A New Classification of Clustering-based for Different Problems in Different Wireless Ad-hoc Networks

TL;DR

This paper reviews and compares clustering-based strategies across various wireless ad-hoc networks, highlighting their strengths, weaknesses, and future challenges in coverage and environment adaptability.

Contribution

It provides a comprehensive classification and analysis of clustering methods tailored for different wireless ad-hoc network types, addressing coverage in deterministic and uncertain environments.

Findings

Clustering strategies vary significantly across network types.

Identifies key challenges and limitations in current clustering models.

Suggests future directions for improving coverage and adaptability.

Abstract

Ad-hoc networks are specifically designed to facilitate communication in environments where establishing a dedicated network infrastructure is exceedingly complex or impractical. The integration of clustering concepts into various ad-hoc network scenarios, including Wireless Sensor Networks (WSN), Mobile Ad-hoc Networks (MANET), Vehicular Ad-hoc Networks (VANET), Delay-Tolerant Networks (DTN), Wireless Ad-hoc Networks (WANET), Underwater Wireless Sensor Networks (UWSN), Unmanned Aerial Vehicle Networks (UAV), commonly known as "drones," Space Networks (SN), and Satellite Networks (TN), presents abundant opportunities for refining strategies in event tracking and area monitoring across both deterministic and uncertain environments. This paper conducts a comparative analysis of diverse proposed strategies leveraging clustering concepts to address coverage challenges within deterministic and uncertain environments. As a result, it addresses current and future challenges inherent in clustering-based WANET, elucidating the merits, shortcomings, and weaknesses of clustering models. Lastly, it identifies potential avenues for addressing coverage issues in existing and emerging technologies.