James Weimer

Scheduling smart home appliances using mixed integer linear programming

This paper considers the minimum electricity cost scheduling problem of smart home appliances. Operation characteristics, such as expected duration and peak power consumption of the smart appliances, can be adjusted through a power profile signal. The optimal power profile signal minimizes cost, while satisfying technical operation constraints and consumer preferences. Constraints such as enforcing uninterruptible and sequential operations are modeled in the proposed framework using mixed integer linear programming (MILP). Several realistic scenarios based on actual spot price are considered, and the numerical results provide insight into tariff design. Computational issues and extensions of the proposed scheduling framework are also discussed.

Robustness of Attack-Resilient State Estimators

The interaction between information technology and phys ical world makes Cyber-Physical Systems (CPS) vulnerable to malicious attacks beyond the standard cyber attacks. This has motivated the need for attack-resilient state estimation. Yet, the existing state-estimators are based on the non-realistic assumption that the exact system model is known. Consequently, in this work we present a method for state estimation in presence of attacks, for systems with noise and modeling errors. When the the estimated states are used by a state-based feedback controller, we show that the attacker cannot destabilize the system by exploiting the difference between the model used for the state estimation and the real physical dynamics of the system. Furthermore, we describe how implementation issues such as jitter, latency and synchronization errors can be mapped into parameters of the state estimation procedure that describe modeling errors, and provide a bound on the state-estimation error caused by modeling errors. This enables mapping control performance requirements into real-time (i.e., timing related) specifications imposed on the underlying platform. Finally, we illustrate and experimentally evaluate this approach on an unmanned ground vehicle case-study.

Reachability for linear hybrid automata using iterative relaxation abstraction

This paper introduces iterative relaxation abstraction (IRA), a new method for reachability analysis of LHA that aims to improve scalability by combining the capabilities of current tools for analysis of low-dimensional LHA with the power of linear programming (LP) for large numbers of constraints and variables. IRA is inspired by the success of counterexample guided abstraction refinement (CEGAR) techniques in verification of discrete systems. On each iteration, a low-dimensional LHA called a relaxation abstraction is constructed using a subset of the continuous variables from the original LHA. Hybrid system reachability analysis then generates a regular language called the discrete path abstraction containing all possible counterexamples (paths to the bad locations) in the relaxation abstraction. If the discrete path abstraction is non-empty, a particular counterexample is selected and LP infeasibility analysis determines if the counterexample is spurious using the constraints along the path from the original high-dimensional LHA. If the counterexample is spurious, LP techniques identify an irreducible infeasible subset (IIS) of constraints from which the set of continuous variables is selected for the the construction of the next relaxation abstraction. IRA stops if the discrete path abstraction is empty or a legitimate counterexample is found. The effectiveness of the approach is illustrated with an example.

Distributed Event-Triggered Estimation in Networked Systems

Abstract The continuous and discrete state estimation problem in linear switched systems with unknown inputs and unstable internal dynamics is addressed. A robust observer based on High-Order Sliding-Mode is proposed to solve the problem under mild structural conditions. Simulation results support the proposed estimation approach.

Distributed detection and isolation of topology attacks in power networks

This paper addresses the issue of detecting and isolating topology attacks in power networks. A topology attack, unlike a data attack and power injection attack, alters the physical dynamics of the power network by removing bus interconnections. These attacks can manifest as both cyber and physical attacks. A physical topology attack occurs when a bus interconnection is physically broken, while a cyber topology attack occurs when incorrect information about the network topology is transmitted to the system estimator and incorporated as the truth. To detect topology attacks, a stochastic hypothesis testing problem is considered assuming noisy measurements are obtained by periodically sampling a dynamic process described by the networked swing equation dynamics, modified to assume stochastic power injections. A centralized approach to network topology detection and isolation is introduced as a two-part scheme consisting of topology detection followed by topology isolation, assuming a topology attack exists. To address the complexity issues arising with performing centralized detection in large-scale power networks, a decentralized approach is presented that uses only local measurements to detect the presence of a topology attack. Simulation results illustrate that both the centralized and decentralized approaches accurately detect and isolate topology attacks.

