Institut of Distributed Systems

Dependable operation of a power system requires fast-acting system reserves provided by flexibility resources that can follow a reference signal in an accurate, timely, and dependable manner. In energy cells, an incom- ing reference signal is received by the control unit of the energy cell. The reference signal is then disaggregated to setpoints for individual flexibility resources. These individual setpoints are sent via a communication network to the flexibility resources, which realize the requested change in power. Generally, the components on the path from the control unit to the flexibility resources may fail. That is to say, a failure may hinder or halt the operation of the control unit, the communication network, or the individual flexibility resources. From the viewpoint of an energy cell, component failures are faults that should be tolerated. This paper proposes an approach for accurate, timely, and dependable control of an energy cell despite faults. The proposed approach uses a genetic algorithm to estimate the state of failed flexibil- ity resources, combined with either optimal or proportional activation of flexibility resources, and state-machine replication. The results show the performance of the approach on a benchmark grid using realistic reference signals and realistic performance requirements based on the example of automatic frequency restoration reserves. The optimal activation of assets uses less energy to follow the reference signal, whereas the proportional activation of assets reduces the variance in the impact depending on which flexibility resources fail. The state estimation explicitly treats the different ways assets can fail and outperforms a comparable approach based only on estimating fluctuations of power provided by flexibility resources. The state-machine replication is shown to withstand crashes of replicas of the control unit with only small delays in reaction.

V2X communication enhances road safety but is vulnerable to data manipulation attacks that could lead to safety-critical incidents, motivating the use of Misbehavior Detection Systems (MDSs). The evaluation of MDSs typically relies on simulated V2X scenarios and attacks. To enable reproducible evaluations, publicly available datasets containing V2X messages are important. Existing datasets have several limitations, including limited attack diversity and missing training/validation/test sets for machine-learning-based MDSs. Therefore, we introduce VeReMi NextGen, generated using the InTAS traffic scenario and Eclipse MOSAIC. The dataset includes urban and highway scenarios, three driver profiles, 15 attack types, and training/validation/test sets, thereby providing significantly broader coverage than previous datasets. The attacks were designed to be more advanced and harder to detect than those in the predecessor VeReMi Extension, as confirmed by an evaluation using a state-of-the-art MDS. Our contribution includes the dataset and a publicly available dataset generator, enabling easy integration of additional attacks and entities, such as vulnerable road users.

Ensuring the semantic correctness of exchanged kinematic data is critical for safety-critical Vehicle-to-Everything (V2X) applications. While onboard Misbehaviour Detection (MBD) mechanisms help address this issue, their effectiveness is inherently limited by the vehicle’s local view. This work investigates whether lightweight, rule-based MBD can be significantly enhanced through infrastructure support without increasing computational complexity. We adopt a Trust Assessment Framework to control the inclusion of V2X data into the vehicle’s Extended Perception Map (EPM), based on trust levels derived from MBD outputs. We compare a standalone setup relying on local evidence only with a federated setup in which the infrastructure aggregates Misbehaviour Reports from multiple vehicles, assesses node trustworthiness, and disseminates this information back to vehicles. Simulation results under kinematic falsification attacks show that the federated setup consistently outperforms the standalone one in filtering altered observations.

Subjective Logic (SL) trust discounting enables trust transitivity along referral paths and is widely applied in distributed cyber-physical systems and ad-hoc networks. When used for adversarial integrity-focused trust assessment, however, established discounting operators exhibit two systematic effects: (i) short chains with conflicting or jointly negative opinions may produce discounted results dominated by uncertainty rather than disbelief; and (ii) under repeated sequential composition, several operators attenuate committed mass multiplicatively, driving uncertainty toward one as path length increases. These behaviors conflict with weakest-link integrity semantics in which a single compromised node should dominate the trust assessment. This paper introduces disbelief-favouring trust discounting (DF), an SL operator that propagates maximum disbelief along a path and redistributes remaining belief–uncertainty mass proportionally. We analyze the structural mechanism underlying uncertainty accumulation in established operators, formalize design requirements for adversarial multi-hop integrity assessment, and evaluate DF against existing operators using controlled synthetic chain experiments and Monte Carlo simulations with probabilistic compromise and detection. Results show that DF avoids uncertainty convergence and improves block-averaged F1 across required trust levels over increasing chain lengths.