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On Distributing a Visibility-based Emergency Stop System for Smart Industries
Mälardalen University, School of Innovation, Design and Engineering.
2021 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Emergency stop systems are extremely important in industries where machines and human operators share the work space. The environment is in need of emergency stop buttons that can shut down machines when dangerous situations occur. Traditionally, stop buttons are located on or near stationary machines and are made to stop a fixed set of machines. In smart industries, with moving vehicles and where working operators have access to moving stop buttons, it is harder to know what to stop. Instead, visibility-based emergency stops can be used to only stop the machines that are visible from the button's location. A server computes the visibility and sends stop signals to those machines that are visible when the button is pressed. However, the workload can be quite heavy for the server due to high numbers of machines and buttons. In this thesis I will explore how to distribute a visibility-based emergency stop system over multiple service instances with the purpose of providing an abstract distributed model. A distributed system would mean that the visibility computations are divided among the servers and thus provide faster response time when an emergency occurs, which in turn is a more reliable system. To answer my research questions, a literature study and an analysis of the visibility algorithm were made in order to propose an abstract distributed model. The model was implemented with the key element that the stop buttons are partitioned among the servers. Experimental results show that the system with multiple servers performs faster visibility calculations, in terms of response time, than a system running with one server. The results obtained in this thesis are beneficial for other safety-critical system as well as systems processing large data sets.

Place, publisher, year, edition, pages
2021. , p. 21
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-54926OAI: oai:DiVA.org:mdh-54926DiVA, id: diva2:1568888
Subject / course
Computer Science
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Available from: 2021-06-18 Created: 2021-06-18 Last updated: 2021-06-18Bibliographically approved

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fulltext(631 kB)232 downloads
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CiteExportLink to record
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Citation style
  • apa
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