WLAN location fingerprinting technique for device-free indoor localization system
Device-free indoor localization (DFIL) system can locate the position of human body in the indoor environment by observing the changes in the received signal strength indicator (RSSI) of the wireless local area network (WLAN). The accuracy of a DFIL system is depreciated, as the change in the indoor...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2016
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010301143&doi=10.1109%2fICCOINS.2016.7783292&partnerID=40&md5=61c6d2a47d220c4d8510b57661d9e01d http://eprints.utp.edu.my/30521/ |
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| Summary: | Device-free indoor localization (DFIL) system can locate the position of human body in the indoor environment by observing the changes in the received signal strength indicator (RSSI) of the wireless local area network (WLAN). The accuracy of a DFIL system is depreciated, as the change in the indoor environment due to furniture and other infrastructure movement. This paper investigates the development of testbed of the Wi-Fi network for measuring the RSSI in various indoor environment, as the initial step for designing the fingerprinting-based algorithms for Wi-fi network. The database of RSSI fingerprint is created initially and then a fingerprint-based algorithm is developed for locating the position of a human body in the indoor environment. The localization algorithm tests the minimum distance in the RSSI values related to the different test points in the indoor environment. This work further demonstrates that how the fingerprints of RSSI are collected and which network configurations generate the most reliable RSSI measurement. For the first phase of designing the testbed, the configurations of different equipment and various tools are elaborated in the indoor environment. For the second phase the RSSI is measured in different propagation indoor environment. The extensive experiments were performed that allow quantification of how changes in an environment affect accuracy. Thus, it is demonstrated that each link offers a viable approach to developing a more robust system for device-free localization that is less susceptible to changes in the environment. © 2016 IEEE. |
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