Radius based domain clustering for WiFi indoor positioning. Issue 1 (16th January 2017)

• Record Type: Journal Article
• Authors: Zhang, Wei; Hua, Xianghong; Yu, Kegen; Qiu, Weining; Chang, Xin; Wu, Bang; Chen, Xijiang
• Is Part Of: Sensor review. Volume 37:Issue 1(2017)
• Page Start: 54
• Page End: 60
• Publication Date: 2017-01-16
• Subjects: Domain clustering -- Naive Bayes classifier -- Received signal strength -- Weighted k nearest neighbour -- WiFi indoor positioning; Sensor systems -- Periodicals; Detectors -- Industrial applications -- Periodicals; Engineering instruments -- Periodicals; 681.2
• DOI: 10.1108/SR-06-2016-0102
• Languages: English
• ISSNs: 0260-2288

Abstract

Abstract : Purpose: Nowadays, WiFi indoor positioning based on received signal strength (RSS) becomes a research hotspot due to its low cost and ease of deployment characteristics. To further improve the performance of WiFi indoor positioning based on RSS, this paper aims to propose a novel position estimation strategy which is called radius-based domain clustering (RDC). This domain clustering technology aims to avoid the issue of access point (AP) selection. Design/methodology/approach: The proposed positioning approach uses each individual AP of all available APs to estimate the position of target point. Then, according to circular error probable, the authors search the decision domain which has the 50 per cent of the intermediate position estimates and minimize the radius of a circle via a RDC algorithm. The final estimate of the position of target point is obtained by averaging intermediate position estimates in the decision domain. Findings: Experiments are conducted, and comparison between the different position estimation strategies demonstrates that the new method has a better location estimation accuracy and reliability. Research limitations/implications: Weighted k nearest neighbor approach and Naive Bayes Classifier method are two classic position estimation strategies for location determination using WiFi fingerprinting. Both of the two strategies are affected by AP selection strategies and inappropriate selection of APs may degrade positioning performance considerably. Practical implications: The RDC positioning approach can improve the performance of WiFi indoor positioning, and the issue of AP selection and related drawbacks is avoided. Social implications: The RSS-based effective WiFi indoor positioning system can makes up for the indoor positioning weaknesses of global navigation satellite system. Many indoor location-based services can be encouraged with the effective and low-cost positioning technology. Originality/value: A novel position estimation strategy is introduced to avoid the AP selection problem in RSS-based WiFi indoor positioning technology, and the domain clustering technology is proposed to obtain a better accuracy and reliability.