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(1)

PERFORMANCE

ENHANCEMENTS OF LORAWAN USING

MACHINE LEARNING ON THE EDGE

Author: Anuar Otynshin

Supervisor: Dimitrios Zorbas

(2)

Presentation Outline

Problem description

Objectives

General information

Proposed Solution

Results

Conclusion and future work

Questions

(3)

Problem Description

THE NUMBER OF IOT

DEVICES IS GROWING LORAWAN IS ONE OF THE MOST PROMISING

PROTOCOLS FOR DEVICES TO TRANSFER DATA

LORAWAN PERFORMS POORLY IN SATURATED

NETWORKS

(4)

Objectives

Evaluate and analyze existing literature Identify gap in literature

Develop and implement an optimization technique

Optimize performance of LoRaWAN in saturated networks Analyze the results and compare to the baseline approach

(5)

What is LoRaWAN?

Long Range Wireless Access Network (LoRaWAN)

Low Power, Wide Area (LPWA) networking protocol for Internet of Things

How it works? (Class A LoRaWAN)

Send a signal

Wait for downlink transmission during 2 receiving windows

Confirmed traffic

Wait for ACK signal

If no gateway sends an ACK, retransmit data

Max number of retransmissions can be configured

(6)

Why LoRaWAN

?

Open source

Low power

consumption

Up to several years of operation

Long range

communication

Many kilometers wide

communication

Security

Encrypted end-to- end communication
(7)

Literature Review

Research Focus

Practical

Theoretical

Methodology

Mathematical Model

Simulations

Real-world testing

(8)

Literature Review

Little research on optimizing the application layer

parameters

Machine learning approach

(9)

Machine Learning Approach

Learn the network behavior

1

Identify any patterns of

packet collision

2

Retransmit the packets taking the pattern

into account

3

(10)

Reinforcement Learning and SARSA

MACHINE LEARNING TECHNIQUE TO INCREASE

REWARD

REQUIRES NO PRE-TRAINED

MODELS LEARN BEHAVIOR BY

INTERACTING WITH ENVIRONMENT

(11)

RESULTS

(12)

Conclusion and Future work

SARSA-1 and SARSA-2 algorithms improve the performance of the network when

compared to the typical LoRaWAN periodic application

Future work

Influence of α, γ, ε

Influence of unconfirmed traffic

Mobile devices

(13)

QUESTIONS?

Referensi

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