abstract: Emerging from years of research and development, the Internet-of-Things (IoT) has finally paved its way into our daily lives. From smart home to Industry 4.0, IoT has been fundamentally transforming numerous domains with its unique superpower of interconnecting world-wide devices. However, the capability of IoT is largely constrained by the limited resources it can employ in various application scenarios, including computing power, network resource, dedicated hardware, etc. The situation is further exacerbated by the stringent quality-of-service (QoS) requirements of many IoT applications, such as delay, bandwidth, security, reliability, and more. This mismatch in resources and demands has greatly hindered the deployment and utilization of IoT services in many resource-intense and QoS-sensitive scenarios like autonomous driving and virtual reality.
I believe that the resource issue in IoT will persist in the near future due to technological, economic and environmental factors. In this dissertation, I seek to address this issue by means of smart resource allocation. I propose mathematical models to formally describe various resource constraints and application scenarios in IoT. Based on these, I design smart resource allocation algorithms and protocols to maximize the system performance in face of resource restrictions. Different aspects are tackled, including networking, security, and economics of the entire IoT ecosystem. For different problems, different algorithmic solutions are devised, including optimal algorithms, provable approximation algorithms, and distributed protocols. The solutions are validated with rigorous theoretical analysis and/or extensive simulation experiments. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019
| Identifer | oai:union.ndltd.org:asu.edu/item:54825 |
| Date | January 2019 |
| Contributors | Yu, Ruozhou (Author), Xue, Guoliang (Advisor), Huang, Dijiang (Committee member), Sen, Arunabha (Committee member), Zhang, Yanchao (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Doctoral Dissertation |
| Format | 236 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
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