Enabling Internet of Things Applications: An End-to-end Approach

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Kortoçi, Pranvera
dc.date.accessioned 2020-05-20T09:00:11Z
dc.date.available 2020-05-20T09:00:11Z
dc.date.issued 2020
dc.identifier.isbn 978-952-60-3926-8 (electronic)
dc.identifier.isbn 978-952-60-3925-1 (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/123456789/44210
dc.description The public defense on 10th June 2020 at 12:00 will be available via remote technology. Link: https://aalto.zoom.us/j/67819394930 Zoom Quick Guide: https://www.aalto.fi/en/services/zoom-quick-guide Electronic online display version of the doctoral thesis is available by email by request from aaltodoc-diss@aalto.fi
dc.description.abstract There is a massive amount of smart objects around us that interact with each other through Internet-based communication standards, forming the so-called Internet of Things (IoT). The scope of the IoT is quite wide and the related applications have diverse requirements in terms of security, data quality, and reliability. We consider different aspects of the IoT: taking an IoT device securely into use, establishing communication with an application server, and collecting as well as transmitting sensory data to remote data storage facilities (e.g., servers in the cloud). In fact, the IoT ecosystem and its immense device-generated data have given rise to several computing paradigms (e.g., cloud, edge, and fog) with different potential and means of sustaining the ever-growing IoT. This dissertation addresses the requirements of a secure and dependable IoT by taking an end-to-end approach. First, it proposes a light-weight mechanism for the initial configuration of network and security parameters to ensure secure bootstrapping of IoT devices. We specifically target a secure as well as a user-friendly IoT: in fact, our solution requires neither human intervention nor physical access to the device, and it incurs low power expenditure. Second, this dissertation addresses challenges in data collection due to the constrained resources available on IoT devices and limited availability of wireless bandwidth. To this end, we consider people- and agent-based data collection with different types of mobility (e.g., uncontrolled, semi-controlled, and fully-controlled). In particular, we leverage the fog computing paradigm and propose a protocol to offload data opportunistically from IoT end-devices to mobile gateways with unknown and uncontrolled mobility. Moreover, we investigate the impact of incentive mechanisms to ensure user participation in the collection of sensory data. To this end, we leverage the mobile edge computing paradigm and design a smart incentive mechanism for participatory crowdsourcing systems that increases the amount of collected data and maximizes the social welfare of the system. Additionally, we propose a communication protocol for ubiquitous wireless devices to disseminate data to mobile agents with fully-controlled mobility to assist search and rescue teams during disaster scenarios. We characterize the impact of our proposed protocol in extending the battery life of the devices and thus increasing the chances of assisting the survivors. Finally, this dissertation presents a light-weight data reduction mechanism that operates at gateways and edge tiers, supporting data-intensive IoT applications. Specifically, it performs filtering and fusion on time series data, thereby reducing the amount of data transmitted to a remote data center while retaining a high recovery accuracy with respect to the original data stream. en
dc.format.extent 91 + app. 58
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 88/2020
dc.relation.haspart [Publication 1]: Mohit Sethi, Pranvera Kortoçi, Mario Di Francesco, Tuomas Aura. Secure and Low-Power Authentication for Resource-Constrained Devices. In Proceedings of the 5th International Conference on the Internet of Things (IoT 2015), Seoul, pp. 30-36. DOI: 10.1109/IOT.2015.7356545
dc.relation.haspart [Publication 2]: Pranvera Kortoçi, Liang Zheng, Carlee Joe-Wong, Mario Di Francesco, Mung Chiang. Fog-based Data Offloading in Urban IoT Scenarios. In Proceedings of the 38th International Conference on Computer Communications (INFOCOM 2019), Paris, pp. 784-792. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201907304523. DOI: 10.1109/INFOCOM.2019.8737503
dc.relation.haspart [Publication 3]: Pranvera Kortoçi, Abbas Mehrabi, Carlee Joe-Wong, Mario Di Francesco. Location-aware Quality Data Collection with Mobile Crowdsourcing for Time-sensitive IoT Applications. Submitted, 2020
dc.relation.haspart [Publication 4]: Farouk Mezghani, Pranvera Kortoçi, Nathalie Mitton, Mario Di Francesco. A Multi-tier Communication Scheme for Drone-assisted Disaster Recovery Scenarios. In IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2019), Istanbul, pages 1-7. DOI: 10.1109/PIMRC.2019.8904140
dc.relation.haspart [Publication 5]: Liang Feng, Pranvera Kortoçi, Yong Liu. A Multi-tier Data Reduction Mechanism for IoT Sensors. In Proceedings of the 7th International Conference on the Internet of Things (IoT 2017), Linz, pages 1-8. DOI: 10.1145/3131542.3131557
dc.relation.haspart [Errata file]: Errata of P. 4
dc.subject.other Computer science en
dc.title Enabling Internet of Things Applications: An End-to-end Approach 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 internet of things en
dc.subject.keyword data collection protocols en
dc.subject.keyword cloud and fog computing en
dc.identifier.urn URN:ISBN:978-952-60-3926-8
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 Papadopouli, Maria, Prof., University of Crete, Greece
dc.rev Reinhardt, Delphine, Prof., University of Göttingen, Germany
dc.rev Abouzeid, Alhussein, Prof., Rensselaer Polytechnic Institute, USA
dc.date.defence 2020-06-10
local.aalto.formfolder 2020_05_19_klo_16_11
local.aalto.archive yes


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