Showing papers in "ACM Transactions on Cyber-Physical Systems in 2017"

Adaptive Real-Time Communication for Wireless Cyber-Physical Systems

Abstract: Low-power wireless technology promises greater flexibility and lower costs in cyber-physical systems. To reap these benefits, communication protocols must deliver packets reliably within real-time deadlines across resource-constrained devices, while adapting to changes in application requirements (e.g., traffic demands) and network state (e.g., link qualities). Existing protocols do not solve all these challenges simultaneously, because their operation is either localized or a function of network state, which changes unpredictably over time. By contrast, this article claims a global approach that does not use network state information as input can overcome these limitations. The Blink protocol proves this claim by providing hard guarantees on end-to-end deadlines of received packets in multi-hop low-power wireless networks, while seamlessly handling changes in application requirements and network state. We build Blink on the non-real-time Low-Power Wireless Bus (LWB) and design new scheduling algorithms based on the earliest-deadline-first policy. Using a dedicated priority queue data structure, we demonstrate a viable implementation of our algorithms on resource-constrained devices. Experiments show that Blink (i) meets all deadlines of received packets, (ii) delivers 99.97% of packets on a 94-node testbed, (iii) minimizes communication energy consumption within the limits of the underlying LWB, (iv) supports end-to-end deadlines of 100ms across four hops and nine sources, and (v) runs up to 4.1 × faster than a conventional scheduler implementation on popular microcontrollers.

Confidentiality Breach Through Acoustic Side-Channel in Cyber-Physical Additive Manufacturing Systems

Abstract: In cyber-physical systems, due to the tight integration of the computational, communication, and physical components, most of the information in the cyber-domain manifests in terms of physical actions (such as motion, temperature change, etc.). This leads to the system being prone to physical-to-cyber domain attacks that affect the confidentiality. Physical actions are governed by energy flows, which may be observed. Some of these observable energy flows unintentionally leak information about the cyber-domain and hence are known as the side-channels. Side-channels such as acoustic, thermal, and power allow attackers to acquire the information without actually leveraging the vulnerability of the algorithms implemented in the system. As a case study, we have taken cyber-physical additive manufacturing systems (fused deposition modeling-based three-dimensional (3D) printer) to demonstrate how the acoustic side-channel can be used to breach the confidentiality of the system. In 3D printers, geometry, process, and machine information are the intellectual properties, which are stored in the cyber domain (G-code). We have designed an attack model that consists of digital signal processing, machine-learning algorithms, and context-based post processing to steal the intellectual property in the form of geometry details by reconstructing the G-code and thus the test objects. We have successfully reconstructed various test objects with an average axis prediction accuracy of 86% and an average length prediction error of 11.11%.

Demand Response and Smart Buildings: A Survey of Control, Communication, and Cyber-Physical Security

Abstract: In this article, we perform a comprehensive survey of the technical aspects related to the implementation of demand response and smart buildings. Specifically, we discuss various smart loads such as heating, ventilating, and air-conditioning (HVAC) systems and plug-in electric vehicles (PEVs); the power architecture with multibus characteristics; different control algorithms such as the hybrid centralized and decentralized control and the distributed coordination among buildings; the communication technologies and network architectures; and the potential cyber-physical security issues and possible mechanisms for enhancing the system security at both cyber and physical layers. The current status of the demand response in United States, Europe, Japan, and China is reviewed, and the benefits, costs, and challenges of implementing and operating demand response and smart buildings are also discussed.

