Development of Machine Learning-based Pbkp_redictive Models for Wireless Indoor Localization Application with Feature Ranking via Recursive Feature Elimination Algorithm

Abstract:

The cutting-edge wireless technologies offer huge array of services, from ultra-high-speed data communications to internet of things. But the current existing infrastructure cannot handle these use cases. Consequently, it is becoming a trend to apply computational intelligence algorithms such as machine learning (ML), deep learning (DL), reinforcement learning (RL) and artificial intelligence (AI) to wireless network infrastructures. And one of these applications is on wireless indoor localization. Wireless indoor localization takes advantage of wireless access points (WAPs) received signal strength indicators (RSSI) values to pinpoint the location of a user, similar to concept of GPS but indoors. The goal of this paper is to develop pbkp_redictive models that can be used to pbkp_redict the location of a user using RSSI readings that his smartphone receives. In this study, four ML algorithms are used which are support vector machines, random forest, Naïve-Bayes classifier and neural networks. The accuracy of each model are 97.83%, 97.67%, 98.50% and 97.33% respectively. Also, a recursive feature elimination algorithm is also used to determine the pbkp_redictor that has the least impact amongst all other features and it is found out in the study that WAP2 is contributes the least influence when the pbkp_redictive models are developed.

Año de publicación:

2020

Keywords:

• wireless indoor localization
• recursive feature elimination
• random forest
• neural network
• SVM
• Machine learning
• Naïve-Bayes

Fuente:

scopus