18,906 research outputs found

    Active learning based laboratory towards engineering education 4.0

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    Universities have a relevant and essential key role to ensure knowledge and development of competencies in the current fourth industrial revolution called Industry 4.0. The Industry 4.0 promotes a set of digital technologies to allow the convergence between the information technology and the operation technology towards smarter factories. Under such new framework, multiple initiatives are being carried out worldwide as response of such evolution, particularly, from the engineering education point of view. In this regard, this paper introduces the initiative that is being carried out at the Technical University of Catalonia, Spain, called Industry 4.0 Technologies Laboratory, I4Tech Lab. The I4Tech laboratory represents a technological environment for the academic, research and industrial promotion of related technologies. First, in this work, some of the main aspects considered in the definition of the so called engineering education 4.0 are discussed. Next, the proposed laboratory architecture, objectives as well as considered technologies are explained. Finally, the basis of the proposed academic method supported by an active learning approach is presented.Postprint (published version

    Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

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    This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

    Recent advances in industrial wireless sensor networks towards efficient management in IoT

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    With the accelerated development of Internet-of- Things (IoT), wireless sensor networks (WSN) are gaining importance in the continued advancement of information and communication technologies, and have been connected and integrated with Internet in vast industrial applications. However, given the fact that most wireless sensor devices are resource constrained and operate on batteries, the communication overhead and power consumption are therefore important issues for wireless sensor networks design. In order to efficiently manage these wireless sensor devices in a unified manner, the industrial authorities should be able to provide a network infrastructure supporting various WSN applications and services that facilitate the management of sensor-equipped real-world entities. This paper presents an overview of industrial ecosystem, technical architecture, industrial device management standards and our latest research activity in developing a WSN management system. The key approach to enable efficient and reliable management of WSN within such an infrastructure is a cross layer design of lightweight and cloud-based RESTful web service

    Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT

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    Wireless sensor networks (WSN) are fundamental to the Internet of Things (IoT) by bridging the gap between the physical and the cyber worlds. Anomaly detection is a critical task in this context as it is responsible for identifying various events of interests such as equipment faults and undiscovered phenomena. However, this task is challenging because of the elusive nature of anomalies and the volatility of the ambient environments. In a resource-scarce setting like WSN, this challenge is further elevated and weakens the suitability of many existing solutions. In this paper, for the first time, we introduce autoencoder neural networks into WSN to solve the anomaly detection problem. We design a two-part algorithm that resides on sensors and the IoT cloud respectively, such that (i) anomalies can be detected at sensors in a fully distributed manner without the need for communicating with any other sensors or the cloud, and (ii) the relatively more computation-intensive learning task can be handled by the cloud with a much lower (and configurable) frequency. In addition to the minimal communication overhead, the computational load on sensors is also very low (of polynomial complexity) and readily affordable by most COTS sensors. Using a real WSN indoor testbed and sensor data collected over 4 consecutive months, we demonstrate via experiments that our proposed autoencoder-based anomaly detection mechanism achieves high detection accuracy and low false alarm rate. It is also able to adapt to unforeseeable and new changes in a non-stationary environment, thanks to the unsupervised learning feature of our chosen autoencoder neural networks.Comment: 6 pages, 7 figures, IEEE ICC 201

    A Proposed Architecture for Big Data Driven Supply Chain Analytics

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    Advancement in information and communication technology (ICT) has given rise to explosion of data in every field of operations. Working with the enormous volume of data (or Big Data, as it is popularly known as) for extraction of useful information to support decision making is one of the sources of competitive advantage for organizations today. Enterprises are leveraging the power of analytics in formulating business strategy in every facet of their operations to mitigate business risk. Volatile global market scenario has compelled the organizations to redefine their supply chain management (SCM). In this paper, we have delineated the relevance of Big Data and its importance in managing end to end supply chains for achieving business excellence. A Big Data-centric architecture for SCM has been proposed that exploits the current state of the art technology of data management, analytics and visualization. The security and privacy requirements of a Big Data system have also been highlighted and several mechanisms have been discussed to implement these features in a real world Big Data system deployment in the context of SCM. Some future scope of work has also been pointed out. Keyword: Big Data, Analytics, Cloud, Architecture, Protocols, Supply Chain Management, Security, Privacy.Comment: 24 pages, 4 figures, 3 table
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