Showing papers in "ACM Transactions on Cyber-Physical Systems in 2019"

Reinforcement Learning for UAV Attitude Control

Abstract: Autopilot systems are typically composed of an “inner loop” providing stability and control, whereas an “outer loop” is responsible for mission-level objectives, such as way-point navigation. Autopilot systems for unmanned aerial vehicles are predominately implemented using Proportional-Integral-Derivative (PID) control systems, which have demonstrated exceptional performance in stable environments. However, more sophisticated control is required to operate in unpredictable and harsh environments. Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL), which has had success in other applications, such as robotics. Yet previous work has focused primarily on using RL at the mission-level controller. In this work, we investigate the performance and accuracy of the inner control loop providing attitude control when using intelligent flight control systems trained with state-of-the-art RL algorithms—Deep Deterministic Policy Gradient, Trust Region Policy Optimization, and Proximal Policy Optimization. To investigate these unknowns, we first developed an open source high-fidelity simulation environment to train a flight controller attitude control of a quadrotor through RL. We then used our environment to compare their performance to that of a PID controller to identify if using RL is appropriate in high-precision, time-critical flight control.

A Distributed Tensor-Train Decomposition Method for Cyber-Physical-Social Services

Abstract: Cyber-Physical-Social Systems (CPSS) integrating the cyber, physical, and social worlds is a key technology to provide proactive and personalized services for humans. In this paper, we studied CPSS by taking human-interaction-aware big data (HIBD) as the starting point. However, the HIBD collected from all aspects of our daily lives are of high-order and large-scale, which bring ever-increasing challenges for their cleaning, integration, processing, and interpretation. Therefore, new strategies for representing and processing of HIBD become increasingly important in the provision of CPSS services. As an emerging technique, tensor is proving to be a suitable and promising representation and processing tool of HIBD. In particular, tensor networks, as a significant tensor decomposition technique, bring advantages of computing, storage, and applications of HIBD. Furthermore, Tensor-Train (TT), a type of tensor network, is particularly well suited for representing and processing high-order data by decomposing a high-order tensor into a series of low-order tensors. However, at present, there is still need for an efficient Tensor-Train decomposition method for massive data. Therefore, for larger-scale HIBD, a highly-efficient computational method of Tensor-Train is required. In this paper, a distributed Tensor-Train (DTT) decomposition method is proposed to process the high-order and large-scale HIBD. The high performance of the proposed DTT such as the execution time is demonstrated with a case study on a typical form of CPSS data, Computed Tomography (CT) image data.

Crossroads+: A Time-aware Approach for Intersection Management of Connected Autonomous Vehicles

Abstract: As vehicles become autonomous and connected, intelligent management techniques can be utilized to operate an intersection without a traffic light. When a Connected Autonomous Vehicle (CAV) approaches an intersection, it shares its status and intended direction with the Intersection Manager (IM), and the IM checks the status of other CAVs and assigns a target velocity/reference trajectory for it to maintain. In practice, however, there is an unknown delay between the time a CAV sends a request to the IM and the moment it receives back the response, namely, the Round-Trip Delay (RTD). As a result, the CAV will start tracking the target velocity/reference trajectory later than when the IM expects, which may lead to accidents. In this article, we present a time-aware approach, Crossroads+, that makes CAVs’ behaviors deterministic despite the existence of the unknown RTD. In Crossroads+, we use timestamping and synchronization to ensure that both the IM and the CAVs have the same notion of time. The IM will also set a fixed start time to track the target velocity/reference trajectory for each CAV. The effectiveness of the proposed Crossroads+ technique is illustrated by experiments on a 1/10 scale model of an intersection with CAVs. We also built a simulator to demonstrate the scalability of Crossroads+ for multi-lane intersections. Results from our experiments indicate that our approach can reduce the position uncertainty by 15% in comparison with conventional techniques and achieve up to 36% better throughputs.

Model Conformance for Cyber-Physical Systems: A Survey

Abstract: Model-based development is an important paradigm for developing cyber-physical systems (CPS). The underlying assumption is that the functional behavior of a model is related to the behavior of a more concretized model or the real system. A formal definition of such a relation is called conformance relation. There are a variety of conformance relations, and the question arises of how to select a conformance relation for the development of CPS. The contribution of this article is a survey of the definitions and algorithms of conformance relations for CPS. Additionally, the article compares several conformance relations and provides guidance on which relation to select for specific problems. Finally, we discuss how to select inputs for testing conformance.

