Services In Cyber-physical Systems Research Articles

An Adaptive Energy Orchestrator for Cyberphysical Systems Using Multiagent Reinforcement Learning

Reducing carbon emissions is a critical issue for the near future as climate change is an imminent reality. To reduce our carbon footprint, society must change its habits and behaviours to optimise energy consumption, and the current progress in embedded systems and artificial intelligence has the potential to make this easier. The smart building concept and intelligent energy management are key points to increase the use of renewable sources of energy as opposed to fossil fuels. In addition, cyber-physical systems (CPSs) provide an abstraction of the management of services that allows the integration of both virtual and physical systems in a seamless control architecture. In this paper, we propose to use multiagent reinforcement learning (MARL) to model the CPS services control plane in a smart house, with the purpose of minimising, by shifting or shutdown services, the use of non-renewable energy (fuel generator) by exploiting solar production and batteries. Furthermore, our proposal dynamically adapts its behaviour in real time according to current and historic energy production, thus being able to handle occasional changes in energy production due to meteorological phenomena or unexpected energy consumption. In order to evaluate our proposal, we have developed an open-source smart building energy simulator and deployed our use case. Finally, several simulations with different configurations are evaluated to verify the performance. The simulation results show that the reinforcement learning solution outperformed the priority-based and the heuristic-based solutions in both power consumption and adaptability in all configurations.

Reinforced practical Byzantine fault tolerance consensus protocol for cyber physical systems

Cyber–physical systems (CPS) are becoming an essential component of modern life. CPS services enable information to be exchanged between physical devices and virtual systems. The increasing malicious activities causing confidential data leakage and incorrect performance of devices are posing challenges for protection against cyber threats. Therefore, development of effective solutions that can protect both CPS data and data exchange networks are extremely urgent. This study makes some improvements on Practical Byzantine Fault Tolerance (PBFT), and provides a critical analysis of the feasibility of using blockchain technology to protect constrained CPS data. It also justifies the choice of the improved PBFT for implementation on such devices and simulates the main distributed ledger scenarios. The improved PBFT works as follows: first, the client broadcasts the signed transaction data to the entire network, rather than just the master node, followed by a hash value comparison verification process; second, select the master node based on the longest chain principle, and punish the malicious node via the “blacklist” mechanism; third, add the data synchronization and verification process, as well as dynamically entering and leaving nodes via state transitions. Ultimately, the PBFT commit phase is eliminated, resulting in a two-stage process. In comparison to the original PBFT protocol, the Reinforced Practical Byzantine Fault Tolerance (RPBFT) protocol may substantially enhance system throughput and minimize consensus communication time, thereby improving overall system efficiency while guaranteeing security.

A case study of intelligent manufacturing for key components of vehicles

Currently, most research on intelligent manufacturing stays at the theoretical stage, which can enhance the understanding of smart factory concepts, but it is still difficult to establish a clear strategy for the industry. In this research, cyber-physical system (CPS) is applied to connect the manufacturing equipment of the production system, so as to develop the internet of things (IoT) object layer. Furthermore, a smart factory framework consisting of IoT object layer, CPS network layer, and CPS service layer is developed, connecting the smart shop-floor and the cloud service platform through the industrial network so as to implement the integration of physical manufacturing and virtual manufacturing. This framework is applied in the production of vehicle components, and the results show that the application of this framework in an actual automobile chassis smart factory can greatly improve production efficiency and energy efficiency, reduce enterprise costs, and shorten product development cycles, accelerating the process of enterprise transformation to informatization, digitalization, and intelligence. Since this framework for intelligent manufacturing has been verified under actual conditions, it can be taken as a reference for further development of the framework for intelligent manufacturing.

