Cloud-based Cyber-physical Systems Research Articles

A framework for throughput bottleneck analysis using cloud-based cyber-physical systems in Industry 4.0 and smart manufacturing

The performance of a production system is primarily evaluated by its throughput, which is constrained by throughput bottlenecks. Thus, bottleneck analysis (BA), encompassing bottleneck identification, diagnosis, prediction, and prescription, is a crucial analytical process contributing to the success of manufacturing industries. Nevertheless, BA requires a substantial quantity of information from the manufacturing system, making it a data-intensive task. Based on the dynamic nature of bottlenecks, the optimal strategy for BA entails making well-informed decisions in real-time and executing necessary modifications accordingly. The efficient implementation of BA requires gathering, storing, analyzing, and illustrating data from the shop floor. Utilizing Industry 4.0 technologies, such as cyber-physical systems and cloud technology, facilitates the execution of data-intensive operations for the successful management of BA in real-world settings. The main objective of this study is to establish a framework for BA through the utilization of Cloud-Based Cyber-Physical Systems (CB-CPSs). First, a literature review was conducted to identify relevant research and current applications of CB-CPSs in BA. Using the results of the review, a CB-CPSs framework was subsequently introduced for BA. The application of the framework was assessed via simulation in a real-world manufacturer of marine engines. The findings indicate that the implementation of CB-CPSs can contribute significantly to throughput improvement.

Swarm intelligence for IoT attack detection in fog-enabled cyber-physical system

To provide remote access, surveillance, and analysis, network integration is common in Cyber-Physical Systems (CPSs). This leads to cyber attacks due to the integration of insecure networking devices. In violating internet security, the attackers interfere with system function, which leads to shattering consequences. With the incorporation of Fog with IoT, the attacks in the CPS can be detected in less time than in cloud-based CPS. The detection of attacks on CPS is targeted by cybercriminals which increases the identification difficulty. This paper proposes a new swarm-based feature selection algorithm to improve attack detection in an IoT-based CPS environment. An Enhanced Chicken swarm optimization (ECSO) with self-learning ability-based feature selection is used to select the relevant features from the preprocessed data. Next, the ensemble classifiers are executed with the desired features on the cloud. The proposed ECSO-based ensemble classifier has experimented against the NSL-KDD dataset. The evaluated results show the adequate performance of the proposed system using various statistical measures.

EB-CSPA: efficient blockchain-based certificateless short signature public auditing scheme for cloud-based cyber-physical systems

With the widespread of digital information and advanced internet technologies, cyber-physical systems (CPS) generate numerous data. Due to the limited storage and computing capacity, CPS devices outsource data to cloud servers. When the data is outsourced to cloud servers, users lose control of their data. Several traditional and blockchain auditing schemes were proposed to verify the integrity of the data. Unfortunately, these existing schemes suffer from key escrow problem, and they are unsuitable for CPS devices as they involve substantial computation costs. This paper proposes an efficient blockchain-based certificateless short signature public auditing scheme for cloud-based CPSs, which uses blockchain technology to verify the integrity of the data. Further, our scheme requires only one pairing operation and two hash functions in the verification process. The performance analysis demonstrates that the proposed scheme outperforms and satisfies low-bandwidth device requirements.

Cloud-based Cyber-Physical Logistics System with Nested MAX-MIN Ant Algorithm for E-commerce logistics

E-commerce logistics have required rapid and efficient e-fulfillment services to deliver goods to consumers within a short time window to fulfill the customer order requirement. This paper seeks to address how an E-commerce Logistics Company can utilize the Cyber-Physical Logistics System (CPLS) and Internet of Things (IoT), which may unlock the potential for solving the order assignments and the vehicle routing problems on a new twist. The CPLS allocates orders to suitable vehicles and plans the best route in order to maximize the order fulfillment rate through cloud computing to reduce local computation power. This paper presents a novel Nested MAX-MIN Ant System (MMAS) to tackle a Vehicle Routing Problem (VRP) that can serve as the core element of the CPLS. The objective is to perform to maximize the orders, minimize the total traveling cost and satisfy all the problem constraints. This paper intends to address the issues associated with the VIP priority and drop-off point limitations to make the solution route more realistic and enhance accuracy. The numerical experiences show that the CPLS is implemented successfully in a Hong Kong-based E-commerce Logistics Company with promising performance within reasonable computation times and new better solutions.