Data Sets, Modeling, and Decision Making in Smart Cities: A Survey

Abstract: Cities are deploying tens of thousands of sensors and actuators and developing a large array of smart services. The smart services use sophisticated models and decision-making policies supported by Cyber Physical Systems and Internet of Things technologies. The increasing number of sensors collects a large amount of city data across multiple domains. The collected data have great potential value, but has not yet been fully exploited. This survey focuses on the domains of transportation, environment, emergency and public safety, energy, and social sensing. This article carefully reviews both the data sets being collected across 14 smart cities and the state-of-the-art work in modeling and decision making methodologies. The article also points out the characteristics, challenges faced today, and those challenges that will be exacerbated in the future. Key data issues addressed include heterogeneity, interdisciplinary, integrity, completeness, real-timeliness, and interdependencies. Key decision making issues include safety and service conflicts, security, uncertainty, humans in the loop, and privacy.

Design and Analysis of Delay-Tolerant Intelligent Intersection Management

Abstract: The rapid development of vehicular network and autonomous driving technologies provides opportunities to significantly improve transportation safety and efficiency. One promising application is centralized intelligent intersection management, where an intersection manager accepts requests from approaching vehicles (via vehicle-to-infrastructure communication messages) and schedules the order for those vehicles to safely crossing the intersection. However, communication delays and packet losses may occur due to the unreliable nature of wireless communication or malicious security attacks (e.g., jamming and flooding), and could cause deadlocks and unsafe situations. In our previous work, we considered these issues and proposed a delay-tolerant intersection management protocol for intersections with a single lane in each direction. In this work, we address key challenges in efficiency and deadlock when there are multiple lanes from each direction, and propose a delay-tolerant protocol for general multi-lane intersection management. We prove that this protocol is deadlock free, safe, and satisfies the liveness property. Furthermore, we extend the traffic simulation suite SUMO with communication modules, implement our protocol in the extended simulator, and quantitatively analyze its performance with the consideration of communication delays. Finally, we also model systems that use smart traffic lights with various back-pressure scheduling methods in SUMO, including the basic back-pressure control, the capacity-aware back-pressure control, and the adaptive max-pressure control. We then compare our delay-tolerant intelligent intersection protocol with smart traffic lights that use the three back-pressure scheduling methods, in the case of a network of interconnected intersections. Simulation results demonstrate that our approach significant outperforms the smart traffic lights under normal operation (i.e., when the communication delay is not too large).

SafeWatch: A Wearable Hand Motion Tracking System for Improving Driving Safety

Abstract: Driving while distracted or losing alertness significantly increases the risk of traffic accident. The emerging Internet of Things (IoT) systems for smart driving hold the promise of significantly reducing road accidents. In particular, detecting unsafe hand motions and warning the driver using smart sensors can improve the driver’s alertness and skill. However, due to the impact of the vehicle’s movement and the significant variation across different driving environments, detecting the position of the driver’s hand is challenging. This article presents SafeWatch—a system based on smartwatches and smartphones that detects the driver’s unsafe behaviors in a real-time manner. SafeWatch infers driver’s hand position based on several important features, such as the posture of the driver’s forearm and the vibration on the smartwatch. SafeWatch employs a novel adaptive training algorithm that keeps updating the training data set at run-time based on inferred hand positions in certain driving conditions. The evaluation with 75 real driving trips from six subjects shows that SafeWatch has a high accuracy over 97.0% for both recall and precision in detection of the unsafe hand positions when the condition lasts for more than 6.0s, as well as over 97.1% recall and over 91.0% precision in detection of the unsafe hand movements when it lasts for more than 2.5s. The relative position of the hand to the steering wheel also reveals some detailed driving habits, like the type of steering method.

A Predictive Framework for Dynamic Heavy-Duty Vehicle Platoon Coordination

Abstract: This article describes a system to facilitate dynamic en route formation of heavy-duty vehicle platoons with the goal of reducing fuel consumption. Safe vehicle platooning is a maturing technology that leverages modern sensor, control, and communication technology to automatically regulate the inter-vehicle distances. Truck platooning has been shown to reduce fuel consumption through slipstreaming by up to 10%; under realistic highway-driving conditions. To further benefit from this technology, a platoon coordinator is proposed, which interfaces with fleet management systems and suggests how platoons can be formed in a fuel-efficient manner over a large region. The coordinator frequently updates the plans to react to newly available information. This way, it requires a minimum of customization with respect to the logistic operations. We discuss the system architecture in detail and introduce important underlying methodological foundations. Plans are derived in computationally tractable stages optimizing fuel savings from platooning. The effectiveness of this approach is verified in a simulation study. It shows that the coordinated platooning system can improve over spontaneously occurring platooning even under the presence of disturbances. A real demonstrator has also been developed. We present data from an experiment in which three vehicles were coordinated to form a platoon on public highways under normal traffic conditions. It demonstrates the feasibility of coordinated en route platoon formation with current communication and on-board technology. Simulations and experiments support that the proposed system is technically feasible and a potential solution to the problem of using vehicle platooning in an operational context.

