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Scalable networked systems: analysis and optimization

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Taleb, Tarik, Prof., Aalto University, Department of Communications and Networking, Finland
dc.contributor.author Premsankar, Gopika
dc.date.accessioned 2020-02-12T10:01:06Z
dc.date.available 2020-02-12T10:01:06Z
dc.date.issued 2020
dc.identifier.isbn 978-952-60-8947-8 (electronic)
dc.identifier.isbn 978-952-60-8946-1 (printed)
dc.identifier.issn 1799-4942 (electronic)
dc.identifier.issn 1799-4934 (printed)
dc.identifier.issn 1799-4934 (ISSN-L)
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/43033
dc.description.abstract The communication networks of today are evolving to support a large number of heterogeneous Internet-connected devices. Several emerging applications and services rely on the growing amount of device-generated data; however, such applications place diverse requirements on the network. For instance, interactive applications such as augmented reality demand very low latency for a satisfactory user experience. On the other hand, automotive applications require very reliable transmission and processing of data to ensure that accidents do not occur. To this end, new technologies have been proposed in the communication network to address these diverse connectivity and computational requirements. In the radio access networks, secondary access technologies comprising WiFi and white space are used to supplement cellular connectivity. New classes of radio technologies, such as long range (LoRa), have emerged for connecting resource-constrained devices. Additionally, processing and storage resources are being placed closer to the end-devices to efficiently process their data under the edge computing paradigm. This dissertation investigates the scalability of communication networks through intelligent network design, analysis, and management. In particular, it proposes novel solutions to manage different components in a network. The overall goal is to ensure that communication networks efficiently support both the connectivity and the computing requirements of a large number of heterogeneous devices. First, we investigate the role of secondary access networks in providing scalable connectivity to devices. Specifically, we propose new algorithms to maximize the traffic that is offloaded to white space and WiFi, thereby resulting in significantly more capacity in the cellular spectrum. Next, we investigate Long Range (LoRa) communications to enable large-scale connectivity for resource-constrained and battery-powered devices. In particular, we propose novel optimization models to manage the LoRa communication parameters to support reliable communications from massive densities of such devices in urban areas. Finally, we investigate the deployment of a communication infrastructure with edge computing capabilities to efficiently process large volumes of device-generated data. In particular, we experimentally characterize the impact of edge computing in supporting data-intensive applications. Additionally, we present a novel approach to optimally place edge devices in an urban environment to support both connectivity of cars and reliable processing of their data. en
dc.format.extent 82 + app. 64
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartofseries Aalto University publication series DOCTORAL DISSERTATIONS en
dc.relation.ispartofseries 24/2020
dc.relation.haspart [Publication 1]: Suzan Bayhan, Gopika Premsankar, Mario Di Francesco, Jussi Kangasharju. Mobile Content Offloading in Database-Assisted White Space Networks. In International Conference on Cognitive Radio Oriented Wireless Networks (CrownCom 2016), pp. 1-9, May 2016. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202002132220. DOI: 10.1007/978-3-319-40352-6_11
dc.relation.haspart [Publication 2]: Mariusz Slabicki, Gopika Premsankar, Mario Di Francesco. Adaptive Configuration of LoRa Networks for Dense IoT Deployments. In 2018IEEE/IFIP Network Operations and Management Symposium (2018NOMS), Taipei, Taiwan, pp. 1-9, Apr 2018. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201812105973. DOI: 10.1109/NOMS.2018.8406255
dc.relation.haspart [Publication 3]: Gopika Premsankar, Bissan Ghaddar, Mariusz Slabicki, Mario DiFrancesco. Optimal configuration of LoRa networks in smart cities. Accepted for publication in IEEE Transactions on Industrial Informatics, pp. 1-12, Jan 2020. DOI: 10.1109/TII.2020.2967123
dc.relation.haspart [Publication 4]: Gopika Premsankar, Mario Di Francesco, Tarik Taleb. Edge Computing for the Internet of Things: A Case Study. IEEE Internet of Things Journal, 2018, vol. 5, no.2, pp. 1275-1284. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201803161754. DOI: 10.1109/JIOT.2018.2805263
dc.relation.haspart [Publication 5]: Gopika Premsankar, Bissan Ghaddar, Mario Di Francesco, Rudi Verago. Efficient Placement of Edge Computing Devices for Vehicular Applications in Smart Cities. In 2018 IEEE/IFIP Network Operations and Management Symposium (2018 NOMS), Taipei, Taiwan, pp. 1-9, Apr 2018. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201812106054. DOI: 10.1109/NOMS.2018.8406256
dc.relation.haspart [Errata file]: Errata of Publication P2
dc.subject.other Computer science en
dc.subject.other Telecommunications engineering en
dc.title Scalable networked systems: analysis and optimization en
dc.type G5 Artikkeliväitöskirja fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.contributor.school School of Science en
dc.contributor.department Tietotekniikan laitos fi
dc.contributor.department Department of Computer Science en
dc.subject.keyword wireless communications en
dc.subject.keyword optimization en
dc.subject.keyword edge computing en
dc.subject.keyword LoRa en
dc.identifier.urn URN:ISBN:978-952-60-8947-8
dc.type.dcmitype text en
dc.type.ontasot Doctoral dissertation (article-based) en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.contributor.supervisor Di Francesco, Mario, Prof., Aalto University, Department of Computer Science, Finland
dc.opn Cuomo, Francesca, Prof., Sapienza University of Rome, Italy
dc.rev Levorato, Marco, Prof., University of California - Irvine, USA
dc.rev Pollin, Sofie, Prof., KU Leuven, Belgium
dc.date.defence 2020-02-28
local.aalto.acrisexportstatus checked 2020-03-21_1000
local.aalto.infra Science-IT
local.aalto.formfolder 2020_02_11_klo_14_40
local.aalto.archive yes

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