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Title
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Adaptive management of Wi-Fi networks in dynamic and heterogeneous environments
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Author
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Abstract
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| The last two decades brought a phenomenal increase in communication devices, mobile and stationary. The overall number of connected devices went past 18 billion with many available technologies. Two wireless technologies dominate the market space: LTE/5G and Wi-Fi. Other new wireless technologies also rose in popularity. This situation gave rise to new applications that require high bandwidth and low latency. However, technologies share use cases, devices can not fully use their technologies, and technologies can interfere with each other. Avoiding interference and enabling technology cooperation are major cornerstones of improving user experience. Nevertheless, cooperation can not overcome all obstacles. Estimation and modeling of performance within certain environments are necessary. Current solutions for integrated technology management are limited. They focus on specific technologies, specific use cases, or have limited management capability. Performance modeling is more common but focuses on interference from other communication technologies, not interference from generic electronic devices. The increasing use of electrical devices multiplies the problem and affects many devices and technologies. This dissertation provides three significant contributions to address these challenges. The first contribution explores and models IEEE 802.11 systems' performance when an interfering source is present that is not a communication technology. We first explore the performance of IEEE 802.11 in a challenging environment via a wireless mesh network and further in a controlled setup and simulation. We provide two models. The first is based on base performance when no interference is present and is computationally fast. The second is an analytical model that models the entire system's behavior but is computationally expensive. The second contribution consists of the ORCHESTRA framework, enabling inter-technology management seamlessly to the user and operator. This framework offers interference mitigation by using multiple technologies. It uses technology abstraction through a virtual layer and advanced packet-level functionalities, such as handovers, load balancing, and duplication. A central controller maintains a global view of the network and makes intelligent decisions to improve performance. As a third contribution, we present a load balancing solution that normalizes latency for links with different latency properties. Different technologies exhibit different latency properties that cause problems when using packet-level load balancing. We provide a machine learning based normalization method that smooths and reduces latency on a flow. Instead of sending out bursts of packets after reordering, packets are sent with a short time in between to avoid burst behavior. |
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Language
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English
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Publication
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Antwerp
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University of Antwerp, Faculty of Science, Department of Computer Science
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2021
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Volume/pages
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xxv, 148 p.
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Note
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Latré, Steven [Supervisor]
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Full text (open access)
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