Biblio

Although DC microgrids using a distributed cooperative control architecture can avoid the instability or shutdown issues caused by a single-point failure as compared to the centralized approach, limited global information in the former makes it difficult to detect cyber attacks. Here, we present a false data injection attack (FDIA)–-termed as a local control input attack–-targeting voltage observers in the secondary controllers and control loops in the primary controllers. Such an attack cannot be detected by only observing the performance of the estimated voltage of each agent, thereby posing a potential threat to the system operation. To address this, a detection method using the outputs of the voltage observers is developed to identify the exact location of an FDIA. The proposed approach is based on the characteristics of the distributed cooperative network and avoids heavy dependency on the system model parameters. Next, an event-driven mitigation approach is deployed to substitute the attacked element with a reconstructed signal upon the detection of an attack. Finally, the effectiveness of the proposed resilient scheme is validated using simulation results.

One of the typical autonomous driving systems is a human-machine cooperative system that intervenes in the driver operation. The autonomous driving needs to make consideration of the driver individuality in addition to safety. This paper considers a human-machine cooperative system balancing safety with the driver individuality using the Human-In-The-Loop System (HITLS) for rear-wheel steering control. This paper assumes that it is safe for HITLS to follow the target side-slip angle and target angular velocity without conflicts between the controller and driver operations. We propose HITLS using the primal-dual algorithm and the internal model control (IMC) type I-PD controller. In HITLS, the signal expander delimits the human-selectable operating range and the controller cooperates stably the human operation and automated control in that range. The primal-dual algorithm realizes the driver and the signal expander. Our outcomes are the making of the rear-wheel steering system which converges to the target value while reflecting the driver individuality.

A human-swarm cooperative system, which mixes multiple robots and a human supervisor to form a mission team, has been widely used for emergent scenarios such as criminal tracking and victim assistance. These scenarios are related to human safety and require a robot team to quickly transit from the current undergoing task into the new emergent task. This sudden mission change brings difficulty in robot motion adjustment and increases the risk of performance degradation of the swarm. Trust in human-human collaboration reflects a general expectation of the collaboration; based on the trust humans mutually adjust their behaviors for better teamwork. Inspired by this, in this research, a trust-aware reflective control (Trust-R), was developed for a robot swarm to understand the collaborative mission and calibrate its motions accordingly for better emergency response. Typical emergent tasks “transit between area inspection tasks”, “response to emergent target - car accident” in social security with eight fault-related situations were designed to simulate robot deployments. A human user study with 50 volunteers was conducted to model trust and assess swarm performance. Trust-R's effectiveness in supporting a robot team for emergency response was validated by improved task performance and increased trust scores.

Existing consumer devices represent the most pervasive computational platform available, but their inherently decentralized nature poses significant challenges for distributed computing adoption. In particular, device owners must willingly cooperate in collective deployments even while others may intentionally work to maliciously disrupt that cooperation. Public, cooperative systems benefit from low barriers to entry improving scalability and adoption, but simultaneously increase risk exposure to adversarial threats via promiscuous participant adoption. In this work, I aim to facilitate widespread adoption of cooperative systems by discussing the unique security and operational challenges of these systems, and highlighting several novel approaches that mitigate these disadvantages.

Identity Management systems cannot be centralized anymore. Nowadays, users have multiple accounts, profiles and personal data distributed throughout the web and hosted by different providers. However, the online world is currently divided into identity silos forcing users to deal with repetitive authentication and registration processes and hindering a faster development of large scale e-business. Federation has been proposed as a technology to bridge different trust domains, allowing user identity information to be shared in order to improve usability. But further research is required to shift from the current static model, where manual bilateral agreements must be pre-configured to enable cooperation between unknown parties, to a more dynamic one, where trust relationships are established on demand in a fully automated fashion. This paper presents IdMRep, the first completely decentralized reputation-based mechanism which makes dynamic federation a reality. Initial experiments demonstrate its accuracy as well as an assumable overhead in scenarios with and without malicious nodes.