Network Slicing Optimization for Heterogeneous IoT Applications in Next-Generation Wireless Networks

Next-generation wireless communication systems have emerged as a significant means of supporting heterogeneous Internet of Things (IoT) applications that have different Quality of Service (QoS) requirements. Some issues, such as dynamic resource allocation, heterogeneous traffic handling, and adaptive orchestration, have been observed in existing approaches to network slicing. This work aims to develop an intelligent next-generation network slicing optimization framework capable of enhancing resource utilization, QoS provisioning, and adaptive slice orchestration through the integration of stochastic traffic modeling. The proposed framework uses a simulated 5G/6G network slicing dataset with heterogeneous IoT traffic. Initially, massive machine-type communication (mMTC), ultra-reliable low-latency communications (URLLC), and enhanced mobile broadband (eMBB) traffic are generated and monitored to collect network states. The collected data are normalized and processed using a deep autoencoder to extract meaningful features. These features are then used by Multi-Agent Proximal Policy Optimization (MAPPO) to optimize bandwidth, CPU, and memory allocation across slices, while Software-Defined Networking (SDN) and Network Function Virtualization (NFV) enable dynamic slice orchestration and adaptive resource management. Experimental results demonstrate that the proposed framework improves throughput by 20.9%, reduces latency by 49.1%, improves reliability by 3.4%, reduces packet loss by 69.2%, increases resource utilization by 9.6%, and improves SLA compliance by 19.4% compared with the baseline static resource allocation approach. Consequently, the proposed framework offers a robust and adaptive network slicing solution for next-generation IoT ecosystems.