ACM Transactions on

Cyber-Physical Systems (TCPS)

Latest Articles

Selecting the Transition Speeds of Engine Control Tasks to Optimize the Performance

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

SMT-Based Observer Design for Cyber-Physical Systems under Sensor Attacks

On Learning How Players Learn: Estimation of Learning Dynamics in the Routing Game


CFP: Special Issue on Real-Time aspects in Cyber-Physical Systems
This special issue invites original, high-quality work that report the latest advances in real-time aspects in CPSs. Featured articles should present novel strategies that address real-time issues in different aspects of CPS design and implementation, including theory, system software, middleware, applications, network, tool chains, test beds, and case studies. For more information, visit the Special Issue webpage.

CFP: Special Issue on Transportation Cyber-Physical Systems
The aim of this special issue will be to feature articles on new technologies that will impact future transportation systems. They might span across vehicular technologies – such as autonomous vehicles, vehicle platooning and electric cars, communication technologies to enable vehicle-to-vehicle and vehicle-to-infrastructure communication, security mechanisms, infrastructure-level technologies to support transportation, as well as management systems and policies such as traffic light control, intersection management, dynamic toll pricing and parking management. In addition to terrestrial transportation, traffic control and autonomous management of aerial vehicles and maritime ships are also of interest. For more information, visit the Special Issue webpage.

CFP: Special Issue on Dependability in Cyber Physical Systems and Applications
This special issue focuses on bringing together current research ideas and techniques from researchers and practitioners belonging to a myriad of research areas, with the final goal of sharing their specific challenges and solutions for CPS dependability. More specifically, contributions related to dependability aspects of CPS applications/systems in practice are of interest. For more information, visit the Special Issue webpage.

CFP: Special Issue on Medical Cyber-Physical Systems
This special issue seeks papers describing significant research contributions in the domain of medical cyber-physical systems; each paper should show enough evidence of contributions to medical cyber-physical systems applications and systems in practice. For more information, visit the Special Issue webpage.

CFP: Special Issue on Internet of Things

This special issue focuses on the technical issues we face when designing, engineering, deploying, and maintaining the IoT. We seek high-quality and unpublished papers that push research in all the facets of the IoT. Contributions may present and solve open technical problems, integrate novel solutions efficiently, and focus on the performance evaluation and comparison with existing standards. Both theoretical and experimental studies are welcome. For more information, visit the Special Issue webpage.

CFP: Special Issue on Smart Homes, Buildings, and Infrastructures

The purpose of this special issue is to present the state-of-the-art CPS research for building efficient smart homes, buildings, and infrastructures. The submissions should address the above challenges with a system perspective that includes both cyber and physical aspects, and should articulate how proposed approaches may be applied in practical CPS systems. For more information, visit the Special Issue webpage.

About TCPS

Cyber-Physical Systems (CPS) has emerged as a unifying name for systems where the cyber parts, i.e., the computing and communication parts, and the physical parts are tightly integrated, both at the design time and during operation. Such systems use computations and communication deeply embedded in and interacting with physical processes to add new capabilities to physical systems. These cyber-physical systems range from miniscule (pace makers) to large-scale (a national power-grid). There is an emerging consensus that new methodologies and tools need to be developed to support cyber-physical systems.  READ MORE

Forthcoming Articles

Preface to the Special Issue: Towards an Efficient and Effective Internet of Things for Cyber-Physical Systems (Part I)

Preface to the Special Issue: Towards an Efficient and Effective Internet of Things for Cyber-Physical Systems (Part II)

Towards Battery-free Wearable Devices: The Synergy between Two Feet

Recent years have witnessed the prevalence of wearable devices.Wearable devices are intelligent and multifunctional, but they rely heavily on batteries. This greatly limits their application scope, where replacement of battery or recharging is challenging or inconvenient. We note that wearable devices have the opportunity to harvest energy from human motion, as they are worn by the users as long as being functioning. In this study, we propose a battery-free sensing platform for wearable devices in the form-factor of shoes. It harvests the kinetic energy from walking or running to supply devices with power for sensing, processing and wireless communication, covering all the functionalities of commercial wearable devices. We achieve this goal by enabling the whole system running on the harvested energy from two feet. Each foot performs separate tasks and two feet are coordinated by ambient backscatter communication. We instantiate this idea by building a prototype, containing energy harvesting insoles, power management circuits and ambient backscatter module. Evaluation results demonstrate that the system can wake up shortly after several seconds walk and have sufficient Bluetooth throughput for supporting many applications. We believe that our framework can stir a lot of useful applications that were infeasible previously.

