Scalable networked systems: analysis and optimization

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School of Science | Doctoral thesis (article-based) | Defence date: 2020-02-28
Degree programme
82 + app. 64
Aalto University publication series DOCTORAL DISSERTATIONS, 24/2020
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.
Supervising professor
Di Francesco, Mario, Prof., Aalto University, Department of Computer Science, Finland
Thesis advisor
Taleb, Tarik, Prof., Aalto University, Department of Communications and Networking, Finland
wireless communications, optimization, edge computing, LoRa
Other note
  • [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:
    DOI: 10.1007/978-3-319-40352-6_11 View at publisher
  • [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:
    DOI: 10.1109/NOMS.2018.8406255 View at publisher
  • [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 View at publisher
  • [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:
    DOI: 10.1109/JIOT.2018.2805263 View at publisher
  • [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:
    DOI: 10.1109/NOMS.2018.8406256 View at publisher
  • [Errata file]: Errata of Publication P2