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The objective of this research is to develop new scientific and engineering principles, algorithms and models for the design of battery powered cyber-physical systems whose computational substrates include high-performance multiprocessor systems-on-chip.
Dependable and secure automotive cyber-physical systems (CPSs) are crucial as human’s lives are dependent on them. Many important subsystems in today’s automobiles such as the engine control system and the anti-brake system are hard real-time systems. If the CPUs in those systems have any fault, regardless of transient faults or hard faults, not only the computation results may be wrong, but also the results may be delivered late. Therefore, CPUs used in those systems must be able to handle two tasks: 1) detect and correct the errors, and 2) ensure that the error detection and correction can be done within the deadline so that the system can function correctly or have a grace period.
This research aims to introduce methods to analyze the robustness of battery supported cyber physical systems under co-designed control, scheduling, and battery management algorithms. Robustness refers to the ability to maintain system performance under perturbations. Robustness in controller design has been well defined and understood for a large class of feedback control systems, yet robustness of scheduling and battery management algorithms is relatively less understood.
ABSTRACT: This paper addresses the problem of resilient consensus in the presence of misbehaving nodes. Although it is typical to assume knowledge of at least some nonlocal information when studying secure and fault-tolerant consensus algorithms, this assumption is not suitable for large-scale dynamic networks. To remedy this, we emphasize the use of local strategies to deal with resilience to security breaches.
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