A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing

With the advancement of additive manufacturing (AM), or 3D printing technology, manufacturing industries are driving towards Industry 4.0 for dynamic changed in customer experience, data-driven smart systems, and optimized production processes. This has pushed substantial innovation in cyber-physical systems (CPS) through the integration of sensors, Internet-of-things (IoT), cloud computing, and data analytics leading to the process of digitization. However, computer-aided design (CAD) is used to generate G codes for different process parameters to input to the 3D printer. To automate the whole process, in this study, a customer-driven CPS framework is developed to utilize customer requirement data directly from the website. A cloud platform, Microsoft Azure, is used to send that data to the fused diffusion modelling (FDM)-based 3D printer for the automatic printing process. A machine learning algorithm, the multi-layer perceptron (MLP) neural network model, has been utilized for optimizing the process parameters in the cloud. For cloud-to-machine interaction, a Raspberry Pi is used to get access from the Azure IoT hub and machine learning studio, where the generated algorithm is automatically evaluated and determines the most suitable value. Moreover, the CPS system is used to improve product quality through the synchronization of CAD model inputs from the cloud platform. Therefore, the customer’s desired product will be available with minimum waste, less human monitoring, and less human interaction. The system contributes to the insight of developing a cloud-based digitized, automatic, remote system merging Industry 4.0 technologies to bring flexibility, agility, and automation to AM processes.

Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System

As cyberattacks develop in volume and complexity, machine learning (ML) was extremely implemented for managing several cybersecurity attacks and malicious performance. The cyber-physical systems (CPSs) combined the calculation with physical procedures. An embedded computer and network monitor and control the physical procedure, commonly with feedback loops whereas physical procedures affect calculations and conversely, at the same time, ML approaches were vulnerable to data pollution attacks. Improving network security and attaining robustness of ML determined network schemes were the critical problems of the growth of CPS. This study develops a new Stochastic Fractal Search Algorithm with Deep Learning Driven Intrusion Detection system (SFSA-DLIDS) for a cloud-based CPS environment. The presented SFSA-DLIDS technique majorly focuses on the recognition and classification of intrusions for accomplishing security from the CPS environment. The presented SFSA-DLIDS approach primarily performs a min-max data normalization approach to convert the input data to a compatible format. In order to reduce a curse of dimensionality, the SFSA technique is applied to select a subset of features. Furthermore, chicken swarm optimization (CSO) with deep stacked auto encoder (DSAE) technique was utilized for the identification and classification of intrusions. The design of a CSO algorithm majorly focuses on the parameter optimization of the DSAE model and thereby enhances the classifier results. The experimental validation of the SFSA-DLIDS model is tested using a series of experiments. The experimental results depict the promising performance of the SFSA-DLIDS model over the recent models.

Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality. The privacy of health data can only be preserved by keeping it in an encrypted form, but it affects usability and flexibility in terms of effective search. Attribute-based searchable encryption (ABSE) has proven its worth by providing fine-grained searching capabilities in the shared cloud storage. However, it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious computations. In a healthcare cloud-based cyber-physical system (CCPS), the data is often collected by resource-constraint devices; therefore, here also, we cannot directly apply ABSE schemes. In the proposed work, the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain network. Thus, it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical CCPS. With the assistance of blockchain technology, the proposed scheme offers two main benefits. First, it is free from a trusted authority, which makes it genuinely decentralized and free from a single point of failure. Second, it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain network. Specifically, the task of initializing the system, which is considered the most computationally intensive, and the task of partial search token generation, which is considered as the most frequent operation, is now the responsibility of the consensus nodes. This eliminates the need of the trusted authority and reduces the burden of data users, respectively. Further, in comparison to existing decentralized fine-grained searchable encryption schemes, the proposed scheme has achieved a significant reduction in storage and computational cost for the secret key associated with users. It has been verified both theoretically and practically in the performance analysis section.