A System-level Behavioral Detection Framework for Compromised CPS Devices: Smart-Grid Case

Abstract: Cyber-Physical Systems (CPS) play a significant role in our critical infrastructure networks from power-distribution to utility networks. The emerging smart-grid concept is a compelling critical CPS infrastructure that relies on two-way communications between smart devices to increase efficiency, enhance reliability, and reduce costs. However, compromised devices in the smart grid poses several security challenges. Consequences of propagating fake data or stealing sensitive smart grid information via compromised devices are costly. Hence, early behavioral detection of compromised devices is critical for protecting the smart grid’s components and data. To address these concerns, in this article, we introduce a novel and configurable system-level framework to identify compromised smart grid devices. The framework combines system and function call tracing techniques with signal processing and statistical analysis to detect compromised devices based on their behavioral characteristics. We measure the efficacy of our framework with a realistic smart grid substation testbed that includes both resource-limited and resource-rich devices. In total, using our framework, we analyze six different types of compromised device scenarios with different resources and attack payloads. To the best of our knowledge, the proposed framework is the first in detecting compromised CPS smart grid devices with system and function-level call tracing techniques. The experimental results reveal an excellent rate for the detection of compromised devices. Specifically, performance metrics include accuracy values between 95% and 99% for the different attack scenarios. Finally, the performance analysis demonstrates that the use of the proposed framework has minimal overhead on the smart grid devices’ computing resources.

Bayesian Spatiotemporal Gaussian Process for Short-term Load Forecasting Using Combined Transportation and Electricity Data

Abstract: Smart cities can be viewed as large-scale Cyber-Physical Systems (CPS) where different sensors and devices record the cyber and physical indicators of the city systems. The collected data are used for improving urban life by offering services such as accurate electric load forecasting, and more efficient traffic management. Traditional monitoring for electricity and transportation networks generally do not provide full observability due to their limited coverage as well as high implementation and maintenance costs. For example, continuous traffic data collection is mostly limited to major highways only in big cities, whereas local roadways are usually covered once or twice a year. Also, there are no high-fidelity and real-time electric monitoring systems in all parts of power distribution networks. Combining the limited data from each of the urban systems together (e.g., electricity, transportation, environment, etc.) provides a better picture of the energy flow in a city. Furthermore, a city should be considered as a collection of the layers of tangled infrastructure networks, which connects people, places, and resources. Therefore, the study of traffic or electricity consumption forecasting should go beyond the transportation and electricity networks and merge with each other and even with other city networks such as environmental networks. As such, this article proposes a Bayesian spatiotemporal Gaussian Process model that employs the most informative spatiotemporal interdependency among different interconnected networks (in this case, electricity, transportation, and weather). The proposed load forecasting method is compared with other state-of-the-art methods using real-life data obtained from the City of Tallahassee in Florida. Results show that the proposed Bayesian spatiotemporal Gaussian Process model outperforms state-of-the-art methods.

A Crowdsensing-based Cyber-physical System for Drone Surveillance Using Random Finite Set Theory

Abstract: Given the popularity of drones for leisure, commercial, and government (e.g., military) usage, there is increasing focus on drone regulation. For example, how can the city council or some government agency detect and track drones more efficiently and effectively, say, in a city, to ensure that the drones are not engaged in unauthorized activities? Therefore, in this article, we propose a crowdsensing-based cyber-physical system for drone surveillance. The proposed system, CSDrone, utilizes surveillance data captured and sent from citizens’ mobile devices (e.g., Android and iOS devices, as well as other image or video capturing devices) to facilitate jointly drone detection and tracking. Our system uses random finite set (RFS) theory and RFS-based Bayesian filter. We also evaluate CSDrone’s effectiveness in drone detection and tracking. The findings demonstrate that in comparison to existing drone surveillance systems, CSDrone has a lower cost, and is more flexible and scalable.

Design and Implementation of Secret Key Agreement for Platoon-based Vehicular Cyber-physical Systems

Abstract: In a platoon-based vehicular cyber-physical system (PVCPS), a lead vehicle that is responsible for managing the platoon’s moving directions and velocity periodically disseminates control messages to the vehicles that follow. Securing wireless transmissions of the messages between the vehicles is critical for privacy and confidentiality of the platoon’s driving pattern. However, due to the broadcast nature of radio channels, the transmissions are vulnerable to eavesdropping. In this article, we propose a cooperative secret key agreement (CoopKey) scheme for encrypting/decrypting the control messages, where the vehicles in PVCPS generate a unified secret key based on the quantized fading channel randomness. Channel quantization intervals are optimized by dynamic programming to minimize the mismatch of keys. A platooning testbed is built with autonomous robotic vehicles, where a TelosB wireless node is used for onboard data processing and multi-hop dissemination. Extensive real-world experiments demonstrate that CoopKey achieves significantly low secret bit mismatch rate in a variety of settings. Moreover, the standard NIST test suite is employed to verify randomness of the generated keys, where the p-values of our CoopKey pass all the randomness tests. We also evaluate CoopKey with an extended platoon size via simulations to investigate the effect of system scalability on performance.

