Adaptive medium access control protocol of Wifi-Halow for home machine-to-machine network
IEEE 802.11ah is a Wi-Fi standard designed to support the Internet of Things (IoT) and Machine-to-machine (M2M) concept. While showing positive promises, the lack of clarity on the implementation is hindering the network from reaching its full potential. Contention window (CW) has been studied in...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/89884/1/FK%202020%2016%20ir.pdf |
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| Summary: | IEEE 802.11ah is a Wi-Fi standard designed to support the Internet of Things
(IoT) and Machine-to-machine (M2M) concept. While showing positive promises,
the lack of clarity on the implementation is hindering the network from reaching
its full potential. Contention window (CW) has been studied intensively
for conventional Wi-Fi but remain unexplored for Wi-Fi HaLow. Besides, the
parameters of new mechanism are not defined optimally even though the procedure
is detailed. The increasing
exibility of wireless network card used also
raises the concern of resources availability due to selfish attack. Therefore,
work can be done on these aspects to improve its performance and introduce
a new dimension of adaptation.
Firstly, this work proposed a novel algorithm known as probability-based opportunity
dynamic adaptation (PODA) of CW for home M2M network. The
relationship between channel winning opportunity and CW of the nodes is
detailed and the CW of the station can be adapted to achieve target resource
share. In the scenario considered, the network saturation throughput is improved
by 23 percent compared to the existing model.
Next, the fundamental of RAW mechanism is analysed. The performance fluctuation due to non-optimal RAW duration is avoided through determine
the optimal RAW slot duration for RAW group of different traffic intensity.
Scaling the RAW slot duration according to the group's traffic allows better
channel utilisation and improve the network throughput by 14 percent in
dense IoT network (n > 2000). Lastly, a novel punishment scheme is suggested based on the replicator dynamic
of Evolutionary Game Theory. The results show that the proposed
algorithm is capable of determining the suitable degree of punishment to handle
the selfish attack effectively and prevent the performance degradation of
at least 8 percent, 300 percent and 75 percent in terms of throughput, delay,
and energy efficiency respectively. |
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