A Revised Received Signal Strength Based Localization for Healthcare
International Journal of Multimedia and Ubiquitous Engineering
Location-awareness is important for healthcare, and can be applied to the various consumer applications. The received signal strength (RSS) based localization technique has advantages of needing no additional hardware and simple to be implemented inbuilding applications. Received signal strength indication fingerprinting (RSSIFP) is an indoor localization technique. However, the RSS is affected by radio signals' reflections, shadowing, and fading. To solve this problem, an effective indoor
... ization method of revised RSSIFP is proposed to reduce the deviation during indoor RSSIFP localization. The proposed algorithm uses the RSSIFP based on the position probability grid. Before position, the RSSIFP data are revised according to anchor node signal and time tag. The K-nearest neighbor (KNN) and weighted centre localization method is adopted in position prediction. A test-bed only including common consumer electronic equipments such as wireless access point (AP), Zigbee node and smart cell-phone is deployed. Performance results show that the proposed algorithm outperforms other algorithms in the healthcare environments. 274 Copyright ⓒ 2015 SERSC much cheaper than TOA and AOA-based methods. It is suitable to be employed in the WSN environment. Unfortunately, it is not easy for radio-based localization, because signal has reflection, diffraction and scattering characters. The studies showed radio waves' degrading effects of reflections, shadowing, and fading cause the large variability of RSS     . As a result, RSS based localization methods' accuracy is not guaranteed due to signals' errors and instability. However, there need no extra equipment attracts many researchers' eyes. In fact, most of smart mobile phones have a built-in RSSI, which provides RSS measurement without any extra cost. Utilizing RSS method's simple, easy measure advantage and avoiding signal instable shortage, we will present a RSS methods combine few anchor nodes, which can lead to accurate location estimation in this paper. The anchor node is used to correct the errors caused by signal's instability. RSSI fingerprints are captured and k-nearest neighbor (KNN) algorithm is adopted to find k locations that have a similar fingerprint. The accuracy of the proposed method is estimated in experiments.