A revised received signal strength based localization for healthcare

Abstract

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 in-building 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 localization 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.