Systematically Ensuring The Confidence of Real Time Home Automation IoT Systems

Recent advances and industry standards in Internet of Things (IoT) have accelerated the real-world adoption of connected devices. To manage this hybrid system of digital real-time devices and analog environments, the industry has pushed several popular home automation IoT (HA-IoT) frameworks, e.g., IFTTT (If-This- Then-That), Apple HomeKit, and Google Brillo. And, users author device interactions by specifying the triggering sensor event and the triggered device command. In this seemly simple software system, two dominant factors govern the system confidence properties with respect to the physical world. First, IoT users are largely non-expert users, who lack the comprehensive consideration regarding potential impact and joint effect with existing rules. Second, while the increasing complexity of IoT devices enables fine-grained control (e.g., heater temperature) on the continuous real time environments, even two simply connected devices can have a huge state space to explore. In fact, bugs that wrongfully control devices and home appliances can have ramifications to system cor- rectness and even user physical safety. It is crucial to help users to make sure the system they created meets their expectation. In this paper, we introduce how techniques from hybrid automata can be practi- cally applied to assist non-expert IoT users in the confidence checking of such hybrid HA-IoT systems. We propose an automated framework for end-to-end programming assistance. We build and check the linear hybrid automata (LHA) model of the system automatically. We also present a quantifier elimination based method to analyze the counterexample found and synthesize the fix suggestions. We implemented a plat- form, MenShen, based on this framework and techniques. We conducted sets of real HA-IoT case studies with up to 46 devices and 65 rules. Empirical results show that MenShen can find violations and generate rule fix suggestions in only 10 seconds.

Cyber-Physical Specification Mismatches

Embedded systems use increasingly complex software and are evolving into cyber-physical systems (CPS) with sophisticated interaction and coupling between physical and computational processes. Many CPS operate in safety-critical environments and have stringent certification, reliability, and correctness requirements. These systems undergo changes throughout their lifetimes, where either the software or physical hardware is updated in subsequent design iterations. One source of failure in safety-critical CPS is when there are unstated assumptions in either the physical or cyber parts of the system, and new components do not match those assumptions. In this work, we present an automated method towards identifying unstated assumptions in CPS. Dynamic specifications in the form of candidate invariants of both the software and physical components are identified using dynamic analysis (executing and/or simulating the system implementation or model thereof). A prototype tool called Hynger (for HYbrid iNvariant GEneratoR) was developed that instruments Simulink/Stateflow (SLSF) model diagrams to generate traces in the input format compatible with the Daikon invariant inference tool, which has been extensively applied to software systems. Hynger, in conjunction with Daikon, is able to detect candidate invariants of several CPS case studies. We use the running example of a DC-to-DC power converter, and demonstrate that Hynger can detect a specification mismatch where a tolerance assumed by the software is violated due to a plant change. Another case study of a powertrain fuel control system is also introduced to illustrate the power of Hynger and Daikon in automatically identifying cyber-physical specification mismatches.

Quantifying the Utility-Privacy Tradeoff in the Internet of Things

The Internet of Things promises many advantages in the control and monitoring of physical systems, from both efficacy and efficiency perspectives. However, in the wrong hands, the data might pose a privacy threat. In this paper, we consider the tradeoff between the operational value of data collected in the IoT and the privacy of consumers. We present a general framework for quantifying this tradeoff in the IoT, and focus on a smart grid application for a proof of concept. In particular, we analyze the tradeoff between smart grid operations and how often data is collected by considering a realistic direct-load control example using thermostatically controlled loads, and we give simulation results to show how its performance degrades as the sampling frequency decreases. Additionally, we introduce a new privacy metric, which we call inferential privacy. This privacy metric assumes a strong adversary model, and provides an upper bound on the adversary's ability to infer a private parameter, independent of the algorithm he uses. Combining these two results allows us to directly consider the tradeoff between better operational performance and consumer privacy.