Towards Cyber-Physical Systems Design for Structural Health Monitoring: Hurdles and Opportunities

Abstract: Large civil structures, such as bridges, buildings, and aerospace vehicles form the backbone of our society are critical to some catastrophic events such as damage. Wired sensor networks are usually adopted for structural health monitoring (SHM) applications. This is also an important Smart City application. Recent wireless sensor networks (WSNs) technology promises the eventual ability to cover such a structure and continuously monitor its health. However, researchers from both engineering and computer science domains face numerous hurdles, such as application-specific requirements, in reaching this goal. These hurdles have a cumulative effect on severely resource-constrained WSNs. This article provides a comprehensive investigation of WSN-based SHM applications with an emphasis on networking perspectives to get insights into a cyber-physical system (CPS) design. First, we provide the SHM philosophy and conduct extensive comparative studies regarding various aspects of benefits and hurdles of going wireless for SHM. Second, we propose a taxonomy of SHM techniques and their applicability to WSNs. Third, we show a transition from the WSN-based SHM towards the CPS design, expecting that such a design will mitigate WSN resource constraints and satisfy SHM application-specific requirements to a great extent. For each of these, we discuss a surge of existing schemes with an emphasis on limitations of the state-of-the-art, and we point out open issues. Finally, we propose a series of design guidelines for a potential CPS. This article will help both engineering and computer science domain researchers/engineers and respective communities in designing future CPS to ensure the economic benefit and public safety in functioning civil structures.

A Cloud-Based Black-Box Solar Predictor for Smart Homes

Abstract: The popularity of rooftop solar for homes is rapidly growing. However, accurately forecasting solar generation is critical to fully exploiting the benefits of locally generated solar energy. In this article, we present two machine-learning techniques to predict solar power from publicly available weather forecasts. We use these techniques to develop SolarCast, a cloud-based web service that automatically generates models that provide customized site-specific predictions of solar generation. SolarCast utilizes a “black box” approach that requires only (1) a site’s geographic location and (2) a minimal amount of historical generation data. Since we intend SolarCast for small rooftop deployments, it does not require detailed site- and panel-specific information, which owners may not know, but instead automatically learns these parameters for each site.We evaluate the accuracy of SolarCast’s different algorithms on two publicly available datasets, each containing over 100 rooftop deployments with a variety of attributes (e.g., climate, tilt, orientation, etc.). We show that SolarCast learns a more accurate model using much less data (∼1 month) than prior SVM-based approaches, which require ∼3 months of data. SolarCast also provides a programmatic API, enabling developers to integrate its predictions into energy efficiency applications. Finally, we present two case studies of using SolarCast to demonstrate how real-world applications can leverage its predictions. We first evaluate a “sunny” load scheduler, which schedules a dryer’s energy usage to maximally align with a home’s solar generation. We then evaluate a smart solar-powered charging station, which can optimally charge the maximum number of electric vehicles (EVs) on a given day. Our results indicate that a representative home is capable of reducing its grid demand up to 40% by providing a modest amount of flexibility (of ∼5 hours) in the dryer’s start time with opportunistic load scheduling. Further, our charging station uses SolarCast to provide EV owners the amount of energy they can expect to receive from solar energy sources.

A Case Study on Improving Capacity Delivery of Battery Packs via Reconfiguration

Abstract: Cell imbalance in large battery packs degrades their capacity delivery, especially for cells connected in series where the weakest cell dominates their overall capacity. In this article, we present a case study of exploiting system reconfigurations to mitigate the cell imbalance in battery packs. Specifically, instead of using all the cells in a battery pack to support the load, selectively skipping cells to be discharged may actually enhance the pack’s capacity delivery. Based on this observation, we propose CSR, a Cell Skipping-assisted Reconfiguration algorithm that identifies the system configuration with (near)-optimal capacity delivery. We evaluate CSR using large-scale emulation based on empirically collected discharge traces of 40 lithium-ion cells. CSR achieves close-to-optimal capacity delivery when the cell imbalance in the battery pack is low and improves the capacity delivery by about 20% and up to 1x in the case of a high imbalance.