Multiple Source Detection and Localization in Advection-Diffusion Processes Using Wireless Sensor Networks

This paper concerns the use of large-scale wireless sensor networks to detect and locate leaks of specified gases in the presence of time-varying advection (air currents) and diffusion. We show that when leaks are rare but constant for long periods, Kalman filtering combined with binary hypothesis testing provides an effective alternative to full-scale hypothesis testing covering all possible combinations of leaks and leak intensities. To reduce energy consumption and use of communication bandwidth, a two-tiered strategy is proposed in which a reduced number of sensors (Tier 1) provides coarse-grid sensing. When a leak is detected by the Tier 1 strategy, fine-grained grids of sensors (Tier 2) are activated around the vicinities of the detected leak areas to provide more precise detection and localization. Energy consumption is further reduced by applying an information versus energy-based dynamic sensor selection technique. Details of a laboratory implementation are presented and simulation results illustrate the approach and demonstrate its effectiveness.

A Relaxation Approach to Dynamic Sensor Selection in Large-Scale Wireless Networks

Wireless sensor networks (WSNs) require more complex sensor selection strategies than other distributed networks to perform optimal state estimation. In addition to constraints associated with distributed state estimation, wireless sensor networks have limitations on bandwidth, energy consumption, and transmission range. This paper introduces and empirically evaluates a dynamic sensor selection strategy. A discrete-time Kalman filter is used for state estimation. At each time step, a subset of sensors is selected to gather data on the following time step because of power and bandwidth constraints that prohibit using all of the sensors. A standard criterion for selecting this subset of sensors is to maximize the information to be gained by minimizing a function of the next-step error covariance matrix. We propose a relaxation of this non-convex combinatorial optimization problem and demonstrate its applicability to large-scale sensor networks. The proposed dynamic sensor selection strategy is compared empirically to other dynamic and static sensor selection strategies with respect to state estimation performance of a convection-dispersion field arising from the problem of surface-based monitoring of CO2 sequestration sites.

Active actuator fault detection and diagnostics in HVAC systems

This paper introduces a new method for performing actuator fault detection and diagnostics (FDD) in heating ventilation and air conditioning (HVAC) systems. The proposed actuator FDD strategy, for testing whether an actuator is stuck in a single position, uses a two-tier approach that includes a dynamic model-based detector and a fast-deciding steady-state detector. The model-based detector is formulated to provide detection performance that asymptotically bounds both the probability of miss and probability of false alarm. To provide a quick confirmation the actuator is working, the steady-state detector utilizes a goodness-of-fit detection strategy to decide if the measurements could be described by an actuator failure. An architecture is introduced that requires multiple steady-state detection experiments to decide that the measurements could be explained by an actuator failure before performing model-based detection. An experimental test bed using a the KTH Royal Institute of Technology campus HVAC system is described and used to evaluate the steady-state and model-based detectors. The experimental test bed is utilized to identify a building dynamics model, that is employed through monte carlo analysis, to characterize the detection performance of both the model-based detector and the steady-state detector.

Sensor attack detection in the presence of transient faults

This paper addresses the problem of detection and identification of sensor attacks in the presence of transient faults. We consider a system with multiple sensors measuring the same physical variable, where some sensors might be under attack and provide malicious values. We consider a setup, in which each sensor provides the controller with an interval of possible values for the true value. While approaches exist for detecting malicious sensor attacks, they are conservative in that they treat attacks and faults in the same way, thus neglecting the fact that sensors may provide faulty measurements at times due to temporary disturbances (e.g., a tunnel for GPS). To address this problem, we propose a transient fault model for each sensor and an algorithm designed to detect and identify attacks in the presence of transient faults. The fault model consists of three aspects: the size of the sensors interval (1) and an upper bound on the number of errors (2) allowed in a given window size (3). Given such a model for each sensor, the algorithm uses pairwise inconsistencies between sensors to detect and identify attacks. In addition to the algorithm, we provide a framework for selecting a fault model for each sensor based on training data. Finally, we validate the algorithms performance on real measurement data obtained from an unmanned ground vehicle.

Attack resilient state estimation for autonomous robotic systems

In this paper we present a methodology to control ground robots under malicious attack on sensors. Within the term attack we intend any malicious disturbance injection on sensors, actuators, and controller that would compromise the safety of a robot. In order to guarantee resilience against attacks, we use a control-level technique implemented within a recursive algorithm that takes advantage of redundancy in the information received by the controller. We use the case study of a vehicle cruise-control, however, the strategy we present in this work is general for several applications. Our methodology relays on redundancy in the sensor measurements: specifically we consider N velocity measurements and use a recursive filtering technique that estimates the state of the system while being resilient against sensor attacks by acting on the variance of the measurements noise. Finally, we move our focus on hardware validation demonstrating our algorithm through extensive outdoor experiments conducted on two unmanned ground robots.