Mobility Predictions for IoT Devices Using Gated Recurrent Unit Network

Abebe Belay Adege*, Hsin Piao Lin, Li Chun Wang

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Wireless and mobile technologies, as well as their users, are growing rapidly as the Internet of Things (IoT) products, such as sensor-network technologies, mobile devices, and supporting applications, become widely dispersed. Owing to the dynamic changes in the wireless networks and the exponential growth of the IoT products, which make it difficult to locate large quantities of users and devices, providing accurate tracking and trajectory predictions in open and highly condensed wireless networks is extremely difficult. An adaptive and scalable system is required to offer accurate location-based services (LBSs) for the success of IoT. To enhance the attainment of IoT, we propose a hybrid of principal component analysis (PCA) and gated recurrent unit (GRU) algorithms for mobility predictions in a wireless urban area. During the system development processes, we first collect an LTE signal from three unmanned aerial vehicle base stations (UAV-BSs), the Wi-Fi signal strength from each reachable Wi-Fi access points (APs), and channel information from the Wi-Fi signal media. We then apply PCA to reduce the number of Wi-Fi features and to decrease signal noise. Next, we train the GRU algorithm to develop models that can predict the mobility of IoT device users. Finally, we evaluate the tracking and trajectory models. To evaluate the proposed techniques, we compare the common parameters of the GRU with those of other deep learning types. The proposed technique provides plausible and state-of-the-art results for mobility predictions of IoT devices in a wireless environment.

Original languageEnglish
Article number8873637
Pages (from-to)505-517
Number of pages13
JournalIEEE Internet of Things Journal
Volume7
Issue number1
DOIs
StatePublished - Jan 2020

Keywords

  • Deep learning
  • gated recurrent units (GRUs)
  • mobility predictions
  • principal component analysis (PCA)

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