Data-driven order correlation pattern and storage location assignment in robotic mobile fulfillment and process automation system

With the rapid development and implementation of ICT, academics and industrial practitioners are widely applying robotic process automation (RPA) to enhance their business processes and operational efficiencies. This paper intends to address the value creation of utilizing RPA under the cloud-based Cyber-Physical Systems (CPS) in Robotic Mobile Fulfillment System (RMFS). By providing a TO-BE analysis of RPA and cloud-based CPS framework, a data-driven approach is proposed for zone clustering and storage location assignment classification in RMFS. The purpose of the paper is to gain better operational efficiency in RMFS. A modified A* algorithm is adopted for calculating the total traveling cost of each moveable rack in the case company layout. Nine common clustering algorithms are applied for the RMFS’s zone clustering. The results from the zone clustering are considered as nine scenarios for data-driven order classification to solve the storage location assignment problem. Six common classification algorithms are applied for a detailed comparison which has been conducted with thousands of orders. The results reveal that K-means, Gaussian Mixture Models, and Bayesian Gaussian Mixture Model are worked well with six supervised classification algorithms which yield an average of 95% accuracy rate and a higher customers’ expectation can be achieved under the customer-driven e-commerce economy.

Dynamic Response Analysis of Gearbox to Improve Fault Detection Using Empirical Mode Decomposition and Artificial Neural Network Techniques

Abstract Since the last decade, gearbox systems have been requiring increasing power, and consequently, the complexity of systems has escalated. Inevitably, this complexity has resulted in the need for the troubleshooting of gearbox systems. With a growing trend of health monitoring in rotating machines, diagnostic and prognostic studies have become focused on diagnosing existing and potential failures in gearbox systems. In this context, this study develops the architecture of the cloud-based cyber-physical system (CPS) for condition monitoring of gearbox. Empirically collected vibration signals of gear wear at various time intervals are processed using empirical mode decomposition (EMD) algorithm. A Euclidian-based distance evaluation technique is applied to select the most sensitive features of car gear wear. Artificial neural network (ANN) is trained using extracted features to monitor the gearbox for the future dataset. Comparison of the performance results revealed that the ANN is superior to the other EMD methods. The present methodology was found efficient and reliable for condition monitoring of industrial gearbox.

Optimal task replication considering reliability, performance, and energy consumption for parallel computing in cloud systems

In a cloud-based cyber–physical system, many jobs consist of multiple parallel tasks. The cloud system usually adopts active task replication to improve performance and guarantee the reliability of a job. This technology creates redundant replicas for each task and then executes the replicas concurrently. In the cloud system, each replica is a virtual machine (VM) image that can be easily assigned to different physical machines (PMs) to overcome resource heterogeneity. However, how to design a rational task replication strategy (including replica creation and VM assignment) is indeed a complex issue. It should comprehensively consider correlations and tradeoffs among reliability, performance, and energy consumption. This paper first proposes a reliability–performance correlation model for a job executed by using active task replication. We design a general method to avoid analyzing complex failure correlations and give a Bayesian approach to calculate the performability metric of the job. The paper also proposes a reliability–energy correlation model to evaluate random energy consumption of a PM hosting multiple VMs by using mixed random variables. Finally, an expected net profit optimization model and a genetic algorithm are developed to search for an optimal task replication strategy balancing tradeoffs among reliability, performance, and energy consumption. Illustrative examples are provided.

Energy and SLA-driven MapReduce Job Scheduling Framework for Cloud-based Cyber-Physical Systems

