1326 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 13, NO. 6, JUNE 2014 SEAMLOC: Seamless Indoor Localization Based on Reduced Number of Calibration Points Milan D. Redži´ c, Conor Brennan, and Noel E. O’Connor Abstract—Indoor localization based on ubiquitous WLAN has exhibited the capability of being a cheap and relatively precise technology and has been veriﬁed by many successful examples. Its performance is subject to change due to multipath propagation and changes in the environment (people, building layouts, antenna characteristics, etc.) which cannot be eliminated easily. An indoor localization and tracking algorithm is described which is based on the creation of a database of WLAN signal strengths at pre-chosen calibration points (CPs). The need for fewer CPs than in standard methods is due to use of a novel interpolation algorithm, based on the speciﬁcation of robust, range and angle-dependent likelihood functions that describe the probability of a user being in the vicinity of each CP. The actual location of the user is estimated by solving a system of two non-linear equations with two unknowns derived for a pair of CPs. Different pairs of CPs can be chosen to make several estimates which then can be combined to increase the accuracy of the estimate. A variety of results are presented using challenging data collected in a typical ofﬁce environment which demonstrate the accuracy that can be achieved with a reduced number of CPs. The method is compared to several competing localization methods and is shown to give superior results. Index Terms—WLAN, ﬁngerprinting, indoor localization, Bayesian approach, linearization, performance measures 1 I NTRODUCTION A LTHOUGH GPS has become synonymous with user localization, indoors its signals are weak or non- existent. Using WLAN as a solution has given promising results, but its performance is subject to change due to multipath propagation and changes in the environment, such as the number of persons present in a given location, variation in orientation, temporary changes to building lay- out, noisy WLAN channel, etc. [1], [2]. Performance also depends on the material the building is made of, size of spaces where measurements take place, antenna orienta- tion, directionality, etc. [2], [3]. Fingerprinting based on WLAN signals [4]–[6] throughout the building has shown particular promise due to its leveraging of pre-existing infrastructure and the relative simplicity of the associated algorithms. The basic idea is to collect signal strength data at many pre-selected locations (which will be referred to as calibration points (CPs)) to create a database which is then used to locate and track users. This is a time consuming personnel-intensive process. Fig. 1 displays an example of typical histogram data collected and stored and illustrates the variation in signal characteristics which enables us to distinguish between locations. Some other proposed indoor location methods, such as Infrared and Ultra Wide Band based solutions, have • The authors are with CLARITY: Centre for Sensor Web Technologies, Dublin City University, Dublin, Ireland. E-mail: {milan.redzic, brennanc, oconnorn}@eeng.dcu.ie. Manuscript received 19 July 2012; revised 25 Mar. 2013; accepted 8 Aug. 2013. Date of publication 20 Aug. 2013; date of current version 29 May 2014. For information on obtaining reprints of this article, please send e-mail to: reprints@ieee.org, and reference the Digital Object Identiﬁer below. Digital Object Identiﬁer 10.1109/TMC.2013.107 very speciﬁc requirements regarding their hardware. This makes the problem of location and tracking indoors an open problem of signiﬁcant interest as evidenced by the number of applications such as EasyLiving [7] and other projects such as [8], [9] that would beneﬁt from a practi- cal and easy-to-employ, location-sensing system. Therefore an ideal localization system should use widely-deployed off-the-shelf hardware, should be based on as small as possible number of CPs and be robust to environmental changes [10]. In this paper an approach which requires a reduced number of CPs (up to 4 times fewer CPs as compared to other approaches) is presented. The reduction is achieved by the use of an interpolation approach which allows us to locate users at points between CPs. The interpolation is based on constructing robust, range and angle-dependent, likelihood functions that describe the probability of a user being in the vicinity of a CP. The actual location of the user is estimated by solving a system of two non-linear equations with two unknowns derived for a pair of CPs. Different pairs of CPs can be chosen to make several estimates which then can be combined to increase the accuracy of the estimate. The probability functions are obtained using a Naive Bayes method formulation [11], [12] which takes into account not only RSSI values of the selected access points (APs) but the frequency of appearance of these APs as well. The method was tested and the results were presented in two experimental setups (ESs): in big ofﬁces and in smaller ofﬁces separated by thicker walls between them. It is shown that this novel technique is robust to a variety of environmental changes including time-of-day variation and the presence or absence of persons in the ofﬁces. Furthermore, the techniques presented here showed robustness over the change of number of training and 1536-1233 c  2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.