Socially Aware Path Planning for a Flying Robot in Close Proximity of Humans

Abstract: In this article, we present a preliminary motion planning framework for a cyber-physical system consisting of a human and a flying robot in vicinity. The motion planning of the flying robot takes into account the human’s safety perception. We aim to determine a parametric model for the human’s safety perception based on test data. We use virtual reality as a safe testing environment to collect safety perception data reflected on galvanic skin response (GSR) from the test subjects experiencing a flying robot in their vicinity. The GSR signal contains both meaningful information driven by the interaction with the robot and also disturbances from unknown factors. To address the issue, we use two parametric models to approximate the GSR data: (1) a function of the robot’s position and velocity and (2) a random distribution. Intuitively, we need to choose the more likely model given the data. When GSR is statistically independent of the flying robot, then the random distribution should be selected instead of the function of the robot’s position and velocity. We implement the intuitive idea under the framework of hidden Markov model (HMM) estimation. As a result, the proposed HMM-based model improves the likelihood compared to the Gaussian noise model, which does not make a distinction between relevant and irrelevant samples due to unknown factors. We also present a numerical optimal path planning method that considers the safety perception model while ensuring spatial separation from the obstacle despite the time discretization. Optimal paths generated using the proposed model result in a reasonably safe distance from the human. In contrast, the trajectories generated by the standard regression model with the Gaussian noise assumption, without consideration of unknown factors, have undesirable shapes.

A Sustainable and User-Behavior-Aware Cyber-Physical System for Home Energy Management

Abstract: There is a growing trend for employing cyber-physical systems to help smart homes improve the comfort of residents. However, a residential cyber-physical system is different from a common cyber-physical system since it directly involves human interaction, which is full of uncertainty. The existing solutions could be effective for performance enhancement in some cases when no inherent and dominant human factors are involved. Besides, the rapidly rising interest in the deployments of cyber-physical systems at home does not normally integrate with energy management schemes, which is a central issue that smart homes have to face. In this article, we propose a cyber-physical-system-based energy management framework to enable a sustainable-edge computing paradigm while meeting the needs of home energy management and residents. This framework aims to enable the full use of renewable energy while reducing electricity bills for households. A prototype system was implemented using real-world hardware. The experiment results demonstrated that renewable energy is fully capable of supporting the reliable running of home appliances most of the time and electricity bills could be cut by up to 60% when our proposed framework was employed.

Modeling and Optimization for Self-powered Non-volatile IoT Edge Devices with Ultra-low Harvesting Power

Abstract: Energy harvesters are becoming increasingly popular as power sources for IoT edge devices. However, one of the intrinsic problems of energy harvester is that harvesting power is often weak and frequently interrupted. Therefore, energy harvesting powered edge devices have to work intermittently. To maintain execution progress, execution states need to be checkpointed into the non-volatile memory before each power failure. In this way, previous execution states can be resumed after power comes back again. Nevertheless, frequent checkpointing and low charging efficiency generate significant energy overhead. To alleviate these problems, this article conducts a thorough energy efficiency analysis and proposes three algorithms to maximize the energy efficiency of program execution. First, a non-volatile processor-aware task scheduling algorithm is proposed to reduce the size of checkpointing data. Second, a tentative checkpointing avoidance technique is proposed to avoid checkpointing for further reduction of checkpointing overhead. Finally, a dynamic wake-up strategy is proposed to wake up the edge device at proper voltages where the total hardware and software overhead is minimized for further energy efficiency maximization. The experiments on a real testbed demonstrate that, with the proposed algorithms, an edge device is resilient to the extremely weak and intermittent power supply and the energy efficiency can be achieved more than 2× higher than the fundamental baseline and 1.5× higher than the state-of-the-art technique.