Energy consumption minimization of cloud data centers (DCs) has attracted much attention from the research community in the recent years; particularly due to the increasing dependence of emerging Cyber-Physical Systems on them. An effective way to improve the energy efficiency of DCs is by using efficient job scheduling strategies. However, the most challenging issue in selection of efficient job scheduling strategy is to ensure service-level agreement (SLA) bindings of the scheduled tasks. Hence, an energy-aware and SLA-driven job scheduling framework based on MapReduce is presented in this article. The primary aim of the proposed framework is to explore task-to-slot/container mapping problem as a special case of energy-aware scheduling in deadline-constrained scenario. Thus, this problem can be viewed as a complex multi-objective problem comprised of different constraints. To address this problem efficiently, it is segregated into three major subproblems (SPs), namely, deadline segregation, map and reduce phase energy-aware scheduling. These SPs are individually formulated using Integer Linear Programming. To solve these SPs effectively, heuristics based on Greedy strategy along with classical Hungarian algorithm for serial and serial-parallel systems are used. Moreover, the proposed scheme also explores the potential of splitting Map/Reduce phase(s) into multiple stages to achieve higher energy reductions. This is achieved by leveraging the concepts of classical Greedy approach and priority queues. The proposed scheme has been validated using real-time data traces acquired from OpenCloud. Moreover, the performance of the proposed scheme is compared with the existing schemes using different evaluation metrics, namely, number of stages, total energy consumption, total makespan, and SLA violated. The results obtained prove the efficacy of the proposed scheme in comparison to the other schemes under different workload scenarios.

Energy Consumption in a Data Control Center of a Cloud-based Cyber-Physical System

Cloud computing and Cyber-physical system (CPS) have gradually emerged as new generation of digital technologies with important research fields in recent times. With the merger of Cloud computing and CPS to form a Cloud-based cyber physical system (CCPS), more functionalities are realized and the performance of CPS is improved when compared to the traditional CPS. However, there are quite some energy challenges in CCPS especially in the data control center. In this work, the energy consumed in the data control center is evaluated while creating a strategy for resource utilization in improving energy efficiency. Simulation results highlight the effects the number of workloads has on the energy consumption of the data control center and also show the energy efficiency of the data control center with different resource utilization at an increasing number of workloads.

Secure and Optimized Cloud-Based Cyber-Physical Systems with Memory-Aware Scheduling Scheme

Computer based algorithms are used for monitoring and controlling a computers where human, physical, digital and analog interaction occurs. This scheme termed as cyber-physical systems (CPS) is being deployed in a wide array of applications due to its easy deployment feature and connectivity. CPS based on cloud computing technology offers wider application range with enormous amount of storage and computing resources. However, despite the wide application and deployment of CPS in combining the key technologies like big data analytics, cloud computing and IoT, its energy consumption is large. Optimization of this energy is a major field of research and is of due importance. For this purpose, in cloud environment, virtual machines (VMs) are used for hosting the applications and the resources are managed thereby optimizing the energy consumption. The security issues and the quality of service (QoS) requirements of the system is also met by optimizing the system design. The scheduling issue of the system is addressed by implementation of efficient memory-aware scheduling strategy and algorithms. The proposed technique is tested for performance and the results of simulation is presented.

Cloud-Based Cyber-Physical Robotic Mobile Fulfillment Systems: A Case Study of Collision Avoidance

The rapid development and implementation of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) in the engineering and manufacturing field have embraced a virtual identity to ensure nearly real-time adjustment. Warehouses are challenged to reassess its order fulfillment operations while simultaneously being provided with the opportunity to develop its own cloud-based CPS with the aid of IoT devices. Robotic Mobile Fulfillment System (RMFS) is a system controlling mobile robots, mobile storage rack, putaway and picking workstations, charging stations, and wireless communication infrastructure in the context of robotic-assisted warehouse. This paper addresses the value creation utilizing cloud-based CPS in RMFS. By providing an analysis of cloud services and IoT enhancement, theoretical concepts from the literatures are consolidated to solve the research que-stions on how RMFS offering better order fulfillment can gain benefits in terms of operational efficiency and system reliability. The paper also proposes a cloud-based CPS architecture, providing a comprehensive understanding on conflict avoidance strategy in the multi-layers multi-deeps warehouse layout. This research presents six conflict classifications in RMFS and provides a case study in the real-life context. Dock grid conflict is a new type of conflict appearing in multi-deeps RMFS. A scenario analysis with real customer orders is applied to present the collision detection and solution.