Dynamic Security Analysis of Power Systems by a Sampling-based Algorithm

Dynamic security analysis is an important problem of power systems on ensuring safe operation and stable power supply even when certain fault occurs. However, the nonlinear hybrid nature, that is, nonlinear continuous dynamics integrated with discrete switching, and the high degree of freedom of the dynamics of power systems make it challenging to conduct the analysis. In this paper, we use the hybrid automaton model to describe the dynamics of a power system, and mainly deal with the index-1 differential-algebraic equations models regarding the continuous dynamics in different discrete states. The analysis problem is formulated as a reachability problem of the associated hybrid model. A sampling-based algorithm is then proposed by integrating with modeling and simulation of the hybrid dynamics to search for a feasible execution connecting an initial state of the post-fault system and a target set in the desired operation mode. The proposed method enables the use of existing power system simulators for the synthesis of discrete switching and control strategies through randomized simulation. The effectiveness and performance of the proposed approach are demonstrated with an application to the dynamic security analysis of the New England 39-bus benchmark power system exhibiting hybrid dynamics.

A Mobile Health System for Neurocognitive Impairment Evaluation based on P300 Detection

A new mobile healthcare solution for neuro-cognitive function monitoring and treatment is presented. The technique is based on spatio-temporal detection and characterization of a specific brain potential, called P300. The diagnosis of cognitive deficit is achieved by analyzing the data collected by the system with a new algorithm called tuned-Residue Iteration Decomposition (t-RIDE). The system has been tested on 12 subjects involved in three different cognitive tasks with increasing difficulty. The system allows fast diagnosis of cognitive deficit, including mild and heavy cognitive impairment: t-RIDE convergence is achieved in 79 iterations (i.e., 1.95s) yielding an 80% accuracy in P300 amplitude evaluation with only 13 trials on a single EEG channel.

A Self-stabilizing Publish/Subscribe Middleware for IoT Applications

This article presents a middleware that provides a communication and data dissemination infrastructure which is suitable for the operation environment of the Internet of Things (IoT). The middleware realizes the channel-based publish/subscribe paradigm that has been identified as a valid means to asynchronously disseminate data in IoT applications. The novelty lies in the routing algorithm PSVR that greatly reduces the path lengths to deliver publications and its suitability for scenarios with a high subscriber fluctuation rate. The middleware is self-stabilizing and eventually provides safety and liveness properties such as the guaranteed delivery of all published messages to all subscribers of the corresponding channel and the cor- rect handling of subscriptions and unsubscriptions, while no error occurs. We consider transient message and memory corruptions and also respect dynamic network changes such as node and link removals and additions. The evaluation of the middleware based on simulations and a real deployment showsthat it has an acceptable memory footprint, scales well with the number of nodes, and has advantages with respect to an existing comparable publish/subscribe system.

Anonymous, Fault-Tolerant Distributed Queries for Smart Devices

Applications that aggregate and query data from distributed client devices are of interest in many settings (smart buildings and cities, the smart power grid, mobile health). However, such devices also pose serious privacy concerns due to the personal nature of the data being collected. In this paper, we present an algorithm for aggregating data in a distributed manner that keeps the data on the devices themselves, releasing only sums and other aggregates to centralized operators. We offer two privacy-preserving configurations of our solution, one limited to crash failures and supporting a basic kind of aggregation; the second supporting a wider range of queries and also tolerating Byzantine behavior by compromised nodes. The former is quite fast and scalable, the latter more robust against attack and capable of offering full differential privacy for an important class of queries, but it costs more and injects noise that makes the query results slightly inaccurate. Other configurations are also possible. At the core of our approach is a new kind of overlay network (a superimposed routing structure operated by the endpoint computers). This overlay is optimally robust and convergent, and our protocols use it both for aggregation and as a general-purpose infrastructure for peer-to-peer communications.

