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dc.contributor.authorSaha, Dipon
dc.date.accessioned2020-08-31T13:15:57Z
dc.date.available2020-08-31T13:15:57Z
dc.identifier.urihttp://hdl.handle.net/10222/79768
dc.description.abstractSoftware-defined networking (SDN) and Network Function Virtualization (NFV) enable efficient network configuration and management in data centers and enterprises. SDN/NFV based design can also bring innovation in the wireless domain like low- power IoT networks with appropriate domain-specific protocol and architecture design. Low-power IoT devices have limited resources (e.g., power, CPU, memory) and operate in the presence of interference. Thus, in this thesis, we propose an energy-efficient interference-aware SDN/NFV framework for IoT networks. First, we formulate an Integer Linear Programming (ILP) problem to minimize the number of activated NFV nodes and the communication energy consumption. We assign IoT traffic sources to those NFVs over energy and interference-aware routes to minimize the network’s overall energy consumption. Then, we develop a heuristic for large IoT networks as the proposed ILP problem is NP-complete. To facilitate the heuristic implementation, we design an SDN/NFV node architecture. We solve the ILP problem using CPLEX and evaluate the heuristic in the Cooja simulator (Contiki OS). Extensive evaluation results over two types of topologies with varying traffic load and network size reveal that the proposed solution uses almost half the communication energy compared to the state-of-the-art schemes. It also offers significantly better packet delivery ratio and network lifetime compared to its counterparts with minimal control overhead.en_US
dc.language.isoenen_US
dc.subjectIoTen_US
dc.subjectSDNen_US
dc.subjectNFVen_US
dc.subjectEnergy-awareen_US
dc.titleAN ENERGY-EFFICIENT SDN/NFV FRAMEWORK FOR LOW-POWER IOT NETWORKSen_US
dc.date.defence2020-08-20
dc.contributor.departmentFaculty of Computer Scienceen_US
dc.contributor.degreeMaster of Computer Scienceen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorDR. MICHAEL MCALLISTERen_US
dc.contributor.thesis-readerDr. Raghav Sampangien_US
dc.contributor.thesis-readerDr. Qiang Yeen_US
dc.contributor.thesis-supervisorDr. Israat Haqueen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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