Dynamic Watermarking-based Defense of Transportation Cyber-physical Systems

Abstract: The transportation sector is on the threshold of a revolution as advances in real-time communication, real-time computing, and sensing technologies have brought to fruition the capability to build Transportation Cyber-Physical Systems (TCPS) such as self-driving cars, unmanned aerial vehicles, adaptive cruise control systems, truck platoons, and so on. While there are many benefits that TCPSs have to offer, a major challenge that needs to be addressed to enable their proliferation is their vulnerability to cyber attacks. In this article, we demonstrate, using laboratory prototypes of TCPSs, how the approach of Dynamic Watermarking can secure them from arbitrary sensor attacks. Specifically, we consider two TCPSs of topical interest: (i) an adaptive cruise control system and (ii) a system of self-driving vehicles tracking given trajectories. In each of these systems, we first show how cyber attacks on sensors can compromise safety and cause collisions between vehicles in spite of the presence of a collision avoidance module in the system. We then apply the approach of Dynamic Watermarking and demonstrate that it detects attacks with “low” delay. Once an attack is detected, the controller can take appropriate control actions to prevent collisions, thereby guaranteeing safety in the sense of collision freedom.

Data Integrity Threats and Countermeasures in Railway Spot Transmission Systems

Abstract: Modern trains rely on balises (communication beacons) located on the track to provide location information as they traverse a rail network. Balises, such as those conforming to the Eurobalise standard, were not designed with security in mind and are thus vulnerable to cyber attacks targeting data availability, integrity, or authenticity. In this work, we discuss data integrity threats to balise transmission modules and use high-fidelity simulation to study the risks posed by data integrity attacks. To mitigate such risk, we propose a practical two-layer solution: At the device level, we design a lightweight and low-cost cryptographic solution to protect the integrity of the location information; at the system layer, we devise a secure hybrid train speed controller to mitigate the impact under various attacks. Our simulation results demonstrate the effectiveness of our proposed solutions.

Fast Feedback Control over Multi-hop Wireless Networks with Mode Changes and Stability Guarantees

Abstract: Closing feedback loops fast and over long distances is key to emerging cyber-physical applications; for example, robot motion control and swarm coordination require update intervals of tens of milliseconds. Low-power wireless communication technology 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 networks has only been demonstrated for update intervals on the order of seconds. To fill this gap, this article presents a wireless embedded system that supports dynamic mode changes and tames imperfections impairing control performance (e.g., jitter and message loss), and a control design that exploits the essential properties of this system to provably guarantee closed-loop stability for physical processes with linear time-invariant dynamics in the presence of mode changes. Using experiments on a cyber-physical testbed with 20 wireless devices and multiple cart-pole systems, we are the first to demonstrate and evaluate feedback control and coordination with mode changes over multi-hop networks for update intervals of 20 to 50 milliseconds.

TOP: Optimizing Vehicle Driving Speed with Vehicle Trajectories for Travel Time Minimization and Road Congestion Avoidance

Abstract: Traffic congestion control is pivotal for intelligent transportation systems. Previous works optimize vehicle speed for different objectives such as minimizing fuel consumption and minimizing travel time. However, they overlook the possible congestion generation in the future (e.g., in 5 minutes), which may degrade the performance of achieving the objectives. In this article, we propose a vehicle Trajectory–based driving speed OPtimization strategy (TOP) to minimize vehicle travel time and meanwhile avoid generating congestion. Its basic idea is to adjust vehicles’ mobility to alleviate road congestion globally. TOP has a framework for collecting vehicles’ information to a central server, which calculates the parameters depicting the future road condition (e.g., driving time, vehicle density, and probability of accident). Based on the collected information, the central server also measures the friendship among the vehicles and considers the delay caused by red traffic signals to help estimating the vehicle density of the road segments. The server then formulates a non-cooperative Stackelberg game considering these parameters, in which when each vehicle aims to minimize its travel time, the road congestion is also proactively avoided. After the Stackelberg equilibrium is reached, the optimal driving speed for each vehicle and the expected vehicle density that maximizes the utilization of the road network are determined. Our real trace analysis confirms some characteristics of vehicle mobility to support the design of TOP. Extensive trace-driven experiments show the effectiveness and superior performance of TOP in comparison with other driving speed optimization methods.

Cyber-physical Support of Daily Activities: A Robot/Smart Home Partnership

Abstract: This article introduces RAS, a cyber-physical system that supports individuals with memory limitations to perform daily activities in their own homes. RAS represents a partnership between a smart home, a robot, and software agents. When smart home residents perform activities, RAS senses their movement in the space and identifies the current activity. RAS tracks activity steps to detect omission errors. When an error is detected, the RAS robot finds and approaches the human with an offer of assistance. Assistance consists of playing a video recording of the entire activity, showing the omitted activity step, or guiding the resident to the object that is required for the current step. We evaluated RAS performance for 54 participants performing three scripted activities in a smart home testbed and for 2 participants using the system over multiple days in their own homes. In the testbed experiment, activity errors were detected with a sensitivity of 0.955 and specificity of 0.992. RAS assistance was performed successfully with a rate of 0.600. In the in-home experiments, activity errors were detected with a combined sensitivity of 0.905 and a combined specificity of 0.988. RAS assistance was performed successfully for the in-home experiments with a rate of 0.830.

