Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information

Citation

Abuhoureyah, Fahd and Wong, Yan Chiew and Mohd Isira, Ahmad Sadhiqin and Al-Andoli, Mohammed Nasser (2023) Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information. IET Wireless Sensor Systems. ISSN 2043-6386

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Abstract

The trajectory localisation of human activities using signal analytics has become a reality due to the widespread use of advanced signal processing systems. Device-free localisation using WiFi devices is prevalent, and the received signal strength indicator (RSSI) and channel state information (CSI) signals offer additional benefits. However, radio frequency (RF) localisation is highly dependent on the environment, so updating fingerprint data is necessary by changing the environment. This work presents Fine-grained Indoor Detection and Angular Radar for recognising and locating humans using a multipath trajectory reflections system that does not require training. It estimates location using a probabilistic approach that considers changes in CSI and RSSI across multiple nodes, generating an informative dataset that reflects the current trajectory and status of the location. The presented method extracts data from clustered Raspberry Pi 4B and Nexmon. The method exhibits a versatile real-time location-tracking solution by utilising the distinctive properties of RF signals. This technology has significant implications for various applications, including human medical monitoring, gaming, smart cities, and optimising building layouts to improve efficiency. The model demonstrates location-independent localisation with up to 80% accuracy in mapping trajectories at any location. The findings indicate that the proposed model is effective and reliable for indoor localisation and activity tracking, making it a promising solution for implementation in real-world environments.

Item Type: Article
Uncontrolled Keywords: body sensor networks, intelligent sensors, signal detection
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 04 Sep 2023 04:11
Last Modified: 04 Sep 2023 04:11
URII: http://shdl.mmu.edu.my/id/eprint/11660

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