Abstract
Correct functioning of cyberphysical systems is of critical importance. This is more so in the case of safety critical systems such as in medical or automotive applications. Since verification of correctness, in general, is infeasible and testing is not exhaustive, it is of critical importance to monitor such system during their operation and detect erroneous behaviors to be acted on.
The problem of designing monitors for safety critical cyberphysical systems is challenging since the correctness property to be monitored is specified on the evolution of system state over time which the monitor cannot directly observe; furthermore, the evolution of the system is probabilistic. The probabilities or randomness in the evolution of the system is due to uncertainties introduced by noise or due to other unpredictable events, such as component failures, modeled probabilistically. The inputs to the monitor are the outputs generated by the system. These may include some sensor outputs. By using these inputs the monitor needs to decide whether the system execution is correct or not.
The project has so far introduced two models for specifying the semantics of such cyberphysical sys tems. The first model is the Hidden Markov System in which the states of the system are modeled as discrete states after quantization. For such systems the property to be monitored is specified by an automaton on in finite strings. We defined two accuracy measures of a given monitor  acceptance and rejection accuracies. The accuracies capture percentage of false alarms and missed alarms, respectively. Using these concepts we defined two notions, called strong monitorability and monitorability. We gave exact characterizations when a system is strongly monitorable and monitorable with respect to a property. Based on these notions we developed techniques for monitoring, when the system to be monitored is specified by a probabilistic hybrid automaton and the property to be monitored is given by a deterministic hybrid automaton. We formulated a monitoring method that uses product automaton and estimates probabilities using particle filters. These monitoring techniques are implemented using Matlab and have been shown to be effective on examples.
We have also introduced Extended Hidden Markov Systems (EHMS) in which the state of the system is hybrid, i.e., has a continuous and discrete component. We extended the monitorability results to the EHMSes.
In order to implement monitors suggested by our theoretical analysis, a state estimation algorithm needs to be used. Even if in each discrete mode the cyberphysical system is linear, mode changes (discrete transitions) introduce nonlinearities. Furthermore, systems we consider have a large number of discrete modes, making traditional modeestimation algorithms difficult to use. In our work we use particle filters to estimate the state of the system; the estimate is in turn used by the monitor. Due to a large number of discrete modes that the particle filter needs to detect, particle depletion represents a particular challenge. Traditionally, depletion calls for increasing number of particles to be used, increasing the computational cost of the particle filter. We use the invariances in the topology of the system stemming from asynchronous transitions to simplify the particle filter and thus dramatically reduce its computational time.
References

A. Sistla, M. ZVefran, and Y. Feng. Monitorability of stochastic dynamical systems. In Computer Aided Verification, pages 720736. Springer, 2011

A. Sistla, M. ZVefran, and Y. Feng. Runtime monitoring of stochastic cyberphysical systems with hybrid state. In Runtime Verification, pages 276293. Springer, 2012
Award ID: 1035914