On the Provisioning of Ultra-Reliable Low-Latency Services in IoT Networks with Multipath Diversity

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dc.contributor.author Sweidan, Zahraa
dc.date.accessioned 2022-06-16T09:29:24Z
dc.date.available 2022-06-16T09:29:24Z
dc.date.copyright 2020 en_US
dc.date.issued 2020-12-18
dc.identifier.uri http://hdl.handle.net/10725/13720
dc.description.abstract The unprecedented increase in the number of smart connected devices invoked a plethora of diverse applications with different performance requirements stipulating various network management strategies. Ultra-reliable low-latency communication (URLLC), one of the promised 5G dimensions, is expected to enable mission-critical applications while adhering to different levels of reliability and latency requirements. At its core, URLLC rests on the notion of providing stringent reliability and latency requirements, in which guaranteed network availability becomes a necessity. In this thesis, we propose different approaches for providing URLLC services in two different contexts: wired and wireless network scenarios. Motivated by the fact that extreme reliability and lowest latency guarantees incur extremely high costs, we aim to provide these requirements using multipath diversity. However, providing such guarantees requires careful resource provisioning and management. In the fi rst part of this work, we utilize the notion of Network Slicing (NS), one of the key paradigms that can offer performance guarantees through customized network management of software defi ned networking (SDN) as well as path diversity. Multiple disjoint paths may be selected to ensure the reliability and latency requirements of supported URLLC-based applications. We formulate the problem as a mixed integer program and then propose a decomposition approach that can efficiently associate each application to its corresponding paths while meeting its strict quality demands. In the second part of this work, we leverage unmanned aerial vehicles (UAVs) as redundant flying edge servers to fulfi l the reliability and latency requirements of devices in a given IoT network. UAVs fly closer to devices which target reliability cannot be met by the serving base station (or access point) equipped with mobile edge computing (MEC) capabilities. We de ne the constraints of the problem, model it as a Markov Decision Process, and propose a reinforcement learning-based solution to optimize the UAV trajectory. Simulation results are presented for both parts of the thesis to illustrate the effectiveness of the proposed solutions and algorithms in comparison with optimal solutions and baseline algorithms. en_US
dc.language.iso en en_US
dc.subject 5G mobile communication systems en_US
dc.subject Computer networks -- Management en_US
dc.subject Drone aircraft -- Mathematical models en_US
dc.subject Internet of things -- Management en_US
dc.subject Lebanese American University -- Dissertations en_US
dc.subject Dissertations, Academic en_US
dc.title On the Provisioning of Ultra-Reliable Low-Latency Services in IoT Networks with Multipath Diversity en_US
dc.type Thesis en_US
dc.term.submitted Fall en_US
dc.author.degree MS in Computer Science en_US
dc.author.school SAS en_US
dc.author.idnumber 201505605 en_US
dc.author.commembers Mansour, Nashat
dc.author.commembers Harati, Ramzi
dc.author.department Computer Science And Mathematics en_US
dc.description.physdesc 1 online resource (xii, 68 leaves): col. ill. en_US
dc.author.advisor Sharafeddine, Sanaa
dc.keywords 5G networks en_US
dc.keywords URLLC en_US
dc.keywords Heterogeneous networks en_US
dc.keywords Network slicing en_US
dc.keywords Multipath diversity en_US
dc.keywords Bandwidth management en_US
dc.keywords Unmanned Aerial Vehicles en_US
dc.keywords MEC en_US
dc.description.bibliographiccitations Bibliography: leaf 60-68. en_US
dc.identifier.doi https://doi.org/10.26756/th.2022.194
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php en_US
dc.publisher.institution Lebanese American University en_US
dc.author.affiliation Lebanese American University en_US

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