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Open AccessFeature PaperArticle

Optimising Performance for NB-IoT UE Devices through Data Driven Models

1
Center of Telecommunication Research, Kings College London, London WC2R 2LS, UK
2
Center for Digital Engineering, Simula Metropolitan, 0167 Oslo, Norway
3
Telenor Research, 1360 Fornebu, Norway
*
Author to whom correspondence should be addressed.
J. Sens. Actuator Netw. 2021, 10(1), 21; https://doi.org/10.3390/jsan10010021
Received: 25 January 2021 / Revised: 23 February 2021 / Accepted: 26 February 2021 / Published: 5 March 2021
(This article belongs to the Special Issue Machine Learning in IoT Networking and Communications)
This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation. View Full-Text
Keywords: NB-IoT; internet of things; reinforcement learning; gradient descent; genetic algorithm; deep learning; machine learning NB-IoT; internet of things; reinforcement learning; gradient descent; genetic algorithm; deep learning; machine learning
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MDPI and ACS Style

Nassef, O.; Mahmoodi, T.; Michelinakis, F.; Mahmood, K.; Elmokashfi, A. Optimising Performance for NB-IoT UE Devices through Data Driven Models. J. Sens. Actuator Netw. 2021, 10, 21. https://doi.org/10.3390/jsan10010021

AMA Style

Nassef O, Mahmoodi T, Michelinakis F, Mahmood K, Elmokashfi A. Optimising Performance for NB-IoT UE Devices through Data Driven Models. Journal of Sensor and Actuator Networks. 2021; 10(1):21. https://doi.org/10.3390/jsan10010021

Chicago/Turabian Style

Nassef, Omar; Mahmoodi, Toktam; Michelinakis, Foivos; Mahmood, Kashif; Elmokashfi, Ahmed. 2021. "Optimising Performance for NB-IoT UE Devices through Data Driven Models" J. Sens. Actuator Netw. 10, no. 1: 21. https://doi.org/10.3390/jsan10010021

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