Harnessing Machine Learning for Intelligent Load Balancing in Wireless Sensor Networks: A Review of Strategies, Challenges, and Future Directions

The widespread importance of Wireless Sensor Networks (WSNs) in modern communication is clear. Nevertheless, issues like keeping them energy-efficient, preventing network overload, and guaranteeing accurate data collection remain significant hurdles, frequently arising from imbalances in how tasks are spread across the network. This paper delves into the current landscape of strategies employing machine learning (ML) to achieve balanced loads and, consequently, optimize WSN performance. Our central aim is to scrutinize the ways in which ML paradigms can alleviate the problems associated with uneven resource utilization, which detrimentally impacts both the operational effectiveness and the longevity of these networks. This review undertakes a qualitative exploration of recent scholarly works, concentrating on the application of supervised, unsupervised, and reinforcement learning algorithms to the management of load within WSNs. The findings of this analysis suggest that solutions driven by machine learning offer notable improvements in load distribution, decreased delays in communication, and enhanced energy usage when contrasted with conventional methodologies. Through a comparative assessment, this study pinpoints significant trends, existing shortcomings, and potential avenues for future investigation. In conclusion, the integration of intelligent load balancing techniques powered by ML enhances the ability of WSNs to adapt to changing conditions and to scale effectively. This paper contributes to the body of knowledge by providing a thorough synthesis of how machine learning is being used to optimize WSNs and by outlining a potential path for the creation of more resourceful and intelligent network systems.