ABSTRACT. Wireless networked control system is gaining momentum in industrial cyber-physical systems, e.g., smart factory. Suffering from limited bandwidth and nondeterministic link quality, a critical challenge in its deployment is how to optimize the closed-loop control system performance as well as maintain stability. In order to bridge the gap between network design and control system performance, we propose an optimal dynamic scheduling strategy that optimizes performance of multi-loop control systems by allocating network resources based on predictions of both link quality and control performance at run-time. The optimal dynamic scheduling strategy boils down to solving a nonlinear integer programming problem, which is further relaxed to a linear programming problem. The proposed strategy provably renders the closed-loop system meansquare stable under mild assumptions. Its efficacy is demonstrated by simulating a four-loop control system over an IEEE 802.15.4 wireless network simulator – TOSSIM. Simulation results show that the optimal dynamic scheduling can enhance control system performance and adapt to both constant and variable network background noises as well as physical disturbance.

ABSTRACT. Design of wireless control systems has been extensively studied, which is one of the fundamental issues in cyber-physical systems. In this paper, we empirically investigate heterogeneous sampling rate assignment with a testbed when multiple physical systems are controlled through an IEEE 802.11 network. Among the critical design variables in wireless control systems, we focus on the sampling rates because they are always key control knobs regardless of network protocols. There has been little experimental research on heterogeneous sampling rate optimization for IEEE 802.11 wireless control systems, where the sampling rates of each control loop may have different values. We first formulate the co-design problem in an optimization framework with respect to the heterogeneous sampling rates by explicitly taking into account the relations of the sampling rates with the control cost, network energy consumption, and network delay. We further relax the problem as convex optimization, which is provably solved in polynomial time. Our empirical study ensures that the approximate solution is tightly close to the original optimum. To validate the proposed optimization framework, we build a disk-levitation tube testbed, which wirelessly controls the height of 20 disks at the same time. Our empirical study confirms that our optimization formulation is highly effective in practice.

ABSTRACT. Closing feedback loops fast and over long distances is key to emerging applications; for example, robot motion control and swarm coordination require update intervals of tens of milliseconds. Low-power wireless is preferred for its low cost, small form factor, and flexibility, especially if the devices support multi-hop communication. Thus far, however, feedback control over multi-hop low-power wireless has only been demonstrated for update intervals on the order of seconds. This paper presents a wireless embedded system that tames imperfections impairing control performance such as jitter or message losses, and a control design that exploits the essential properties of this system to provably guarantee closed-loop stability for linear physical processes. Using experiments on a cyber-physical testbed with 20 wireless nodes and multiple cart-pole systems, we are the first to demonstrate and evaluate feedback control and coordination over multi-hop low-power wireless for update intervals of 20 to 50 milliseconds.

ABSTRACT. Age-of-Information (AoI) is a recently introduced metric for network operation with sensor applications which quantifies the freshness of data. In the context of networked control systems (NCSs), we compare the worth of data freshness to the value of information (VoI) for reducing the uncertainty in stochastic processes. First, we show that the uncertainty propagates non-linearly over time depending on system dynamics. Next, we define the value of a new update of the process of interest as a function of AoI and system parameters. Additionally, we use it as a metric for centralized scheduling problem in a network of multiple heterogeneous control loops. By conducting a simulative analysis, we show that prioritizing transmissions with higher VoI decreases the total uncertainty compared to providing fair data freshness to all sub-systems equally.

ABSTRACT. In this paper, we propose a technique that attempts to control energy consumption in distributed cyber-physical systems (CPS) in order to improve the level of security of the system. This is in contrast to most existing methods, where the system is set to use a certain level of authentication at design time, such as basic authentication, certificate-based authentication, or no authentication at all. To this end, we propose a notion of authenticatable task graph, which encodes standard task dependencies and allows for authentication tasks to be intermittently inserted into the computation task graph. The optimization objective here is to maximize the number of authentication tasks as well as peer-authentication, while remaining in the system energy bounds. We propose three offline optimization techniques and one online algorithm, where the system can dynamically manage the tradeoff between energy consumption and the level of security in the presence of uncertainties imposed by the physical environment. Our optimization algorithms are validated by a rich set of simulations as well as a real-world case study on a group of unmanned aerial vehicles (UAVs) that are assigned area search tasks and are required to perform peer-authentication within their battery limits.

ABSTRACT. We consider the problem of implementing a Linear Quadratic Gaussian (LQG) controller on a distributed system, while maintaining the privacy of the measurements, state estimates, control inputs and system model. The component sub-systems and actuator outsource the LQG computation to a cloud controller and encrypt their signals and matrices. The encryption scheme used is Labeled Homomorphic Encryption, which supports the evaluation of degree-2 polynomials on encrypted data, by attaching a unique label to each piece of data and using the fact that the outsourced computation is known by the actuator. We write the state estimate update and control computation as multivariate polynomials in the encrypted data and propose an extension to the Labeled Homomorphic Encryption scheme that achieves the evaluation of low-degree polynomials on encrypted data, with degree larger than two. We showcase the numerical results of the proposed protocol for a temperature control application that indicates competitive online times.