Holistic Cyber-Physical Management for Dependable Wireless Control Systems

Wireless sensor-actuator networks (WSAN) is gaining momentum in industrial process automation as a communication infrastructure for lowering deployment and maintenance costs. In traditional wireless control systems the plant controller and the network manager operates in isolation, which ignores the significant influence of network reliability on plant control performance. To enhance the dependability of industrial wireless control, we propose a holistic cyber-physical management framework that employs run-time coordination between the plant control and network management. Our design includes a holistic controller that generates actuation signals to physical plants and reconfigures the WSAN to maintain desired control performance while saving wireless resources. As a concrete example of holistic control, we design a holistic manager that dynamically reconfigures the number of transmissions in the WSAN based on online observations of physical and cyber variables. We have implemented the holistic management framework in the Wireless Cyber-Physical Simulator (WCPS). A systematic case study has been presented based on two 5-state plants sharing a 16-node WSAN. Simulation results show that the holistic management design has significantly enhanced the resilience of the system against both wireless interferences and physical disturbances, while effectively reducing the number of wireless transmissions.

Tradeoffs in Neuroevolutionary Learning-Based Real-Time Robotic Task Design in the Imprecise Computation Framework

A cyberphysical avatar is a semi-autonomous robot that adjusts to an unstructured environment and performs physical tasks subject to critical timing constraints while under human supervision. The paper first realizes a cyberphysical avatar that integrates three key technologies: body-compliant control, neuroevolution, and real-time constraints. Body-compliant control is essential for operator safety because avatars perform cooperative tasks in close proximity to humans; neuroevolution (NEAT) enables programming avatars such that they can be used by non-experts for a large array of tasks, some unforeseen, in an unstructured environment; and real-time constraints are indispensable to provide predictable, bounded-time response in humanavatar interaction. Then, we present a study on the tradeoffs between three design parameters for robotic task systems which must incorporate at least three dimensions: (1) the amount of training effort for robot to perform the task, (2) the time available to complete the task when the command is given, and (3) the quality of the result of the performed task. A tradeoff study in this design space by using the imprecise computation as a framework is to perform a common robotic task, specifically, grasping of unknown objects. The results were validated with a real robot and contribute to the development of a systematic approach for designing robotic task systems that must function in environments like flexible manufacturing systems of the future.

A Dependable Time Series Analytic Framework for Cyber-Physical Systems of IoT-based Smart Grid

With the emergence of cyber-physical systems (CPS), we are now at the brink of next computing revolution. As one of the foundations for this CPS revolution, IoT (Internet of Things) based Smart Grid (SG) is defined as a power grid integrated with a large network of smart objects. The volume of time series of SG equipments is tremendous and the raw time series are very likely to contain missing values because of undependable network transferring. The problem of storing tremendous volume of raw time series thereby providing a solid support for precise time series analytics is now become tricky. In this paper we propose a dependable time series analytics (DTSA) framework for IoT based SG. Our proposed DTSA framework is capable of proving a dependable data transforming from CPS to target database with an extraction engine to preliminary refining raw data and further cleansing the data with a correction engine built on top of a sensor-network-regularization based matrix factorization (SnrMF) method. The experimental results reveal that our proposed DTSA framework is capable of effectively increasing the dependability of raw time series transforming between CPS and the target database system through the online light-weight extraction engine and the offline correction engine. Our proposed DTSA framework would be useful for other industrial big data practices.

Dependable Visual Light Based Indoor Localization with Automatic Anomaly Detection for Location Based Service of Mobile Cyber-Physical Systems

Indoor localization has become popular in recent years due to the increasing need of location based services in mobile cyber-physical systems (CPS). The massive deployment of Light Emitting Diodes (LEDs) further promotes the indoor localization using visual light. As a key enabling technique for mobile CPS, accurate indoor localization based on visual light communication (VLC) remains nontrivial due to various non-idealities such as attenuation induced by unexpected obstacles. The anomalies of localication can potentially reduce the dependability of location based services. In this paper we develop a novel indoor localization framework based on relative received signal strength (RRSS). Most importantly, an efficient method is derived from the triangle inequality to automatically detect the abnormal LED lamps that are blocked by obstacles. These LED lamps are then ignored by our localization algorithm so that they do not bias the localization results, which improves the dependability of our localization framework. As demonstrated by the simulation results, the proposed techniques can achieve superior accuracy over the conventional approaches especially when there exists abnormal LED lamps.

