Recent Advances in IoT based Human Resources Balanced Allocation Method Based on Recalling-Enhanced Salp Swarm Recurrent Neural Network: A Systematic Review

Abstract

The rapid integration of Internet of Things (IoT) technologies into organizational ecosystems has significantly transformed human resource management practices, particularly in workforce monitoring, task allocation, and performance optimization. This study presents a systematic review of recent advances in IoT-based human resources balanced allocation methods, with a specific focus on recalling-enhanced salp swarm recurrent neural networks. The proposed paradigm combines the adaptive optimization capability of the salp swarm algorithm with the temporal learning strength of recurrent neural networks, enhanced through memory recall mechanisms to improve predictive accuracy and allocation efficiency. The review evaluates current methodologies, architectural innovations, and optimization strategies employed in recent literature, highlighting their effectiveness in handling dynamic workforce environments and real-time decision-making. Furthermore, the study identifies key challenges such as scalability, data privacy, model interpretability, and computational complexity, which remain critical barriers to practical deployment. Comparative insights are drawn across multiple studies to assess performance improvements in terms of accuracy, resource utilization, and response time. The findings demonstrate that recalling-enhanced hybrid models significantly outperform traditional approaches by enabling adaptive learning and efficient resource distribution in IoT-driven environments. This systematic review aims to provide a comprehensive understanding of emerging trends, technological advancements, and future research directions in intelligent human resource allocation systems.