Federated EGSV-AACO for Decentralized Spectrum Sensing and Sharing in IoT Networks
Abstract
Wireless bandwidth is in greater demand than ever before due to the Internet of Things' (IoT) applications' rapid expansion in fields including smart cities, autonomous and Industry 4.0. Traditional fixed spectrum allocation approaches can lead to inefficient utilization and excessive interference levels, particularly in densely populated areas. The purpose of this evaluation is to create an intelligent, decentralized, and privacy-preserving framework for optimizing spectrum detection and sharing among IoT devices utilizing machine learning (ML) techniques. The Cognitive Radio Networks (CRNs) Dataset is gathered from the Kaggle source. The procedure consists of four sequential steps. Each IoT node uses Extreme Gradient Support Vector with Adaptive Ant Colony Optimization (EGSV-AACO) to monitor spectrum occupancy and identify idle bands. Each node builds a local spectrum access model based on temporal spectrum patterns. Model weights are delivered to a nearby edge server on a regular basis to avoid exposing raw data using Federated Averaging (FedAvg). The server aggregates the locally trained models to form a global model and redistributes it to all participating devices. This updated global model will drive real-time, collision-free spectrum allocation among IoT devices. A smart campus simulation using MATLAB shows that the proposed EGSV-AACO framework ensures access convergence, improves spectrum usage, and prevents raw data leakage. The developed model outperforms all baseline methods and achieved an accuracy of 97%, precision of 97.5%, recall of 96%, and an F1-score of 96.5%. Overall, this research introduces a novel Federated EGSV-AACO framework that significantly enhances decentralized, privacy-preserving, and intelligent spectrum sensing and sharing in IoT networks.References
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