Fog computing significantly enhances the efficiency of IoT applications by providing computation, storage, and networking resources at the edge of the network. In this paper, we propose a federated fog computing framework designed to optimize resource management, minimize latency, and reduce energy consumption across distributed IoT environments. Our framework incorporates predictive scheduling, energy-aware resource allocation, and adaptive mobility management strategies. Experimental results obtained from extensive simulations using the OMNeT++ environment demonstrate that our federated approach outperforms traditional non-federated architectures in terms of resource utilization, latency, energy efficiency, task execution time, and scalability. These findings underline the suitability and effectiveness of the proposed framework for supporting sustainable and high-performance IoT services.

翻译：雾计算通过在网络边缘提供计算、存储和网络资源，显著提升了物联网应用的效率。本文提出一种联邦雾计算框架，旨在优化分布式物联网环境中的资源管理、最小化延迟并降低能耗。该框架融合了预测性调度、能量感知资源分配和自适应移动性管理策略。在OMNeT++环境中进行广泛仿真所获得的实验结果表明，我们的联邦方法在资源利用率、延迟、能效、任务执行时间和可扩展性方面均优于传统的非联邦架构。这些发现凸显了所提框架在支持可持续高性能物联网服务方面的适用性与有效性。