Cost risk analysis for instance recommendation in a sustainable Cloud‐cyber‐physical system framework

AbstractCloud markets advocate powerful instances to take computation over from the cyber‐physical system (CPS). Combining the Cloud and CPS layer, the whole Cloud–CPS framework is designed to achieve both accurate data sensing and fast data analysis. While most researchers trust the computation side, and focus on the actuator in the physical space to ensure the service‐level objectives, SLO, that is, deadline misses, cloud can be a threat to the service sustainability as instance may fail, especially when one tries to make a cost‐effective design. Specifically, users must bear the risk of instance failure. These risks can cause the entire cyber‐physical system to collapse. Our work tackles the cloud aspect of the sustainability challenge from the cloud side in a cloud–CPS framework. We have studied the instance selection problem for the CPS systems, and propose a Cost‐Risk Analysis for Instance Recommendation, or CRAIR, to support a sustainable Cloud–CPS framework. We have adopted the classic risk analysis process from the portfolio management in hedge financial market, combining with the system modeling for the CPS instance selection, as an optimization problem. To solve this problem, we formulate it as a multi‐armed bandit problem and solve it with our upper confidence bound bandit algorithm together, our CRAIR can provide an online risk analysis to maximize the profit with a comparative ratio of O(1+). We have evaluated CRAIR based on simulations using real‐world Google and Alibaba workloads and cloud market numbers. The results show that, compared to traditional approaches, our approach provides the best tradeoff between SLOs and costs. All users achieve their SLOs goals while minimizing their average expenses by 34.6%. By using CRAIR for instance selection, the CPS service can maximize its benefit under a controlled risk.

A Human-centric and Environment-aware Testing Framework for Providing Safe and Reliable Cyber-Physical System Services

The functions, capabilities, and effects produced by the application services of cyber physical systems (CPS) are usually consumed by users performing their daily activities in a variety of environmental conditions. Thus, it is critical to ensure that those systems neither interfere with human activities nor harm the users involved. In this paper, we propose a framework for testing and verifying the safety and reliability of CPS services from the perspectives of CPS environments and users. The framework provides an environmentaware testing method by which the efficiency of testing CPS services can be improved by prioritizing CPS environments and by applying machinelearning techniques. The framework also includes a metric by which we can automate the test of the most effective services that deliver effects from physical devices to users. Additionally, the framework provides a computational model that assesses mental workloads to test whether a CPS service can cause cognitive depletion or contention problems for users. We conducted a series of experiments to show the effectiveness of the proposed approaches for ensuring the safety and reliability of CPS application services during the development and operation phases.

BLCS: Brain-Like Distributed Control Security in Cyber Physical Systems

The cyber-physical system (CPS) has operated, controlled, and coordinated the physical systems integrated by a computing and communication core applied in Industry 4.0. To accommodate CPS services, fog radio and optical networks (F-RON) has become an important supporting physical cyber infrastructure taking advantage of both the inherent ubiquity of wireless technology and the large capacity of optical networks. However, cyber security is the biggest issue in the CPS scenario as there is a trade-off between security control and privacy exposure in F-RON. To deal with this issue, we propose a brain-like distributed control security (BLCS) architecture for F-RON in CPS by introducing a brain-like security (BLS) scheme. BLCS can accomplish the secure cross-domain control among tripartite controllers verification in the scenario of decentralized F-RON for distributed computing and communications, which has no need to disclose the private information of each domain against cyber-attacks. BLS utilizes parts of information to perform control identification through a relation network and a deep learning of behavior library. The functional modules of BLCS architecture are illustrated including various controllers and a brain-like knowledge base. The interworking procedures in distributed control security modes based on BLS are described. The overall feasibility and efficiency of the architecture are experimentally verified on a software defined network testbed in terms of average mistrust rate, path provisioning latency, packet loss probability, and blocking probability. The emulation results are obtained and dissected based on the testbed.

Achieving Personalized $k$-Anonymity-Based Content Privacy for Autonomous Vehicles in CPS

