AI-driven cluster-based routing protocols in WSNs: A survey of fuzzy heuristics, metaheuristics, and machine learning models

Abstract

Cluster-based routing techniques have become a key solution for managing data flow in Wireless Sensor Networks (WSNs), which often struggle with limited resources and dynamic network conditions. With the growing need for efficient data management in these networks, it is more important than ever to understand and enhance these techniques. This survey evaluates recent cluster-based routing protocols released from 2021 to 2024, focusing on the AI-driven approaches in WSNs including fuzzy heuristics, metaheuristics, and machine learning models, along with their combinations. Each approach is evaluated through a deep analysis of solution-based and network configuration-based factors. Solution-based parameters include performance mode, selection strategies, optimization objectives, modeling techniques, and key factors affecting the overall effectiveness of each approach. Additionally, network configuration analysis deals with the type of topology, communication architecture, network scale, performance metrics, and simulators used. This comprehensive analysis unveils valuable insights into the capabilities and limitations of each method. By identifying shortcomings and highlighting areas for improvement, this survey aims to guide future research towards the development of more efficient cluster-based routing techniques for WSNs. These methods, incorporating intelligent performance characteristics, will be well-equipped to address the ever-growing demands of the intelligent era.