Strengthening Adaptation in Cyber-Physical Systems via Meta-Adaptation Strategies

Abstract: The dynamic nature of complex Cyber-Physical Systems puts extra requirements on their functionalities: they not only need to be dependable, but also able to adapt to changing situations in their environment. When developing such systems, however, it is often impossible to explicitly design for all potential situations up front and provide corresponding strategies. Situations that come out of this “envelope of adaptability” can lead to problems that end up by applying an emergency fail-safe strategy to avoid complete system failure. The existing approaches to self-adaptation cannot typically cope with such situations better—while they are adaptive (and can apply learning) in choosing a strategy, they still rely on a pre-defined set of strategies not flexible enough to deal with those situations adequately. To alleviate this problem, we propose the concept of meta-adaptation strategies, which extends the limits of adaptability of a system by constructing new strategies at runtime to reflect the changes in the environment. Though the approach is generally applicable to most approaches to self-adaptation, we demonstrate our approach on IRM-SA—a design method and associated runtime model for self-adaptive distributed systems based on component ensembles. We exemplify the meta-adaptation strategies concept by providing three concrete meta-adaptation strategies and show its feasibility on an emergency coordination case study.

Security of Cyber-Physical Systems in the Presence of Transient Sensor Faults

Abstract: This article is concerned with the security of modern Cyber-Physical Systems in the presence of transient sensor faults. We consider a system with multiple sensors measuring the same physical variable, where each sensor provides an interval with all possible values of the true state. We note that some sensors might output faulty readings and others may be controlled by a malicious attacker. Differing from previous works, in this article, we aim to distinguish between faults and attacks and develop an attack detection algorithm for the latter only. To do this, we note that there are two kinds of faults—transient and permanent; the former are benign and short-lived, whereas the latter may have dangerous consequences on system performance. We argue that sensors have an underlying transient fault model that quantifies the amount of time in which transient faults can occur. In addition, we provide a framework for developing such a model if it is not provided by manufacturers.Attacks can manifest as either transient or permanent faults depending on the attacker’s goal. We provide different techniques for handling each kind. For the former, we analyze the worst-case performance of sensor fusion over time given each sensor’s transient fault model and develop a filtered fusion interval that is guaranteed to contain the true value and is bounded in size. To deal with attacks that do not comply with sensors’ transient fault models, we propose a sound attack detection algorithm based on pairwise inconsistencies between sensor measurements. Finally, we provide a real-data case study on an unmanned ground vehicle to evaluate the various aspects of this article.

Event-Based Mobility Modeling and Analysis

Abstract: Mobility is a critical issue that must be considered during the modeling and analyzing of a mobile system. At a high abstract level, event-based models can directly specify a mobile system without the introduction of additional mechanisms. In this article, we first propose two types of special events, entering and exiting an ambient, as movement events. Next, based on the movement events, we introduce the notion of a movement path and propose a feasible movement criterion (deciding whether a given movement path of a mobile object (agent) is feasible or not in terms of spatiotemporal topological relationships of ambients). Then, we investigate how a message movement--based communication model represents synchronous communication, asynchronous communication, and broadcast communication in a unified way. Finally, we use movement event sequences to discuss the exclusivity of ambients (an ambient only allows one mobile object to occupy (enter) it at any moment) and show that a priority scheduling control policy can guarantee exclusivity. Accordingly, we propose a correct movement criterion—that is, a correct movement path is feasible and satisfies the exclusivity of ambients. Case studies demonstrate these results.

