The growth of the internet towards the Internet of Things (IoT) has impacted the way we live. Intelligent (smart) devices which can act autonomously has resulted in new applications for example industrial automation, smart healthcare systems, autonomous transportation to name just a few. These applications have dramatically improved the way we live as citizens. While the internet is continuing to grow at an unprecedented rate, this has also been coupled with the growing demands for new services e.g. machine-to machine (M2M) communications, smart metering etc. Transmission Control Protocol/Internet Protocol (TCP/IP) architecture was developed decades ago and was not prepared nor designed to meet these exponential demands. This has led to the complexity of the internet coupled with its inflexible and a rigid state. The challenges of reliability, scalability, interoperability, inflexibility and vendor lock-in amongst the many challenges still remain a concern over the existing (traditional) networks. In this study, an evolutionary approach into implementing a "Scalable IoT Data Transmission Network" (S-IoT-N) is proposed while leveraging on existing transport networks. Most Importantly, the proposed evolutionary approach attempts to address the above challenges by using open (existing) standards and by leveraging on the (traditional/existing) transport networks. The Proof-of-Concept (PoC) of the proposed S-IoT-N is attempted on a physical network testbed and is demonstrated along with basic network connectivity services over it. Finally, the results are validated by an experimental performance evaluation of the PoC physical network testbed along with the recommendations for improvement and future work.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/36073 |
| Date | 14 March 2022 |
| Creators | Sizamo, Yandisa |
| Contributors | Ramotsoela, Daniel |
| Publisher | Faculty of Engineering and the Built Environment, Department of Electrical Engineering |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Master Thesis, Masters, MSc (Eng) |
| Format | application/pdf |
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