Improved unsupervised coloring algorithm for spectrum allocation in multiple wireless body area networks

As electromagnetic waves fade rapidly when passing through the human body, interferences may significantly degrade communication over wireless body area networks (WBANs). However, due to the limited power and resource restrictions in WBAN devices, existing interference mitigation methods may not fully address this issue. In this study, considering the architecture and equipment capacity of WBANs, the clustering mechanism is improved using unsupervised machine learning during the coloring process. Accordingly, a novel unsupervised coloring algorithm (UCA) for spectrum allocation in multiple WBANs is presented in this paper. The nodes in the region are separated by clustering, and the channel group is assigned to the cells by coloring to achieve UCA. We simulated the performance of UCA in different scenarios with different node densities for both indoor and outdoor environments. The experimental results showed that the proposed UCA achieved convergence results with accurate and inaccurate positioning. It outperformed the existing methods by exhibiting less time complexity, lower interference strength, and faster algorithm convergence. Moreover, the proposed UCA was robust against topology changes.