540 research outputs found

    Cyber-physical manufacturing systems: An architecture for sensor integration, production line simulation and cloud services

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    none9noThe pillars of Industry 4.0 require the integration of a modern smart factory, data storage in the Cloud, access to the Cloud for data analytics, and information sharing at the software level for simulation and hardware-in-the-loop (HIL) capabilities. The resulting cyber-physical system (CPS) is often termed the cyber-physical manufacturing system, and it has become crucial to cope with this increased system complexity and to attain the desired performances. However, since a great number of old production systems are based on monolithic architectures with limited external communication ports and reduced local computational capabilities, it is difficult to ensure such production lines are compliant with the Industry 4.0 pillars. A wireless sensor network is one solution for the smart connection of a production line to a CPS elaborating data through cloud computing. The scope of this research work lies in developing a modular software architecture based on the open service gateway initiative framework, which is able to seamlessly integrate both hardware and software wireless sensors, send data into the Cloud for further data analysis and enable both HIL and cloud computing capabilities. The CPS architecture was initially tested using HIL tools before it was deployed within a real manufacturing line for data collection and analysis over a period of two months.openPrist Mariorosario; Monteriu' Andrea; Pallotta Emanuele; Cicconi Paolo; Freddi Alessandro; Giuggioloni Federico; Caizer Eduard; Verdini Carlo; Longhi SauroPrist, Mariorosario; Monteriu', Andrea; Pallotta, Emanuele; Cicconi, Paolo; Freddi, Alessandro; Giuggioloni, Federico; Caizer, Eduard; Verdini, Carlo; Longhi, Saur

    Modelling and Analysis for Cyber-Physical Systems: An SMT-based approach

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    Modeling IoT-aware Business Processes - A State of the Art Report

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    This research report presents an analysis of the state of the art of modeling Internet of Things (IoT)-aware business processes. IOT links the physical world to the digital world. Traditionally, we would find information about events and processes in the physical world in the digital world entered by humans and humans using this information to control the physical world. In the IoT paradigm, the physical world is equipped with sensors and actuators to create a direct link with the digital world. Business processes are used to coordinate a complex environment including multiple actors for a common goal, typically in the context of administrative work. In the past few years, we have seen research efforts on the possibilities to model IoT- aware business processes, extending process coordination to real world entities directly. This set of research efforts is relatively small when compared to the overall research effort into the IoT and much of the work is still in the early research stage. To create a basis for a bridge between IoT and BPM, the goal of this report is to collect and analyze the state of the art of existing frameworks for modeling IoT-aware business processes.Comment: 42 page

    Trustee: A Trust Management System for Fog-enabled Cyber Physical Systems

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    In this paper, we propose a lightweight trust management system (TMS) for fog-enabled cyber physical systems (Fog-CPS). Trust computation is based on multi-factor and multi-dimensional parameters, and formulated as a statistical regression problem which is solved by employing random forest regression model. Additionally, as the Fog-CPS systems could be deployed in open and unprotected environments, the CPS devices and fog nodes are vulnerable to numerous attacks namely, collusion, self-promotion, badmouthing, ballot-stuffing, and opportunistic service. The compromised entities can impact the accuracy of trust computation model by increasing/decreasing the trust of other nodes. These challenges are addressed by designing a generic trust credibility model which can countermeasures the compromise of both CPS devices and fog nodes. The credibility of each newly computed trust value is evaluated and subsequently adjusted by correlating it with a standard deviation threshold. The standard deviation is quantified by computing the trust in two configurations of hostile environments and subsequently comparing it with the trust value in a legitimate/normal environment. Our results demonstrate that credibility model successfully countermeasures the malicious behaviour of all Fog-CPS entities i.e. CPS devices and fog nodes. The multi-factor trust assessment and credibility evaluation enable accurate and precise trust computation and guarantee a dependable Fog-CPS system

    Frost monitoring cyber-physical system: a survey on prediction and active protection methods

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    Frost damage in broadacre cropping and horticulture (including viticulture) results in substantial economic losses to producers and may also disrupt associated product value chains. Frost risk windows are changing in timing, frequency, and duration. Faced with the increasing cost of mitigation infrastructure and competition for resources (e.g., water and energy), multiperil insurance, and the need for supply chain certainty, producers are under pressure to innovate in order to manage and mitigate risk. Frost protection systems are cyber-physical systems (CPSs) consisting of sensors (event detection), intelligence (prediction), and actuators (active protection methods). The Internet-of-Things communication protocols joining the CPS components are also evaluated. In this context, this article introduces and reviews existing methods of frost management. This article focuses on active protection methods because of their potential for real-time deployment during frost events. For integrated frost prediction and active protection systems, prediction method, sensor types, and integration architecture are assessed, research gaps are identified and future research directions proposed

    A cyber-physical system for smart healthcare

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    Abstract: The increasing number of patients in hospitals is becoming a serious concern in most countries owing to the significantly associated implications for resources such as staff and budget shortages. This problem has prompted researchers to investigate low-cost alternative systems that may assist medical staff with monitoring and caring for patients. In view of the recent widespread availability of cost-effective internet of things (IoT) technologies such as ZigBee, WiFi and sensors integrated into cyber-physical systems, there is the potential for deployment as different topologies in applications such as patient diagnoses and remote patient monitoring...M.Tech. (Electrical and Electronic Engineering Technology

    Cyber-Physical Systems for Smart Water Networks: A Review

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    There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.acceptedVersio
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