Please use this identifier to cite or link to this item: https://knowledgecommons.lakeheadu.ca/handle/2453/4776
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dc.contributor.advisorFadlullah, Zubair
dc.contributor.authorSakib, Sadman
dc.date.accessioned2021-04-30T16:02:10Z
dc.date.available2021-04-30T16:02:10Z
dc.date.created2021
dc.date.issued2021
dc.identifier.urihttp://knowledgecommons.lakeheadu.ca/handle/2453/4776
dc.description.abstractThe advent of the 5G and Beyond 5G (B5G) communication system, along with the proliferation of the Internet of Things (IoT) and Artificial Intelligence (AI), have started to evolve the vision of the smart world into a reality. Similarly, the Internet of Medical Things (IoMT) and AI have introduced numerous new dimensions towards attaining intelligent and connected mobile health (mHealth). The demands of continuous remote health monitoring with automated, lightweight, and secure systems have massively escalated. The AI-driven IoT/IoMT can play an essential role in sufficing this demand, but there are several challenges in attaining it. We can look into these emerging hurdles in IoT from three directions: the sensing layer, the communication layer, and the computing layer. Existing centralized remote cloud-based AI analytics is not adequate for solving these challenges, and we need to emphasize bringing the analytics into the ultra-edge IoT. Furthermore, from the communication perspective, the conventional techniques are not viable for the practical delivery of health data in dynamic network conditions in 5G and B5G network systems. Therefore, we need to go beyond the traditional realm and press the need to incorporate lightweight AI architecture to solve various challenges in the three mentioned IoT planes, enhancing the healthcare system in decision making and health data transmission. In this thesis, we present different AI-enabled techniques to provide practical and lightweight solutions to some selected challenges in the three IoT planes.en_US
dc.language.isoen_USen_US
dc.subjectIoT sensor technologyen_US
dc.subjectMachine learningen_US
dc.subjectDeep learningen_US
dc.subjectArtificial neural networksen_US
dc.subjectMobile health (mHealth)en_US
dc.subjectReservoir Computingen_US
dc.titleTowards designing AI-aided lightweight solutions for key challenges in sensing, communication and computing layers of IoT: smart health use-casesen_US
dc.typeThesisen_US
etd.degree.nameMaster of Scienceen_US
etd.degree.levelMasteren_US
etd.degree.disciplineComputer Scienceen_US
etd.degree.grantorLakehead Universityen_US
Appears in Collections:Electronic Theses and Dissertations from 2009

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