A Dynamic Routing Framework for Shared Mobility Services

Abstract: Travel time in urban centers is a significant contributor to the quality of living of its citizens. Mobility on Demand (MoD) services such as Uber and Lyft have revolutionized the transportation infrastructure, enabling new solutions for passengers. Shared MoD services have shown that a continuum of solutions can be provided between the traditional private transport for an individual and the public mass transit-based transport, by making use of the underlying cyber-physical substrate that provides advanced, distributed, and networked computational and communicational support. In this article, we propose a novel shared mobility service using a dynamic framework. This framework generates a dynamic route for multi-passenger transport, optimized to reduce time costs for both the shuttle and the passengers and is designed using a new concept of a space window. This concept introduces a degree of freedom that helps reduce the cost of the system involved in designing the optimal route. A specific algorithm based on the Alternating Minimization approach is proposed. Its analytical properties are characterized. Detailed computational experiments are carried out to demonstrate the advantages of the proposed approach and are shown to result in an order of magnitude improvement in the computational efficiency with minimal optimality gap when compared to a standard Mixed Integer Quadratically Constrained Programming-based algorithm.

Publish or Drop Traffic Event Alerts? Quality-aware Decision Making in Participatory Sensing-based Vehicular CPS

Abstract: Vehicular cyber-physical systems (VCPS), among several other applications, may help address an ever-increasing challenge of traffic congestion in large cities. Nevertheless, VCPS can be hindered by information falsification problem, resulting due to the wrong perception of a traffic event or deliberate faking by the participating vehicles. Such information fabrication causes the re-routing of vehicles and artificial congestion, leading to economic, safety, environmental, and health hazards. Thus, it is imperative to infer truthful traffic information in real-time to restore the operational reliability of the VCPS. In this work, we propose a novel reputation scoring and decision support framework, called Spoofed and False Report Eradicator (SAFE), which offers a cost-effective and efficient solution to handle information falsification problem in the VCPS domain. The framework includes humans in the sensing loop by exploiting the paradigm of participatory sensing, a concept of a mobile security agent (MSA) to nullify the effects of deliberate false contribution, and a variant of the distance bounding mechanism to thwart location-spoofing attacks. A regression-based model integrates these effects to generate the expected truthfulness of a participant’s contribution. To determine if any contribution is true or false, a generalized linear model is used to transform the expected truthfulness into a Quality of Contribution (QoC) score. The QoC of different reports is aggregated to compute user reputation. Such reputation enables classification of different participation behaviors. Finally, an Expected Utility Theory (EUT)-based decision model is proposed that utilizes the reputation score to determine if event-specific information should be published or dropped. To evaluate the SAFE framework through experimental study, we used both simulated and real data to compare its reputation-based user segregation performance with state-of-the-art frameworks. Experimental results exhibit that SAFE captures the fine differences in participants’ behavior through the quality and quantity of participation, and the accuracy of their informed location. It also significantly improves operational reliability through publishing the information of only legitimate events.

Test Specification and Generation for Connected and Autonomous Vehicle in Virtual Environments

Abstract: The trend of connected/autonomous features adds significant complexity to the traditional automotive systems to improve driving safety and comfort. Engineers are facing significant challenges in designing test environments that are more complex than ever. We propose a test framework that allows one to automatically generate various virtual road environments from the path and behavior specifications. The path specification intends to characterize geometric paths that an environmental object (e.g., a roadway or a pedestrian) needs to be visualized or move over. We characterize this aspect in the form of constraints of 3-Dimensional (3D) coordinates. Then, we introduce a test coverage, called an area coverage, to quantify the quality of the generated paths in terms of how diverse of an area the generated paths can cover. We propose an algorithm that automatically generates such paths using an SMT (Satisfiability Modulo Theories) solver. However, the behavioral specification intends to characterize how an environmental object changes its mode over time by interacting with other objects (e.g., a pedestrian waits for a signal or starts crossing). We characterize this aspect in the form of timed automata. Then, we introduce a test coverage, called an edge/location coverage, to quantify the quality of the generated mode changes in terms of how many modes or transitions are visited. We propose a method that automatically generates many different mode changes using a model-checking. To demonstrate the test framework, we developed the right-turn pedestrian warning system in intersection scenarios and generated many different types of pedestrian paths and behaviors to analyze the effectiveness of the system.

