Information Diffusion Models and Fuzzing Algorithms for a Privacy-Aware Data Transmission Scheduling in 6G Heterogeneous ad hoc Networks

In this paper, we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks. This system enables end nodes to select the optimum time and scheme to transmit private data safely. In 6G dynamic heterogeneous infrastructures, unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy. Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service (QoS). As the transport network is built of ad hoc nodes, there is no guarantee about their trustworthiness or behavior, and transversal functionalities are delegated to the extreme nodes. However, while security can be guaranteed in extreme-to-extreme solutions, privacy cannot, as all intermediate nodes still have to handle the data packets they are transporting. Besides, traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models. The proposed scheme fulfills this gap. Findings show the probability of a successful intelligent attack reduces by up to 65% compared to ad hoc networks with no privacy protection strategy when used the proposed technology. While congestion probability can remain below 0.001%, as required in 6G services.