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ANN-based optimization framework for performance enhancement of Restricted Access Window mechanism in dense IoT networks

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Abstract

IEEE 802.11ah, marketed as Wi-Fi HaLow, operates at Sub 1 GHz spectrum to provide broad coverage, high throughput, energy efficiency and scalability. This makes IEEE 802.11ah a promising candidate for the Internet of Things (IoT). One of the major enhancements in the MAC layer is the Restricted Access Window (RAW) mechanism, which focuses on mitigating the channel contention in dense networks. The RAW mechanism reduces the channel contention among the group of devices by restricting their channel access to the allocated RAW slots. Since the standard does not specify the optimal RAW configuration parameters, choosing the number of RAW slots has significant impact on the performance of the RAW mechanism. In this paper, we develop an optimization framework by exploiting the Multilayer Perceptron Artificial Neural Network (MLP-ANN) to find the optimal number of RAW slots that can maximize the performance of the RAW mechanism in terms of throughput, delay and energy consumption. We train the ANN using the network size, Modulation and Coding Schemes, duration of the RAW period and the optimal number of RAW slots found using the analytical model presented in this paper. Further, we evaluate the performance of the RAW mechanism by choosing the optimal number of RAW slots provided by the ANN-based optimization framework. Results show that the proposed scheme significantly enhances the performance of the RAW mechanism. Finally, the analytical results are corroborated using extensive simulations done in ns-3.

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Notes

  1. In uniform groping scheme the number of RAW slots is equal to the number of groups.

  2. We consider \(m=5\), \(R=7\), \(CW_{min}=32\) and \(CW_{max}=1024\) according to [3].

  3. Although multiple packets can be sent in a TXOP, we assume only one packet for simplicity. \(\Delta _{\textit{DATA}}\) and \(\Delta _{\textit{ACK}}\) represent the time taken to transmit the data packet and acknowledgment corresponding to the MCS used.

Abbreviations

N :

Network size

n :

Index of a device in the network

G :

Number of groups

g :

Index of a device in a group

\(\Delta _{\textit{RAW}}\) :

Duration of RAW period

\(\Delta _{\textit{slot}}\) :

Duration of RAW slot

\(\xi \) :

Transmission Opportunity (TXOP)

\(\xi '\) :

TXOP+DIFS

\(\Delta _{\textit{DATA}}\) :

Time taken to transmit a data packet

\(\Delta _{\textit{ACK}}\) :

Time take to transmit acknowledgment

\(\delta _{s}\) :

SIFS duration

\(\delta _{d}\) :

DIFS duration

\(\rho \) :

Duration of mini-slot

m :

Number of back-off stages

\(m'\) :

Maximum re-transmission limit

s(t):

Stochastic process representing the back-off stage

b(t):

Stochastic process representing the back-off counter

l :

Number of transactions

\(\Delta _{h}\) :

Duration of holding time

\(\Delta _{g}\) :

Guard interval

\(B_l\) :

Number of back-off slots before \(l{th}\) transaction

\(\Delta _{l}\) :

Duration of l transactions

\(\psi \) :

Number of mini-slot in a RAW slot

\({\mathcal {T}}_u\) :

Maximum number of transaction in a RAW slot

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  1. Department of Electronics and Communication Engineering, National Institute of Technology Puducherry, Karaikal, India

    Miriyala Mahesh & V P Harigovindan

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  1. Miriyala Mahesh
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Mahesh, M., Harigovindan, V.P. ANN-based optimization framework for performance enhancement of Restricted Access Window mechanism in dense IoT networks. Sādhanā 45, 52 (2020). https://doi.org/10.1007/s12046-020-1287-6

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