In wireless communications, trustworthiness computation has emerged as a crucial aspect of safeguarding modern systems against cybersecurity threats, ensuring reliable data transmission and upholding user trust. However, there is no unified definition of trustworthiness computation in the literature, and it is often presented as a specifically tailored adaptation of attack detection mechanisms. In contrast, this work introduces a general method for trustworthiness computation in wireless networks. It leverages key system characteristics, such as the channel, timing, and packet information to identify measurable Quality of Service (QoS) features with sufficient sensitivity across varying operational conditions. Building on these features, a novel three-step approach is applied. It employs changepoint detection to identify potential trustworthiness issues, calculates indicators based on the observed features, and finally combines them into a quantitative representation of trustworthiness. This systematic method effectively distinguishes between regular statistical variations in QoS features and actual trustworthiness issues. The applicability of the presented approach is demonstrated using a typical IEEE 802.11 wireless link, where different QoS features and scenarios are defined. These scenarios include network attacks, system malfunctions, and typical operational conditions. Our trustworthiness computation method correctly alerts the system to all trustworthiness issues that we challenge it with.

Intelligent behavior is an emergent phenomenon observed in biological organisms across all scales. It describes the cooperative behavior of low complexity entities to accomplish complex tasks, which exceed their individual capabilities. This property is particularly important for the Internet of Bio-Nano Things (IoBNT), which consists of Bio-Nano Things (BNTs) used in the human body, where they face many restrictions, such as bio-compatibility and size constraints. In this paper, we present a novel BNT-architecture, called Molecular Nano Neural Networks (M3N), which allows the implementation of intelligence on the micro-/nano-scale. The proposed structure consists of compartments (low complexity entities) that are connected to each other to form a network. Based on reaction and diffusion of molecules in and between connected compartments, this network mimics an artificial neural network, which is an important step towards artificial intelligence in the IoBNT. We provide design guidelines for the proposed M3N and successfully validate it by applying a regression and classification task.

6G networks will play a crucial role in Industry 4.0 advancing further smart manufacturing and trustworthy communication. This paper introduces a reinforcement learning scheduler that considers environmental knowledge designed to improve reliability and security aspects of trustworthiness by favoring to serve nodes when in line-of-sight. We evaluate the performance in a factory floor scenario for two different heights of remote-radio heads and various blockage densities. The results show that the proposed method outperforms traditional schedulers such as round robin and proportional fair in terms of reliability, availability and fairness enhancing thus reliability and security aspects of trustworthiness when the environment is prone to a mixture of line-of-sight and non-line-of-sight.

We present a trustworthiness score for double-sided two-way ranging on ultra-wideband devices. It serves as quantitative measure for assessing the reliability and security aspects of ranging. Leveraging the trustworthiness score, we propose two localization schemes that utilize the weighted non-linear least squares approach. The computation of the trustworthiness score relies on an autoencoder model trained on trustworthy data. To evaluate the performance of our trustworthiness score, we conduct indoor localization experiments that include channel manipulations such as line-of-sight obstructions and timestamp attacks resembling the well-known Cicada attack. The incorporation of trustworthiness led to an improvement in localization accuracy, reducing the root mean square error (RMSE) by up to 50% in the dynamic scenario.

Abstract Spatial modulation (SM) is one of the probable candidates to be utilised in the fifth generation of wireless networks due to its power and spectral efficiencies. Since only one active transmit antenna exists in spatial modulation, inter‐channel interferences are avoided and the number of radio frequency (RF) chains is reduced. However, channel estimation is a major challenge in spatial modulation communication systems. In this study, a novel blind signal and channel estimation method for spatial modulation‐based multi‐input multi‐output communications, based on the well‐known Expectation‐Maximisation algorithm, is presented. This blind detector requires only a few pilot symbols at the beginning of the transmission to resolve the inherent phase and permutation ambiguities. Simulation results demonstrate that the proposed detector performs very similar to the optimum detector with full channel state information and outperforms existing blind detectors.