National-scale Traffic Model Calibration in Real Time with Multi-source Incomplete Data

Abstract: Real-time traffic modeling at national scale is essential to many applications, but its calibration is extremely challenging due to its large spatial and fine temporal coverage. The existing work is focused on urban-scale calibration with complete field data from single data sources (e.g., loop sensors or taxis), which cannot be generalized to national scale because complete single-source field data at national scale are almost impossible to obtain. To address this challenge, in this article, we design MultiCalib, a model calibration framework to optimize traffic models based on multiple incomplete data sources at national scale in real time. Instead of simply combining multi-source data, we theoretically formulate a multi-source model calibration problem based on real-world contexts and multi-view learning. In particular, we design (i) convex multi-view learning to integrate multi-source data by quantifying biases of data sources, and (ii) context-aware tensor decomposition to infer incomplete multi-source data by extracting real-world contexts. More importantly, we implement and evaluate MultiCalib with two heterogeneous nationwide vehicle networks with 340,000 vehicles to infer traffic conditions on 36 expressways and 119 highways, along with four cities across China. The results show that MultiCalib outperforms baseline calibration by 25% on average with the same input data. Based on the proposed national-scale traffic model calibration, we design a novel dispatching framework integrated with our speed calibration model where we guide a vehicular fleet among national-scale highways with a routing strategy to reduce general traveling time. The results show that a routing strategy based on MultiCalib outperforms a routing strategy based on a state-of-the-art traffic model by 45% on average.

CSIP: A Synchronous Protocol for Automated Vehicles at Road Intersections

Abstract: Intersection management is one of the main challenging issues in road safety because intersections are a leading cause of traffic congestion and accidents. In fact, more than 44% of all reported crashes in the U.S. occur around intersection areas, which, in turn, has led to 8,500 fatalities and approximately 1 million injuries every year. With vehicles expected to become self-driving, the question is whether high throughput can be obtained through intersections while keeping them safe. A spatio-temporal intersection protocol named the Ballroom Intersection Protocol (BRIP) [8] was recently proposed in the literature to address this situation. Under this protocol, automated and connected vehicles arrive at and go through an intersection in a cooperative fashion with no vehicle needing to stop, while maximizing the intersection throughput. Though no vehicles run into one another under ideal environments with BRIP, vehicle accidents can occur when the self-driving vehicles have location errors and/or control system failure. In this article, we present a safe and practical intersection protocol named the Configurable Synchronous Intersection Protocol (CSIP). CSIP is a more general and resilient version of BRIP. CSIP utilizes a certain inter-vehicular distance to meet safety requirements in the presence of GPS inaccuracies and control failure. The inter-vehicular distances under CSIP are much more acceptable and comfortable to human passengers due to longer inter-vehicular distances that do not cause fear. With CSIP, the inter-vehicular distances can also be changed at each intersection to account for different traffic volumes, GPS accuracy levels, and geographical layout of intersections. Our simulation results show that CSIP never leads to traffic accidents even when the system has typical location errors, and that CSIP increases the traffic throughput of the intersections compared to common signalized intersections.

Designing a Controller with Image-based Pipelined Sensing and Additive Uncertainties

Abstract: Pipelined image-based control uses parallel instances of its image-processing algorithm in a pipelined fashion to improve the quality of control. A performance-oriented control design improves the controller settling time with each additional processing resource, which creates a resources-performance trade-off. In real-life applications, it is common to have a continuous-time model with additive uncertainties in one or more parameters that may affect the controller performance and the aforementioned trade-off. We present a robustness analysis framework for performance-oriented pipelined controllers with additive model uncertainties. We present a technique to obtain discrete-time uncertainties based on the continuous-time uncertainties for given uncertainty bounds. To benchmark such uncertainty bounds for a real system, we consider uncertainties in one element of the system, potentially caused by multiple uncertain parameters in the model. Robustness and its impact in the trade-off analysis are studied. We also provide a robustness-oriented pipelined controller design that takes into account the benchmarked uncertainties. Our results show that in performance-oriented designs, the tolerable uncertainties for a pipelined controller decrease when increasing the number of pipes. In robustness-oriented designs, the controller robustness is enhanced with each newly added pipe. We show the feasibility of our technique by implementing a realistic example in a Hardware-in-the-Loop simulation.