Adaptive Real-Time Scheduling of Cyber-Physical Energy Systems

Abstract: This article addresses the application of real-time scheduling to the reduction of the peak load of power consumption generated by electric loads in Cyber-Physical Energy Systems (CPES). The goal is to reduce the peak load while achieving a desired Quality of Service of the physical system under control. The considered physical processes are characterized by integrator dynamics and modelled as sporadic real-time activities. Timing constraints are obtained from physical parameters and are used to manage the activation of electric loads by a real-time scheduling algorithm. As a main contribution, an algorithm derived from the multi-processor real-time scheduling domain is proposed to efficiently deal with a high number of physical processes (i.e., electric loads), making its scalability suitable for large CPES, such as smart energy grids. The cyber-physical nature of the proposed method arises from the tight interaction between the physical processes operated by the electric loads, and the applied scheduling.To allow the use of the proposed approach in practical applications, modelling approximations and uncertainties on physical parameters are explicitly included in the model. An adaptive control strategy is proposed to guarantee the requirements on physical values under control in presence of modelling and measurement uncertainties. The compensation for such uncertainties is done by dynamically adapting the values of timing parameters used by the scheduler. Formal results have been derived to put into relationship the values of quantities describing the physical process with real-time parameters used to model and to schedule the activation of loads. The performance of the method is evaluated by means of physically accurate simulations of thermal systems, showing a remarkable reduction of the peak load and a robust enforcement of the desired physical requirements.

Enabling Distributed Energy Storage by Incentivizing Small Load Shifts

Abstract: Reducing peak demands and achieving a high penetration of renewable energy sources are important goals in achieving a smarter grid. To reduce peak demand, utilities are introducing variable rate electricity prices to incentivize consumers to manually shift their demand to low-price periods. Consumers may also use energy storage to automatically shift their demand by storing energy during low-price periods for use during high-price periods. Unfortunately, variable rate pricing provides only a weak incentive for distributed energy storage and does not promote its adoption at large scales. In this article, we present the storage adoption dilemma to capture the problems with incentivizing energy storage using variable rate prices. To address the problem, we propose a simple pricing scheme, called flat-power pricing, which incentivizes consumers to shift small amounts of load to flatten their demand rather than shift as much of their power usage as possible to low-price, off-peak periods. We show that compared to variable rate pricing, flat-power pricing (i) reduces consumers’ upfront capital costs, as it requires significantly less storage capacity per consumer; (ii) increases energy storage’s return on investment, as it mitigates free riding and maintains the incentive to use energy storage at large scales; and (iii) uses aggregate storage capacity within 31% of an optimal centralized approach. In addition, unlike variable rate pricing, we also show that flat-power pricing incentivizes the scheduling of elastic background loads, such as air conditioners and heaters, to reduce peak demand. We evaluate our approach using real smart meter data from 14,000 homes in a small town.

QoS-Driven Data Processing Algorithms for Smart Electric Grids

Abstract: Smart-grid applications have widely varying data needs as well as bandwidth and latency requirements. The usual approach to accumulating the available data (e.g., from Phasor Measurement Units) at a centralized site and executing all the applications there leads to large network latencies. This article proposes techniques where data packets are prioritized and disseminated based on applications’ data needs and semantics. In particular, these techniques systematically exploit in-network processing capability and filter data in the dissemination network. This filtered data is assigned higher priority compared to the raw unfiltered data—helping meet QoS requirements of various applications. Performance evaluation of our distributed techniques over a testbed designed for the Indian electric grid demonstrates that processing latency for various smart grid applications reduces by at least 50% for large PMU data sizes, compared to the traditional centralized approach.

Collaborative Load Management with Safety Assurance in Smart Grids

Abstract: Load shedding can combat the overload of a power grid that may jeopardize the grid’s safety. However, disconnected customers may be excessively inconvenienced or even endangered. With the emergence of demand-response based on cyber-enabled smart meters and appliances, customers may participate in solving the overload by curtailing their demands collaboratively such that no single customers will have to bear a disproportionate burden of reduced usage. However, compliance or commitment to curtailment requests by untrusted users is uncertain, which causes an important safety concern. This article proposes a two-phase load management scheme that (i) gives customers a chance to curtail their demands and correct a grid’s overload when there are no immediate safety concerns but (ii) falls back to load shedding to ensure safety once the grid enters a vulnerable state. Extensive simulations based on a 37-bus electrical grid and traces of real electrical load demonstrate the effectiveness of this scheme. In particular, if customers are, as expected, sufficiently committed to the load curtailment, overloads can be resolved in real time by collaborative and graceful usage degradation among them, thereby avoiding unpleasant load shedding.