Enabled by the industrial Internet, intelligent transportation has made remarkable achievements such as autonomous vehicles by carnegie mellon university (CMU) Navlab, Google Cars, Tesla, etc. Autonomous vehicles benefit, in various aspects, from the cooperation of the industrial Internet and cyber-physical systems. In this process, users in autonomous vehicles submit query contents, such as service interests or user locations, to service providers. However, privacy concerns arise since the query contents are exposed when the users are enjoying the services queried. Existing works on privacy preservation of query contents rely on location perturbation or $k$ -anonymity, and they suffer from insufficient protection of privacy or low query utility incurred by processing multiple queries for a single query content. To achieve sufficient privacy preservation and satisfactory query utility for autonomous vehicles querying services in cyber-physical systems, this article proposes a novel privacy notion of client-based personalized $k$ -anonymity (CP $k$ A). To measure the performance of CP $k$ A, we present a privacy metric and a utility metric, based on which, we formulate two problems to achieve the optimal CP $k$ A in term of privacy and utility. An approach, including two modules, to establish mechanisms which achieve the optimal CP $k$ A is presented. The first module is to build in-group mechanisms for achieving the optimal privacy within each content group. The second module includes linear programming-based methods to compute the optimal grouping strategies. The in-group mechanisms and the grouping strategies are combined to establish optimal CP $k$ A mechanisms, which achieve the optimal privacy or the optimal utility. We employ real-life datasets and synthetic prior distributions to evaluate the CP $k$ A mechanisms established by our approach. The evaluation results illustrate the effectiveness and efficiency of the established mechanisms.

A Personalized QoS Prediction Approach for CPS Service Recommendation Based on Reputation and Location-Aware Collaborative Filtering.

With the rapid development of cyber-physical systems (CPS), building cyber-physical systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedure of building Cyber-physical systems, it has been found that a large number of functionally equivalent services exist, so it becomes an urgent task to recommend suitable services from the large number of services available in CPS. However, since it is time-consuming, and even impractical, for a single user to invoke all of the services in CPS to experience their QoS, a robust QoS prediction method is needed to predict unknown QoS values. A commonly used method in QoS prediction is collaborative filtering, however, it is hard to deal with the data sparsity and cold start problem, and meanwhile most of the existing methods ignore the data credibility issue. Thence, in order to solve both of these challenging problems, in this paper, we design a framework of QoS prediction for CPS services, and propose a personalized QoS prediction approach based on reputation and location-aware collaborative filtering. Our approach first calculates the reputation of users by using the Dirichlet probability distribution, so as to identify untrusted users and process their unreliable data, and then it digs out the geographic neighborhood in three levels to improve the similarity calculation of users and services. Finally, the data from geographical neighbors of users and services are fused to predict the unknown QoS values. The experiments using real datasets show that our proposed approach outperforms other existing methods in terms of accuracy, efficiency, and robustness.

Towards energy efficient service composition in green energy powered Cyber–Physical Fog Systems

After decades of fast development in Cyber–Physical System (CPS), CPS services are becoming more and more ubiquitous. While, the still growing trends on ambient intelligence asks for more smart CPS applications. Meanwhile, fog computing may also exhibit advantage on the energy efficiency as the fog nodes are distributed in the environment and can harvest green energy from the environment. Fog computing has been regarded as an ideal platform for the distributed and diverse CPS applications. In this paper, we are motivated to investigate how to explore the energy generation diversity in fog computing platform to achieve energy efficient service composition in a green energy powered Cyber–Physical Fog System (CPFS), with the joint consideration of source rate control, load balancing and service replica deployment. The energy efficiency problem is formulated as a mixed integer linear programming (MILP) and proved as NP-hard. To address the computation complexity, we further propose a heuristic algorithm. Extensive simulation studies are conducted and the results show the high energy efficiency of our algorithm by the fact it performs much close to the optimal solution.

ОНТОЛОГИЧЕСКОЕ МОДЕЛИРОВАНИЕ УПРАВЛЯЮЩИХ АВТОМАТОВ СЕРВИСОВ КИБЕРФИЗИЧЕСКИХ СИСТЕМ

Introduction: Services of cyberphysical systems are supposed to be able not only to access Internet for information search, extraction and representation, but also to make decisions in accordance with the environment state. Due to computational limitations of such devices, it is considered to be promising to use state machines for their programming. However, the necessity of their interaction causes problems related to their interoperability support. Purpose: Our goal is ontological modelling of state machines for cyberphysical system services. Results: Usage of state machines for the development of cyberphysical system services has been proposed. A RDF-compatible ontological model of a state machine for a cyberphysical system service has been developed, based on a representation of the services, their states and output symbols as classes, and their input symbols as properties. The results are demonstrated via a case study of robot interaction for assembling a given word out of letters. Practical relevance: The obtained results can be used in the development of cyberphysical system services.

Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems.