Energy-Efficient ECG Signal Compression for User Data Input in Cyber-Physical Systems by Leveraging Empirical Mode Decomposition

Abstract: Human physiological data are naturalistic and objective user data inputs for a great number of cyber-physical systems (CPS). Electrocardiogram (ECG) as a widely used physiological golden indicator for certain human state and disease diagnosis is often used as user data input for various CPS such as medical CPS and human–machine interaction. Wireless transmission and wearable technology enable long-term continuous ECG data acquisition for human–CPS interaction; however, these emerging technologies bring challenges of storing and wireless transmitting huge amounts of ECG data, leading to energy efficiency issue of wearable sensors. ECG signal compression technique provides a promising solution for these challenges by decreasing ECG data size. In this study, we develop the first scheme of leveraging empirical mode decomposition (EMD) on ECG signals for sparse feature modeling and compression and further propose a new ECG signal compression framework based on EMD constructed feature dictionary. The proposed method features in compressing ECG signals using a very limited number of feature bases with low computation cost, which significantly improves the compression performance and energy efficiency. Our method is validated with the ECG data from MIT-BIH arrhythmia database and compared with existing methods. The results show that our method achieves the compression ratio (CR) of up to 164 with the root mean square error (RMSE) of 3.48% and the average CR of 88.08 with the RMSE of 5.66%, which is more than twice of the average CR of the state-of-the-art methods with similar recovering error rate of around 5%. For diagnostic distortion perspective, our method achieves high QRS detection performance with the sensitivity (SE) of 99.8% and the specificity (SP) of 99.6%, which shows that our ECG compression method can preserve almost all the QRS features and have no impact on the diagnosis process. In addition, the energy consumption of our method is only 30% of that of other methods when compared under the same recovering error rate.

Reliable Communication and Latency Bound Generation in Wireless Cyber-Physical Systems

Abstract: Low-power wireless communication has been widely used in cyber-physical systems that require time-critical data delivery. Achieving this goal is challenging because of link burstiness and interference. Based on significant empirical evidence of 21 days and over 3.6 M packet transmissions per link, we propose both routing and scheduling algorithms that produce latency bounds of the real-time periodic streams and accounts for both link bursts and interference. The solution is achieved through the definition of a new metric Bmax that characterizes links by their maximum burst length, and by choosing a novel least-burst-route that minimizes the sum of worst-case burst lengths over all links in the route. With extensive data-driven analysis, we show that our algorithms outperform existing solutions by achieving accurate latency bound with much less energy consumption. In addition, a testbed evaluation consisting of 48 nodes spread across a floor of a building shows that we obtain 100% reliable packet delivery within derived latency bounds. We also demonstrate how performance deteriorates and discuss its implications for wireless networks with insufficient high-quality links.

Resilient Clock Synchronization Using Power Grid Voltage

Abstract: Many clock synchronization protocols based on message passing, e.g., the Network Time Protocol (NTP), assume symmetric network delays to estimate the one-way packet transmission time as half of the round-trip time. As a result, asymmetric network delays caused by either network congestion or malicious packet delays can cause significant synchronization errors. This article exploits sinusoidal voltage signals of an alternating current (AC) power grid to limit the impact of the asymmetric network delays on these clock synchronization protocols. Our extensive measurements show that the voltage signals at geographically distributed locations in a city are highly synchronized. Leveraging calibrated voltage phases, we develop a new clock synchronization protocol that we call Grid Time Protocol (GTP), which allows direct measurement of one-way packet transmission times between its slave and master nodes, subject to an analytic condition that can be easily verified in practice. The direct measurements render GTP resilient against asymmetric network delays under this condition. A prototype implementation of GTP maintains sub-millisecond synchronization accuracy for two nodes tens of kilometers apart in the presence of malicious packet delays. The result has been demonstrated for both Singapore and Hangzhou, China. Simulations driven by real network delay measurements between Singapore and Hangzhou under both normal and congested network conditions also show the synchronization accuracy improvement by GTP. We believe that GTP is suitable for grid-connected distributed systems that are currently served by NTP but desire higher resilience against unfavorable network dynamics and packet delay attacks.

The AirTight Protocol for Mixed Criticality Wireless CPS

Abstract: This article describes the motivation, design, analysis, and configuration of the criticality-aware multi-hop wireless communication protocol AirTight. Wireless communication has become a crucial part of the infrastructure of many cyber-physical applications. Many of these applications are real-time and also mixed-criticality, in that they have components/subsystems with different consequences of failure. Wireless communication is inevitably subject to levels of external interference. In this article, we represent this interference using a criticality-aware fault model; for each level of temporal interference in the fault model, we guarantee the timing behaviour of the protocol (i.e., we guarantee that packet deadlines are satisfied for certain levels of criticality). Although a new protocol, AirTight is built upon existing standards such as IEEE 802.15.4. A prototype implementation and protocol-accurate simulator have been produced. This article develops a series of schedulability analysis techniques for single-channel and multichannel wireless Cyber-Physical Systems (CPS). Heuristics are specified and evaluated as the starting point of design space exploration. Genetic algorithms are then defined and evaluated to assess their performance in developing schedule tables incorporating multichannel allocations in these systems.