On Exploiting Structured Human Interactions to Enhance Sensing Accuracy in Cyber-physical Systems

Abstract: In this article, we describe a general methodology for enhancing sensing accuracy in cyber-physical systems that involve structured human interactions in noisy physical environment. We define structured human interactions as domain-specific workflow. A novel workflow-aware sensing model is proposed to jointly correct unreliable sensor data and keep track of states in a workflow. We also propose a new inference algorithm to handle cases with partially known states and objects as supervision. Our model is evaluated with extensive simulations. As a concrete application, we develop a novel log service called Emergency Transcriber, which can automatically document operational procedures followed by teams of first responders in emergency response scenarios. Evaluation shows that our system has significant improvement over commercial off-the-shelf (COTS) sensors and keeps track of workflow states with high accuracy in noisy physical environment.

Optimal Control of PEVs with a Charging Aggregator Considering Regulation Service Provisioning

Abstract: Plug-in electric vehicles (PEVs) are considered the key to reducing fossil fuel consumption and an important part of the smart grid. The plug-in electric vehicle-to-grid (V2G) technology in the smart grid infrastructure enables energy flow from PEV batteries to the power grid so that the grid stability is enhanced and the peak power demand is shaped. PEV owners will also benefit from V2G technology, as they will be able to reduce energy cost through proper PEV charging and discharging scheduling. Moreover, power regulation service (RS) reserves have been playing an increasingly important role in modern power markets. It has been shown that by providing RS reserves, the power grid achieves a better match between energy supply and demand in presence of volatile and intermittent renewable energy generation. This article starts with the problem of PEV charging under dynamic energy pricing, properly taking into account the degradation of battery state-of-health (SoH) during V2G operations as well as RS provisioning. An overall optimization throughout the whole parking period is proposed for the PEV and an adaptive control framework is presented to dynamically update the optimal charging/discharging decision at each hour to mitigate the effect of RS tracking error.As more and more PEVs are being plugged into the power grid, the control or management issue of PEV charging arises, since mass unregulated charging processes of PEVs may result in degradation of power quality and damage utility equipments and customer appliances. To solve this problem, this article also presents an SoH-aware charging aggregator design, which decides the control sequences of a group of PEVs. An energy storage system is used in the charging aggregator to do a peak power shaving, and future parking PEVs are properly taken care of. Experimental results show that the proposed optimal charging algorithm minimizes the combination of electricity cost and battery aging cost in the RS provisioning power market. Experimental results also show that the introduction of charging aggregator can significantly reduce the peak power consumption caused by simultaneous PEV charging.

Data Analytics for Managing Power in Commercial Buildings

Abstract: Commercial buildings are significant consumers of electricity. We propose a number of methods for managing power in commercial buildings. The first step toward better energy management in commercial buildings is monitoring consumption. However, instrumenting every electrical panel in a large commercial building is an expensive proposition. In this article, we demonstrate that it is also unnecessary. Specifically, we propose a greedy meter (sensor) placement algorithm based on maximization of information gain subject to a cost constraint. The algorithm provides a near-optimal solution guarantee, and our empirical results demonstrate a 15% improvement in prediction power over conventional methods. Next, to identify power-saving opportunities, we use an unsupervised anomaly detection technique based on a low-dimensional embedding. Furthermore, to enable a building manager to effectively plan for demand response programs, we evaluate several solutions for fine-grained, short-term load forecasting. Our investigation reveals that support vector regression and an ensemble model work best overall. Finally, to better manage resources such as lighting and HVAC, we propose a semisupervised approach combining hidden Markov models (HMMs) and a standard classifier to model occupancy based on readily available port-level network statistics. We show that the proposed two-step approach simplifies the occupancy model while achieving good accuracy. The experimental results demonstrate an average occupancy estimation error of 9.3% with a potential reduction of 9.5% in lighting load using our occupancy models.