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
Securing Communication Data in Pervasive Social Networking based on Trust with KP-ABE

Pervasive Social Networking (PSN) intends to support instant social activities in a pervasive way at anytime and anywhere. In order to protect crucial social activities and enhance user privacy, securing pervasive social communications becomes especially important. However, neither centralized nor distributed solutions can protect PSN communications as expected. How to automatically control data access in a trustworthy and efficient way is an important security issue. In this paper, we propose a scheme to guarantee communication data security in PSN based on two dimensions of trust in a flexible manner on the basis of Key-Policy Attribute-based Encryption (KP-ABE). Its advantages and performance are justified and evaluated through extensive analysis on security, computation complexity, communication cost, scalability and flexibility, as well as scheme implementation. In addition, we develop a demo system based on Android mobile devices to test our scheme in practice. The results demonstrate its efficiency and effectiveness. Comparison with our previous work based on CP-ABE [Yan and Wang 2014] further shows its feasibility to be applied into PSN.

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.

CHARIOT: Goal-driven Orchestration Middleware for Resilient IoT Systems

The emerging trend in Internet of Things (IoT) applications is to move the computation (cyber) closer to the source of the data (physical). This paradigm is often referred to as edge computing. If edge resources are pooled together they can be used as decentralized shared resources for IoT applications, providing increased capacity to scale up computations and minimize end-to-end latency. Managing applications on these edge resources is hard, however, due to their remote, distributed, and possibly dynamic nature, which necessitates autonomous management mechanisms that facilitate application deployment, failure avoidance, failure management, and incremental updates. To address this need, we present CHARIOT, which is orchestration middleware capable of autonomously managing IoT systems that comprises edge resources and applications. CHARIOT implements a three-layer architecture. The topmost layer comprises a system description language; the middle layer comprises a persistent data storage layer and the corresponding schema to store system information; and the bottom layer comprises a management engine, which uses information stored in persistent data storage to formulate constraints that encode system properties and requirements, thereby enabling the use of Satisfiability Modulo Theories (SMT) solvers to compute optimal system (re)configurations dynamically at runtime. This paper describes the structure and functionality of CHARIOT and evaluates its efficacy as the basis for a smart parking system case study that is responsible for parking space management.

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.

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.

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 Analytics 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.

Privacy-preserving Data Aggregation Computing in Cyber-Physical Social Systems

In cyber-physical social systems (CPSS), a group of volunteers report data about the physical environment through their cyber devices and data aggregation is widely utilized. An important issue in data aggregation for CPSS is to protect users' privacy. In this paper, we use bitwise XOR and propose a bit-choosing algorithm to realize privacy-preserving min, \textit{k}-th min and percentile computation. By our algorithm, the aggregator can confirm whether a user's data value is equal to certain value or within certain scale. Consequently, it is also possible to count the number of users satisfying given conditions. Our bit-choosing algorithm makes sure that the users send non-repetition replies to the aggregator so as to raise the aggregation accuracy. We analyze the communication cost and the achievable accuracy of our algorithm. Via performance comparison against existing protocols, the efficiency and accuracy of our algorithm are verified.

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 Building

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.

A Limb Tracking Platform for Tele-Rehabilitation

The average age of the population is regularly increasing. It is a symptom of an aging society which needs more and more moto-rehabilitative therapies. If the longer life expectancy is coupled with the problem of a decreasing availability of public money for the healthcare sector, the consequence is a degradation of the quality of care provided. A recent trend is to increase the adoption of self-care procedures, such as a motor therapy and rehabilitative sessions performed primarily in patients environment rather than in formal hospitals or healthcare structures. In particular, the diffusion of an efficient telerehabilitation system implies significant advantages for patients, their family, caregivers, clinicians, and researchers, especially in the case of intensive rehabilitation and chronic patients rehabilitation. In line with the promised advantages, this work presents a platform to enhance limb functional recovery through telerehabilitation sessions. The framework relies on a sensing system, based on inertial sensors and data fusion algorithms, a module to provide bio-feedback tailored to the users, and a one dedicated to the physicians practices. The systems design poses interesting cyber-physical problems due the tight interaction between patient and sensors, for instance, when taking body kinematics into account to improve the precision of measurements, simplify the calibration procedure, or generate bio-feedback signals. The precision improvement is shown through a set of experiments. Currently, the proposed solution is being tested in a medical trial, comparing the results with the traditional methodologies, both for patients rehabilitation and healthy subjects training. The presented approach can be quickly adapted for other application fields, as neurological rehabilitation (e.g., Parkinson, Stroke, etc.) and sports training.

Closed-loop quantitative verification of rate-adaptive pacemakers

Rate-adaptive pacemakers are cardiac devices able to automatically adjust the pacing rate depending on the metabolic demand of the patient. These devices are implanted in patients with chronotropic incompetence, i.e. whose heart is unable to provide an adequate rate at increasing levels of physical, mental or emotional activity. Rate-adaptive pacemakers work by processing data from physiological sensors in order to detect the patient's activity and update the pacing rate accordingly. However, rate-adaptation parameters depend on many patient-specific factors such as age, lifestyle and tolerance to rapid pacing, and personalization of such treatments can only be achieved through extensive testing under varying levels of physical exercise, which is time consuming in clinical practice and could be intolerable for the patient. In this work, we introduce a data-driven and model-based approach for the automated verification of rate-adaptive pacemakers. We develop a novel dual-sensor VVIR pacemaker model that combines information from electrocardiogram (ECG) and accelerometer sensors. The approach involves estimation of personalised heart models from patient data and enables closed-loop verification through the generation of synthetic, model-based physiological signals. To capture the probabilistic and non-linear dynamics of the heart, we define a probabilistic extension of timed I/O automata with data and employ statistical model checking (SMC) for the quantitative verification of rate modulation. We evaluate our VVIR pacemaker on three subjects and perform an extensive analysis of rate control under typical exercise curves and at increasing degrees of chronotropic incompetence, indicative of worsening heart conditions. Further, we show that phenomena of sensor-induced endless-loop tachycardia can be reproduced by our closed-loop design, and can be appropriately detected through SMC. The results demonstrate that our approach can provide the necessary safety assurance for rate-adaptive pacemakers through quantitative verification, eliminating the need for exercise testing with real patients through data-driven personalisation of models and rate modulation algorithms.

Time-soundness of Time Petri Nets Modelling Real-Time 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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