TORUS: Scalable Requirements Traceability for Large-Scale Cyber-Physical Systems

Cyber-Physical Systems (CPS) contain intertwined and distributed software, hardware and physical components to control complex physical processes. They are now finding wider application in smart grids, such as in addressing the increasingly complex communication and computation needs of substation protection functions. Due to the scale and complexity of the interactions that occur within CPS, tracing requirements through to the system components and software code that implement them is often hard. Existing requirements management systems do not scale well and traceability is difficult to implement and maintain in highly heterogeneous systems. However, the information traces provide is crucial for supporting testing and certification activities in safety-critical environments such as smart grids. The well-formed models of power systems provided by the IEC 61850 standard, and software design structure provided by the IEC 61499 Function Blocks standard, can be leveraged to automate many traceability operations. We present TORUS (Traceability Of Requirements Using Splices), a novel traceability framework for the development of large-scale safety-critical CPS. TORUS introduces splices, autonomous graph-based data structures that automatically create and manage traces between requirements and components through the inevitable changes that occur during system development. The formal, graph-based structure of TORUS lends itself well to the development of sophisticated algorithms to automate the extraction of useful traceability information such as historical records and metrics for requirements coverage and component coupling. By capturing not only the current state of the system but also historical information, TORUS allows project teams to see a much richer view of the system and its artifacts. We apply TORUS to the development of a protection system for smart grid substations. In addition, through a number of experiments in splice creation, modification and applying automated algorithms, we show that TORUS scales easily to large systems containing hundreds of thousands of requirements and system components, and millions of possible traceability links.

Resource Cost-aware Fault-tolerant Design Methodology for End-to-end Functional Safety Computation on Automotive Cyber-Physical Systems

Automotive functional safety standard ISO 26262 aims to avoid unreasonable risks caused by systematic failures and random hardware failures. Automotive functions involve distributed end-to-end computation in automotive cyber-physical systems (ACPS). The automotive industry is highly cost-sensitive to the mass market. This study presents a resource cost-aware fault-tolerant design methodology for end-to-end functional safety computation on ACPS. The proposed design methodology involves early functional safety requirement verification and late resource cost design optimization. We first propose the functional safety requirement verification (FSRV) method to verify the functional safety requirement consisting of reliability and response time requirements of the distributed automotive function during the early design phase. We then propose the resource cost-aware fault-tolerant optimization (RCFO) method to reduce the resource cost while satisfying the functional safety requirement of the function during the late design phase. Finally, we perform experiments with real-life automotive and synthetic automotive functions. Findings reveal that the proposed RCFO and VFSR methods demonstrate satisfactory resource cost reduction compared with other methods while satisfying the functional safety requirement.

A Survey of Mobile Crowdsensing Techniques: A Critical Component for The Internet of Things

Mobile crowdsensing serves as a critical building block for the emerging Internet of Things (IoT) applications. However, the sensing devices continuously generate a large amount of data, which consumes much resources (e.g., bandwidth, energy and storage), and may sacrifice the quality-of-service (QoS) of applications. Prior work has demonstrated that there is significant redundancy in the content of the sensed data. By judiciously reducing the redundant data, the data size and the load can be significantly reduced, thereby reducing resource cost, facilitating the timely delivery of unique, probably critical information and enhancing QoS. This paper presents a survey of existing works for the mobile crowdsensing strategies with emphasis on reducing the resource cost and achieving high QoS. We start by introducing the motivation for this survey, and present the necessary background of crowdsensing and IoT. We then present various mobile crowdsensing strategies and discuss their strengths and limitations. Finally, we discuss the future research directions of mobile crowdsensing for IoT. The survey addresses a broad range of techniques, methods, models, systems and applications related to mobile crowdsensing and IoT. Our goal is not only to analyze and compare the strategies proposed in the prior works but also to discuss their applicability towards the IoT, and provide the guidance on the future research direction of mobile crowdsensing.

