Teleoperation serves as the fallback solution to autonomous driving but reliable functions of the teleoperation require a certain amount of mobile network resources, which cannot be guaranteed at all times. Therefore, predictive quality of service (pQoS) is introduced as a concept to increase the resilience of the teleoperation. In this paper, based on a data measurement campaign, we propose a prediction framework to prediction two important network KPIs of teleoperation: uplink data-rate and round-trip latency. Furthermore, we introduce a method to alleviate the performance degradation of machine-learning-based prediction models on previously unseen data due to concept drift by incorporating historic data into the prediction pipeline. Additionally, we introduce the metric of critical scenario detection to evaluate the prediction performance specifically for teleoperation.

Mobile wireless sensor networks (MWSNs) have blended into a new application scenario to create revolutionary intelligent cities and handle numerous challenges of security, adaptability, and robustness as wireless technology has advanced. Exploiting lightweight cryptosystems is considered one of the main approaches for MWSNs to achieve confidentiality, integrity, and authentication to prevent malicious cyberattacks and resource abuses. The Saturnin lightweight cryptosystem, presented at ToSC in 2020, can be applied to protect MWSNs. No literature suggests Saturnin can defend against a ciphertext-only attack, where the attackers are the most susceptible. This study proposes the novel statistical differential fault analysis (SDFA) in this attack scenario with double distinguishers of square Chi-maximum likelihood estimate and Dice-Hamming weight. Following the experiments, it recovered the 256-bit secret key using 656 faults in the fourth-to-last round of Saturnin. Compared to the classic statistical fault analysis (SFA), the novel SDFA can expand fault injections to the deeper round and decrease the faults by half with a reliability of at least 99%. It gives an essential reference for figuring out how secure lightweight cryptosystems in MWSNs are.