Indoor Localization system Based on Deep Learning Approach


Kordi, Khaldon Azzam and Roslee, Mardeni and Alias, Mohamad Yusoff (2022) Indoor Localization system Based on Deep Learning Approach. In: Postgraduate Colloquium December 2022, 1-15 December 2022, Multimedia University, Malaysia. (Unpublished)

[img] Text
33_ KHALDON KORDI -foe.pdf - Submitted Version
Restricted to Repository staff only

Download (1MB)


In recent years, several studies focused on indoor localization systems. This concept includes many services, for example the location-based services. The localization system is a portion of IoT “the Internet of Things” domain, which offers a solution for tracking in real-time. Several techniques and technologies were proposed in the current research studies for indoor localization services to provide improved end-user services. This study discusses and highlights localization techniques and emerging technologies including the updates for new localization system. A range of the proposed systems based on scalability, accuracy, robustness, cost, and complexity are also discussed and assessed. This study also discussed the application of optimization techniques, like machine learning and profound learning, to improve the estimation accuracy of localization systems while addressing some new IoT and 5G technologies. Several challenges and future directions of localization systems are highlighted. This research provides a guide and an excellent platform for researchers who are working in indoor localization systems.

Item Type: Conference or Workshop Item (Poster)
Uncontrolled Keywords: IoT, 5G
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 29 Dec 2022 02:49
Last Modified: 29 Dec 2022 02:49


Downloads per month over past year

View ItemEdit (login required)