BuildingRules: A Trigger-Action Based System To Manage Complex Commercial Buildings

Modern Building Management Systems (BMSs) have been designed to automate the behavior of complex buildings, but unfortunately they do not allow occupants to customize it according to their preferences, and only the facility manager is in charge of setting the building policies. To overcome this limitation, we present BuildingRules, a trigger-action programming based system that aims to provide occupants of commercial buildings with the possibility of specifying the characteristics of their office environment through an intuitive interface. Trigger action programming is intuitive to use and has been shown to be effective in meeting user requirements in home environments. To extend this intuitive interface to commercial buildings, an essential step is to manage the system scalability as large number of users will express their policies. BuildingRules has been designed to scale well for large commercial buildings as it automatically detects conflicts that occur among user specified policies and it supports intelligent grouping of rules to simplify the policies across large number of rooms. We ensure the conflict resolution is fast for a fluid user experience by using the Z3 SMT solver. BuildingRules backend is based on RESTful web services so it can connect to various building management systems and scale well with large number of buildings. We have tested our system with 23 users across 17 days in a virtual office building, and the results we have collected prove the effectiveness and the scalability of BuildingRules.

Switching and Data Injection Attacks on Stochastic Cyber-Physical Systems: Modeling, Resilient Estimation and Attack Mitigation

In this paper, we consider the problem of attack-resilient state estimation, that is to reliably estimate the true system states despite two classes of attacks: (i) attacks on the switching mechanisms and (ii) false data injection attacks on actuator and sensor signals, in the presence of unbounded stochastic process and measurement noise signals. We model the systems under attack as hidden mode stochastic switched linear systems with unknown inputs and propose the use of a multiple-model inference algorithm to tackle these security issues. Moreover, we characterize fundamental limitations to resilient estimation (e.g., upper bound on the number of tolerable signal attacks) and discuss the topics of attack detection, identification and mitigation under this framework. Simulation examples of switching attacks on benchmark and power systems show the efficacy of our approach to recover resilient (i.e., asymptotically unbiased) state estimates.

Long-Term Event Processing over Data Streams in Cyber-Physical Systems

Internet of Things (IoT) is a new paradigm which offers real-time situation awareness and intelligent in-formation reasoning to connect physical and cyber world. Event processing is one of the important corner-stones for IoT to evolve into Cyber-Physical System (CPS) by providing intelligent information discovery and decision-making ability. In various scenarios, event patterns usually take a relatively long time to assemble. Processing long-term event with traditional approaches usually leads to increase runtime states and therefore impacts the processing performance. Hence, it requires an efficient long-term event pro-cessing approach and intermediate results storage/query policy to solve this problem. In this paper, we propose a long-term complex event processing model, named LTCEP, to remedial the noted problem. We leverage the semantic constraints calculus to split long-term event into sub-models. A long-term query and intermediate result buffering mechanism was established to optimize the real-time response ability and throughput performance. Experiments prove that, for long-term event processing, LTCEP model can effec-tively reduce the redundant runtime states, which provides a higher response performance and system throughput comparing to other selected benchmarks.

Time-soundness of Time Petri Nets Modelling Time-Critical Systems

Time Petri Nets (TPN) as a kind of formal method are widely used to model and analyze real-time systems in which events are closely related to time. Since the firing of every event is limited in a fix time intervals, the behaviors of TPNs are more complex than Petri nets without time labels. This paper proposes a novel property for TPN named \emph{time-soundness}. It guarantees that the modeled system always owns deterministic behaviors after any event is executed no matter when the event is executed. We prove that a TPN is time-sound if and only if any two states reached by the same event sequence are bisimilar to each other. We present the concept of \emph{schedulable subclass} in view of the traditional \emph{state class} and then develop an algorithm to check time-soundness based on them. Additionally, we use our concept and method to check whether the control system of a \emph{multi-track level crossing with sensors} is safe and correct.

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