and scalability, have been successfully addressed using clustering algorithms. This study looks at cluster-based routing and optimization methods in detail to help with choosing the best cluster head and adapting the clustering. Network longevity, energy consumption, and stability are measured for key mobility-aware, energy-efficient, and social-aware algorithms such as LEACH, PEGADyn, GMCA, AREA, and SoCS. Despite their benefits, existing solutions face limitations such as reliance on simulation-based evaluation, assumptions of moderate network sizes or stable links, and trade-offs between energy, latency, and security. The findings highlight the effectiveness of clustering in reducing routing complexity, improving data aggregation, and enhancing overall network performance. However, existing methods are primarily validated through simulations and often rely on assumptions such as stable connectivity or moderate network sizes, which may limit applicability in large-scale and highly dynamic real-world environments.

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