Xerox Corporation

Raman Goyal

Shantanu Rane

Christoforos Somarakis

Erfaun Noorani

Aleksandar B Feldman

David Eric Schwartz

Saman Mostafavi

Muhammad Adil

Subhashis Hazarika

One embodiment can provide a method and system for estimating a remote quantity of interest (QoI). During operation, the system can receive, over a communication channel, a radio frequency (RF) signal carrying an estimate of the QoI measured by a sensor. The system can estimate probability distributions of a set of random channel parameters associated with the HF communication channel. The system can further reconstruct the estimate based on the probability distributions of the channel parameters and the received RF signal, determine a level of uncertainty associated with the reconstructed estimate, and combine reconstructed estimates from multiple sensors based on the determined level of uncertainty associated with each reconstructed estimate to output a combined estimate of the QoI.

System and method for estimating errors in a sensor network implementing high frequency (HF) communication channels

A system and a method for secure control of a physical system are described. During operation, the system can obtain measurement of one or more sensors associated with the physical system. The system then estimates a state of the physical system based on the measurement of the sensors. Subsequently, the system generates a feedback control signal based on the estimated state and generates a watermarking signal based on a stored estimated state of the physical system at a previous time instant. The system then generates a watermarked control signal by combining the feedback control signal and the watermarking signal and applies the watermarked control signal to the physical system to regulate the state of the physical system, thereby facilitating secure control of the physical system.

System and method for security control in cyber-physical systems with delay-induced feedback watermarking

A physics model of a building is linearized around an operating point. Measurements received from sensors define a system state of the HVAC system. The linearized physics model is used as an equality constraint for a model predictive controller that determines a next control input to the HVAC system based on the system state by solving an optimization problem for a time horizon of size N. A constraint matrix H of the equality constraint is decomposed into factors of U and V matrices such that UUand VV are both diagonal matrices. An objective function of the model predictive controller is optimized by iteratively solving a linear system of equations that includes inverses of UUand VV to determining a sequence of inputs for a horizon of the model predictive controller. A first input of the sequence of inputs to is used to control the HVAC system.

Method and system for scalable embedded model predictive control of HVAC systems

Embodiments described herein provide a design architecture for co-designing a controller and a watermarking signal for a cyber-physical system. During operation, the architecture can determine, in conjunction with each other, respective values of a first set of parameters indicating operations of the controller and a second set of parameters representing the watermarking signal. Here, the watermarking signal is combinable with a control signal from the controller for monitoring an output signal of the cyber-physical system for detecting malicious data at different time instances. Subsequently, the architecture can determine a state manager for determining the states of the cyber-physical system from the monitored output signal based on the first and second sets of parameters. The architecture can also determine a detector capable of identifying presence of an attack from the states of the cyber-physical system at a plurality of time instances using the watermarking signal.

Method and architecture for providing integrated design of cyber-physical system with watermarking

Embodiments described herein provide a supervisor for fault management at a production system. During operation, the supervisor can obtain a set of sensor readings and a state of the production system. A respective sensor reading is an output of a sensor in the production system. The supervisor can then determine, using an artificial intelligence (AI) model, whether the set of sensor readings accommodates a fault associated with a corresponding sensor. Subsequently, the supervisor can determine an action that mitigates an effect of the fault and modify the set of sensor readings based on the action. Here, the modified set of sensor readings is used by a controller that controls the production system.

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Mountain View, CA, United States of America

Raman Goyal