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A Cost Efficient Design of a Multi-Sink Multi-ControllerWSN in a Smart Factory
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1384-5323
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-5590-0784
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-6132-7945
Stockholm University, Stockholm, Sweden.
2018 (English)In: Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017, 2018, p. 594-602Conference paper, Published paper (Refereed)
Abstract [en]

Internet of Things (IoT), one of the key elementsof a smart factory, is dubbed as Industrial IoT (IIoT). Softwaredefined networking is a technique that benefits network managementin IIoT applications by providing network reconfigurability.In this way, controllers are integrated within the networkto advertise routing rules dynamically based on network andlink changes. We consider controllers within Wireless SensorNetworks (WSNs) for IIoT applications in such a way to providereliability and timeliness. Network reliability is addressed for thecase of node failure by considering multiple sinks and multiplecontrollers. Real-time requirements are implicitly applied bylimiting the number of hops (maximum path-length) betweensensors and sinks/controllers, and by confining the maximumworkload on each sink/controller. Deployment planning of sinksshould ensure that when a sink or controller fails, the networkis still connected. In this paper, we target the challenge ofplacement of multiple sinks and controllers, while ensuring thateach sensor node is covered by multiple sinks (k sinks) andmultiple controllers (k' controllers). We evaluate the proposedalgorithm using the benchmark GRASP-MSP through extensiveexperiments, and show that our approach outperforms thebenchmark by lowering the total deployment cost by up to24%. The reduction of the total deployment cost is fulfilled notonly as the result of decreasing the number of required sinksand controllers but also selecting cost-effective sinks/controllersamong all candidate sinks/controllers.

Place, publisher, year, edition, pages
2018. p. 594-602
Keywords [en]
IoT
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:mdh:diva-36445DOI: 10.1109/HPCC-SmartCity-DSS.2017.77ISBN: 9781538625880 (print)OAI: oai:DiVA.org:mdh-36445DiVA, id: diva2:1142022
Conference
19th IEEE Intl Conference on High Performance Computing and Communications, 15th IEEE Intl Conference on Smart City, and 3rd IEEE Intl Conference on Data Science and Systems, HPCC/SmartCity/DSS 2017; Bangkok; Thailand; 18 December 2017 through 20 December 2017
Available from: 2017-09-18 Created: 2017-09-18 Last updated: 2018-06-07Bibliographically approved
In thesis
1. Optimizing Timing-Critical Cloud Resources in a Smart Factory
Open this publication in new window or tab >>Optimizing Timing-Critical Cloud Resources in a Smart Factory
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis addresses the topic of resource efficiency in the context of timing critical components that are used in the realization of a Smart Factory.The concept of the smart factory is a recent paradigm to build future production systems in a way that is both smarter and more flexible. When it comes to realization of a smart factory, three principal elements play a significant role, namely Embedded Systems, Internet of Things (IoT) and Cloud Computing. In a smart factory, efficient use of computing and communication resources is a prerequisite not only to obtain a desirable performance for running industrial applications, but also to minimize the deployment cost of the system in terms of the size and number of resources that are required to run industrial applications with an acceptable level of performance. Most industrial applications that are involved in smart factories, e.g., automation and manufacturing applications, are subject to a set of strict timing constraints that must be met for the applications to operate properly. Such applications, including underlying hardware and software components that are used to run the application, constitute a real-time system. In real-time systems, the first and major concern of the system designer is to provide a solution where all timing constraints are met. To do so we need a time-predictable IoT/Cloud Computing framework to deal with the real-time constraints that are inherent in industrial applications running in a smart factory. Afterwards, with respect to the time predictable framework, the number of required computing and communication resources can and should be optimized such that the deployed system is cost efficient. In this thesis, to investigate and present solutions that provide and improve the resource efficiency of computing and communication resources in a smart factory, we conduct research following three themes: (i) multi-core embedded processors, which are the key element in terms of computing components embedded in the machinery of a smart factory, (ii) cloud computing data centers, as the supplier of a massive data storage and a large computational power, and(iii) IoT, for providing the interconnection of computing components embedded in the objects of a smart factory. Each of these themes are targeted separately to optimize resource efficiency. For each theme, we identify key challenges when it comes to achieving a resource-efficient design of the system. We then formulate the problem and propose solutions to optimize the resource efficiency of the system, while satisfying all timing constraints reflected in the model. We then propose a comprehensive resource allocation mechanism to optimize the resource efficiency in the whole system while considering the characteristics of each of these research themes. The experimental results indicate a clear improvement when it comes to timing-critical IoT / Cloud Computing resources in a smart factory. At the level of multi-core embedded devices, the total CPU usage of a quad-core processor is shown to be improved by 11.2%. At the level of Cloud Computing, the number of cloud servers that are required to execute a given set of real-time applications is shown to be reduced by 25.5%. In terms of network components that are used to collect sensor data, our proposed approach reduces the total deployment cost of thesystem by 24%. In summary these results all contribute towards the realization of a future smart factory.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2018
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 255
Keywords
Cloud Computing; Fog Computing; Edge Computing; Real-Time Systems; Resource Allocation
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-38659 (URN)978-91-7485-376-6 (ISBN)
Public defence
2018-03-08, Gamma, Mälardalens högskola, Västerås, 13:30 (English)
Opponent
Supervisors
Available from: 2018-02-13 Created: 2018-02-12 Last updated: 2018-06-12Bibliographically approved

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Faragardi, Hamid RezaFotouhi, HosseinNolte, Thomas

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