Cyber-physical systems (CPS) have received much attention from both academia and industry. An increasing number of functions in CPS are provided in the way of services, which gives rise to an urgent task, that is, how to recommend the suitable services in a huge number of available services in CPS. In traditional service recommendation, collaborative filtering (CF) has been studied in academia, and used in industry. However, there exist several defects that limit the application of CF-based methods in CPS. One is that under the case of high data sparsity, CF-based methods are likely to generate inaccurate prediction results. In this paper, we discover that mining the potential similarity relations among users or services in CPS is really helpful to improve the prediction accuracy. Besides, most of traditional CF-based methods are only capable of using the service invocation records, but ignore the context information, such as network location, which is a typical context in CPS. In this paper, we propose a novel service recommendation method for CPS, which utilizes network location as context information and contains three prediction models using random walking. We conduct sufficient experiments on two real-world datasets, and the results demonstrate the effectiveness of our proposed methods and verify that the network location is indeed useful in QoS prediction.

Integrated scheduling of material flows and information services in industry 4.0 supply networks

In this contribution, an original service-oriented form of structure dynamics control (SDC) and a model for dynamic scheduling of services for Industry 4.0 supply networks is developed. Problem of design and dynamic scheduling of services in cyber-physical systems is stated and solved with the help of SDC approach. The proposed service-oriented description makes it possible simultaneously to (i) determine the volume of information services needed for physical supply processes, (ii) determine this volume in monetary form, and (iii) interconnect each service with information systems needed for its realization. The results provide a base for performing service scheduling according to actual physical process performance and their adaptation (re-configuration) as well as for monetary estimation of investments into information systems and return on investments. Copyright © 2015 IFAC

Crowdsourcing in Cyber-Physical Systems: Stochastic Optimization With Strong Stability

Cyber-physical systems (CPSs), featuring a tight combination of computational and physical elements as well as communication networks, attracted intensive attention recently because of their wide applications in various areas. In many applications, especially those aggregating or processing a large amount of data over large spatial regions or long spans of time or both, the workload would be too heavy for any CPS element (or node) to finish on its own. How to enable the CPS nodes to efficiently collaborate with each other to accommodate more CPS services is a very challenging problem and deserves systematic research. In this paper, we present a cross-layer optimization framework for hybrid crowdsourcing in the CPSs to facilitate heavy-duty computation. Particularly, by joint computing resource management, routing, and link scheduling, we formulate an offline finite-queue-aware CPS service maximization problem to crowdsource nodes' computing tasks in a CPS. We then find both lower and upper bounds on the optimal result of the problem. In addition, the lower bound result is proved to be a feasible result that guarantees all queues in the network are finite, i.e., network strong stability. Extensive simulations have been conducted to validate the proposed algorithms' performance.

A Novel Reliability Assurance Method for Cyberphysical System Components Substitution

Reliability of cyberphysical system (CPS) components substitution is an important issue for CPS troubleshooting and system upgrading. In this paper, decision problem of components substitution is regarded as decision problem of services substitution through a service-oriented architecture of CPS. Further, a reliability assurance method for CPS service substitution is proposed, which comprises two parts. The first one is a qualitative judgment method for CPS service substitution according to the relationship between service compatibility and substitution based on time-space -calculus with time and space operators. The other one is consisted of substitution processes from above judgment results based on service management theory. Finally, a case study is performed to show how to apply this method to ensure CPS components reliable substitution. The experimental result shows that this method is reasonable and feasible.

A novel capacity and trust based service selection mechanism for collaborative decision making in CPS

Cyber-physical system (CPS) provides more powerful service through combining software service and physical device. It is an effective solution to organize various CPS services to realize collaborative decision making (CDM). In CPS, finding out the most competent participators for CDM sponsor is a core problem. To solve this problem, we propose a novel capacity and trust computation based CPS service selection mechanism in intelligent and automatic manners. It comprises three phases, including capacity evaluation, trust computation and negotiation selection. In the first phase, CDM sponsor describes formal semantic of decision task and computes the capacity evaluation values according to participator instructions. In the second phase, we design a novel trust computation method to calculate the values of activity trust, subjective belief, objective reputation, physical trust and recommended trust respectively. In the third phase, service selection is achieved through a negotiation mechanism according to capacity evaluation and trust computation.