A Comprehensive Review of IoT based Human Resources Balanced Allocation Method Based on Recalling-Enhanced Salp Swarm Recurrent Neural Network

Abstract

The rapid evolution of Internet of Things (IoT) technologies has significantly transformed human resource management by enabling real-time monitoring, data-driven decision-making, and intelligent workforce optimization. However, achieving balanced human resource allocation in dynamic and heterogeneous environments remains a critical challenge. This paper presents a comprehensive review of IoT-based human resource allocation methods with a particular focus on recalling-enhanced Salp Swarm Recurrent Neural Network (RE-SSRNN) approaches. The integration of IoT data streams with advanced deep learning architectures and swarm intelligence optimization techniques provides a promising framework for addressing workforce imbalance, task scheduling inefficiencies, and stress prediction in employees. The recalling-enhanced mechanism improves temporal learning capabilities of recurrent neural networks by capturing long-term dependencies, while the Salp Swarm Algorithm contributes to global optimization and convergence efficiency. This review systematically explores existing methodologies, highlighting their strengths, limitations, and applicability in real-world HR scenarios. Furthermore, the paper discusses how hybrid intelligent models can improve allocation fairness, operational efficiency, and employee well-being. The findings suggest that combining IoT-enabled data acquisition with adaptive neural and optimization frameworks offers a scalable and robust solution for next-generation human resource systems. This work serves as a foundational reference for researchers and practitioners aiming to design intelligent HR allocation systems.