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Access Point Assignment for Hybrid Wi-Fi and Li-Fi Networks

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Thesis(456.97 MB)

Date

2019

Authors

Supervisor

Liu, William
Tan, Jin

Item type

Thesis

Degree name

Master of Computer and Information Sciences

Journal Title

Journal ISSN

Volume Title

Publisher

Auckland University of Technology

Abstract

Li-Fi is the trend of next-generation network that enable security and fast data transmission. Combining Li-Fi and Wi-Fi not only achieves high-speed network transmission but also compensates for the small coverage area of a signal Li-Fi Access Point (AP). However, there will be multiple Li-Fi APs in one area due to the small illumination range of Light-emitting Diode (LED). Furthermore, Wi-Fi and Li-Fi signals are overlap in the heterogeneous networks. It leads to the problem of AP assignment. In this thesis, we have proposed and implemented three kinds of access point assignment algorithms to access point assignment in hybrid Wi-Fi and Li-Fi network. The first algorithm, called as Load Balancing based Access Point Assignment (LBAPA), is to resolve the unbalanced network resource allocation problem in current algorithm Received Signal Strength (RSS) by using load balance. Then the Two-Steps Access Point Assignment (TSAPA) is proposed to add user mobility and channel blocking factors into the calculation. At last, to advance the flexible and accuracy of LBAPA and TSAPA algorithms, the Fuzzy Logic Access Point Assignment (FLAPA) is proposed by using the fuzzy logic approach. It can synthesize the user mobility, channel blockage degree and interference to calculate a final score as the priority for the users to connect to Li-Fi. The extensive simulation studies have confirmed that our LBAPA algorithm has higher resource utilization than RSS algorithm. The TSAPA has better performance when the users are movable or when the channel blockage occurs. The FLAPA algorithm is flexible and scalable to maintain good network performance in complex environment.

Description

Keywords

li-fi, access point assignment, load balancing, fuzzy logic

Source

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Permanent link

https://hdl.handle.net/10292/12541

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Masters Theses