Artificial Intelligence-based Decision-Making Algorithms, Industrial Big Data, and Smart Connected Sensors in Cloud-based Cyber-Physical Manufacturing Systems

Artificial Intelligence-based Decision-Making Algorithms, Industrial Big Data, and Smart Connected Sensors in Cloud-based Cyber-Physical Manufacturing Systems

Cloud based Cyber Physical Systems Security Issues: A Survey

Cloud based Cyber Physical Systems Security Issues: A Survey

Privacy Preservation in Cloud Based Cyber Physical Systems

The Cyber physical systems (CPS) are integration of tightly coupled resource constrained physical and computational systems,which have several real time critical care applications. These systems depend on sensors to sense the physical environment. To overcome the limitations of computational power, CPS are supported by cloud computing systems as their backend. The enablement of cloud support to cyber physical cloud computing systemshave seamless advantages of improved efficiency, in same way this leads to major security and privacy concerns. These systems have decentralized and heterogeneous infrastructure and to fulfill all the security requirements, it is certainly a very complex task. In this paper we propose a generalized system model for securing different types of cloud based cyber physical systems, adversary model and holistic literature review of various CPS services, attacks, security challenges and the security schemes that can be applied to overcome a given attacks. At the end of the paper we discuss a case study on securing Medical CPS and compare the applicability of security schemes in this scenario.

Cloud based cyber-physical systems: Network evaluation study

In recent years, the Industry 4.0 concept brings new demands and trends in different areas; one of them is distributing computational power to the cloud. This concept also introduced the Reference Architectural Model for Industry 4.0 (RAMI 4.0). The efficiency of data communications within the RAMI 4.0 model is a critical issue. Aiming to evaluate the efficiency of data communication in the Cloud Based Cyber-Physical Systems (CB-CPS), this study analyzes the periods and data amount required to communicate with individual hierarchy levels of the RAMI 4.0 model. The evaluation of the network properties of the communication protocols eligible for CB-CPS is presented. The network properties to different cloud providers and data centers’ locations have been measured and interpreted. To test the findings, an architecture for cloud control of laboratory model was proposed. It was found that the time of the day; the day of the week; and data center utilization have a negligible impact on latency. The most significant impact lies in the data center distance and the speed of the communication channel. Moreover, the communication protocol also has impact on the latency. The feasibility of controlling each level of RAMI 4.0 through cloud services was investigated. Experimental results showed that control is possible in many solutions, but these solutions mostly cannot depend just on cloud services. The intelligence on the edge of the network will play a significant role. The main contribution is a thorough evaluation of different cloud providers, locations, and communication protocols to provide recommendations sufficient for different levels of the RAMI 4.0 architecture.

Cyber-physical application monitoring across multiple clouds

Cyber-physical systems (CPS) integrate cyber-infrastructure comprising computers and networks with physical processes. The cyber components monitor, control, and coordinate the physical processes typically via actuators. As CPS are characterized by reliability, availability, and performance, they are expected to have a tremendous impact not only on industrial systems but also in our daily lives. We have started to witness the emergence of cloud-based CPS. However, cloud systems are prone to stochastic conditions that may lead to quality of service degradation. In this paper, we propose M2CPA - a novel framework for multi-virtualization, and multi-cloud monitoring in cloud-based cyber-physical systems. M2CPA monitors the performance of application components running inside multiple virtualization platforms deployed on multiple clouds. M2CPA is validated through extensive experimental analysis using a real testbed comprising multiple public clouds and multi-virtualization technologies.

A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems

Nowadays, with the development of cyber-physical systems (CPS), there are an increasing amount of applications deployed in the CPS to connect cyber space with physical world better and closer than ever. Furthermore, the cloud-based CPS bring massive computing and storage resource for CPS, which enables a wide range of applications. Meanwhile, due to the explosive expansion of applications deployed on the CPS, the energy consumption of the cloud-based CPS has received wide concern. To improve the energy efficiency in the cloud environment, the virtualized technology is employed to manage the resources, and the applications are generally hosted by virtual machines (VMs). However, it remains challenging to meet the Quality-of-Service (QoS) requirements. In view of this challenge, a QoS-aware VM scheduling method for energy conservation, named QVMS, in cloud-based CPS is designed. Technically, our scheduling problem is formalized as a standard multi-objective problem first. Then, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is adopted to search the optimal VM migration solutions. Besides, SAW (Simple Additive Weighting) and MCDM (Multiple Criteria Decision Making) are employed to select the most optimal scheduling strategy. Finally, simulations and experiments are conducted to verify the effectiveness of our proposed method.