A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Analysis of Received Signal Strength Quantization in Fingerprinting Localization (2020)

Khandker, S., Torres-Sospedra, J., & Ristaniemi, T. (2020). Analysis of Received Signal Strength Quantization in Fingerprinting Localization. Sensors, 20(11), Article 3203. https://doi.org/10.3390/s20113203

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Khandker, Syed; Torres-Sospedra, Joaquín; Ristaniemi, Tapani

Lehti tai sarja: Sensors

eISSN: 1424-8220

Julkaisuvuosi: 2020

Volyymi: 20

Lehden numero: 11

Artikkelinumero: 3203

Kustantaja: MDPI

Julkaisumaa: Sveitsi

Julkaisun kieli: englanti

DOI: https://doi.org/10.3390/s20113203

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Kokonaan avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/70909

Tiivistelmä

In recent times, Received Signal Strength (RSS)-based Wi-Fi fingerprinting localization has become one of the most promising techniques for indoor localization. The primary aim of RSS is to check the quality of the signal to determine the coverage and the quality of service. Therefore, fine-resolution RSS is needed, which is generally expressed by 1-dBm granularity. However, we found that, for fingerprinting localization, fine-granular RSS is unnecessary. A coarse-granular RSS can yield the same positioning accuracy. In this paper, we propose quantization for only the effective portion of the signal strength for fingerprinting localization. We found that, if a quantized RSS fingerprint can carry the major characteristics of a radio environment, it is sufficient for localization. Five publicly open fingerprinting databases with four different quantization strategies were used to evaluate the study. The proposed method can help to simplify the hardware configuration, enhance security, and save approximately 40–60% storage space and data traffic.

YSO-asiasanat: sisätilapaikannus; langattomat lähiverkot

Vapaat asiasanat:  fingerprinting; quantization; indoor positioning

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 1