MCVAE: A New Multi-Conditional Approach for Scalable Emergency Ad Hoc Network Design and Node Localization Using Generative Variational Autoencoder

In disaster scenarios where traditional communication infrastructure is unavailable or damaged, the rapid deployment of efficient ad hoc wireless networks is critical. These networks must be adaptable, self-organizing, and capable of maintaining reliable communication links under uncertain and dynamic environmental conditions. However, obtaining optimal ad hoc network design and accurate node localization remains a challenging task, especially in disaster scenarios where time is critical. This work addresses these issues by introducing a novel Multi-Conditioning Variational Autoencoder (MCVAE) model for generating optimized ad hoc node localization conditioned on multiple factors, such as network area size, node count, and success criteria. The proposed generative model not only addresses the localization problem but also enables on-demand generation of emergency ad hoc networks, making it a practical solution for real-time deployments in disaster-stricken areas. Furthermore, a synthetic dataset incorporating realistic features that effectively describe the ad hoc node distribution environment was created. The MCVAE model was trained on this dataset to learn complex dependencies between input conditions and network design. Experimental results demonstrate that the model effectively generalizes beyond the training area sizes, accurately generating node layouts for previously unseen dimensions with efficient distribution and low localization error (RMSE 0.125 for a network size of 450 m2 with 285 nodes) and an R2 score of 0.9999. Additionally, the proposed methodology achieves excellent scalability and adaptability for generative ad hoc networks, allowing users to specify different area dimensions and automatically receive efficient node distributions with minimal localization error. The findings highlight the model's potential for real-time, data-driven deployment of resilient ad hoc networks in disaster-stricken or infrastructure-limited regions. This is the first work to contribute a practical and scalable use of a generative AI model that can significantly enhance wireless communication readiness in emergency situations.