ABSTRACT. In this paper, we introduce method of two-way coding, a concept originating in communication theory characterizing coding schemes for two-way channels, into (networked) feedback control systems under injection attacks. We first show that the presence of two-way coding can distort the perspective of the attacker on the control system. In general, the distorted viewpoint on the attacker side as a consequence of two-way coding will facilitate detecting the attacks, or restricting what the attacker can do, or even correcting the attack effect. In the particular case of zero-dynamics attacks, it is seen that the attacks will be detected if designed according to the original plant, while the attack effect may be corrected in steady state if the attacks are to be designed with respect to the equivalent plant as viewed by the attacker.

ABSTRACT. Instrumenting water distribution networks with sensors for monitoring is critical to maintain adequate levels of water quality and quantity. Existing efforts to detect and localize adverse events in the network have explored either installing in-situ sensors on junctions or deploying a number of mobile sensors through pipes. These approaches have high costs, low sensing accuracy, lack sufficient coverage or provide intermittent monitoring. In this paper, we combine the benefits of in-situ and mobile sensing with various geosocial factors to develop a cost-effective hybrid monitoring architecture that minimizes the impact of adverse water events on the community. The architecture can adaptively adjust sensing resolutions on-demand within the network, determine required sensing capabilities based on the event, and respond to varying event severities. We propose a two-phase planning and deployment approach that first integrates network structure, event, and community information with simulation based analytics to determine locations to install in-situ sensors and mobile sensor insertion infrastructure. We then incorporate network flow information to determine mobile sensor deployment locations and volume to quickly localize detected events to minimize their impact. We evaluate our approach using multiple real-world water networks for adverse water quality and loss events and compare it to existing approaches. Our results show that our proposed approach can achieve upto 79% reduction in impact with upto 68% greater cost efficiency compared to approaches using traditional coverage heuristics, and upto 30% reduction in impact while being upto 52% more cost efficient compared to approaches that attempt to minimize impact.

ABSTRACT. Computer modeling and simulation for medical device evaluation has been pursued as a means for reducing the cost and scope of clinical trials while minimizing the risk of unforeseen adverse outcomes. Advances in computational technologies and algorithms have further enabled analysis with complex and realistic Bayesian models to be applied to clinical trials. However, a major challenge is quantifying the degree of uncertainty in simulation outcomes as well as clearly communicating simulation assumptions and parameters such that the results can be considered as regulatory-grade evidence.

In this work, we formulate the Computer-Aided Clinical Trial (CACT) within a Bayesian statistical framework allowing explicit modeling of assumptions and utilization of simulation at all stages of a clinical trial. To quantify the robustness of the simulated endpoints of a CACT with respect to an assumption, we derive δ-robustness as the minimum perturbation of the base prior distribution before the outcome of the CACT changes. The CACT framework and estimation of δ-robustness is applied to the Rhythm ID Goes Head-to-head Trial (RIGHT) which is a comparative evaluation of the safety and efficacy of specific software algorithms across different implantable cardiac devices. We demonstrate the utility of the framework and how the results of δ-robustness evaluation can be utilized at various stages of a clinical trial. Finally, we introduce a hardware interface that allows for direct interaction with the physical device in order validate and confirm the results of CACT.

ABSTRACT. This paper presents a methodology to detect an object with an accelerometer potentially among many other moving objects in a camera scene. By matching sensor readings from a wearable accelerometer with analogous readings from a single camera or plurality of cameras, we detect instances of the same physical movement that both modalities capture. This has a wide range of potential applications in the cyber-physical systems domain such as localization, identification, and detecting context for activity recognition. We present an approach to project data from camera frames into accelerometer frames, where they share the same physical representation, allowing for comparing and determining similarities between the two modalities by using computational algorithms in the cyber world. This is challenging as depth is unknown when using a single 2D camera. We translate camera measurements into the acceleration physical domain and acquire an estimated depth, when the depth is not varying significantly during the motion. We model this translation as an optimization problem to find the optimal depth that maximizes the similarity between readings of the camera and accelerometer. Additionally, we discuss a potential solution with multiple cameras that works for arbitrary varying depth motions. Experimental results demonstrate that the system can detect matching between data stemming from physical movements observed by a wearable accelerometer and a single camera or plurality of cameras.

ABSTRACT. The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Moreover, any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We create a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches that aim to learn a policy over the entire state-space of the problem. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. Finally, we compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which shows that our approach results in a reduction in response time of responders with a drastic reduction in computational time.