Indoor Location Classification Algorithm using RSSI and Device Height 


Vol. 42,  No. 8, pp. 1573-1580, Aug.  2017
10.7840/kics.2017.42.8.1573


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  Abstract

Location-based service (LBS) provision is one of the visions for internet of things (IoTs). It is therefore necessary to build and provide efficient localization technologies to achieve the vision. GPS technology is a very prominent localization technology adopted in various systems such as cars, aircrafts, smart watches, smart phones and many others. However GPS is constrained in terms of its accuracy in indoor scenarios. Received signal strength indicator (RSSI) based approaches that make use of minimal wireless communication infrastructures in indoor scenarios have taken centre stage in tackling the problem of indoor positioning. However, the RSSI estimate of the radio wave highly depends on the indoor environments physical features hence may not be perfect for estimation of the distance between the transmitter and receiver. Conventional indoor positioning systems that use the RSSI, have considered the use of adaptive filters such as the Kalman filter to improve on the transmit-receive distance estimation in indoor environments. However, Providing LBS in real time is a challenge because of the large amount of computation. Again, most LBSs need to be able to classify the space where the receiver is located, not the precise indoor positioning. In this paper, we propose a receiver location classification algorithm based on trilateration using the RSSI of a beacon and the estimated receiver height mathematically. The performance of the proposed algorithm is compared with the conventional RSSI based technique which does not consider height. The performance proposed algorithm is also compared with the ITU-R path loss model for estimation of separation distance and path loss.

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  Cite this article

[IEEE Style]

Y. Kim and D. S. Han, "Indoor Location Classification Algorithm using RSSI and Device Height," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 8, pp. 1573-1580, 2017. DOI: 10.7840/kics.2017.42.8.1573.

[ACM Style]

Youngwoo Kim and Dong Seog Han. 2017. Indoor Location Classification Algorithm using RSSI and Device Height. The Journal of Korean Institute of Communications and Information Sciences, 42, 8, (2017), 1573-1580. DOI: 10.7840/kics.2017.42.8.1573.

[KICS Style]

Youngwoo Kim and Dong Seog Han, "Indoor Location Classification Algorithm using RSSI and Device Height," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 8, pp. 1573-1580, 8. 2017. (https://doi.org/10.7840/kics.2017.42.8.1573)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880