and Huzur, S, вЂњLOCATOR: Location Estimation System for Wireless LANs,вЂќ Proc. of 13th International Conference on Control, Automation and Systems (ICCAS), Oct. and Park, S., вЂњFingerprint DB Generating System Exploiting PDR Based Dynamic Collection for Indoor Localization of Smartphone Users,вЂќ Proc. of International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Gwangju, May. and Apavatjrut, A., вЂњIndoor Wi-Fi Localization on Mobile Devices,вЂќ Proc. and Niemegeers, I., вЂњA Survey of Indoor Positioning Systems for Wireless Personal Networks,вЂќ IEEE Communications Surveys & Tutorials, Vol. S., вЂњA Survey of Calibration-free Indoor Positioning Systems,вЂќ Computer Communications, Vol.

of IEEE Applied Imagery Pattern Recognition Workshop: Sensing for Control and Augmentation, Washington, Oct. Pritt, N., вЂњIndoor Location with Wi-Fi Fingerprinting,вЂќ Proc.of International Conference on Communications and Signal Processing, India, Apr. and Jayabharathy, R., вЂњEvaluating Context Based Indoor Positioning System Using Wi-Fi Standards,вЂќ Proc. and Liu, L., вЂњSurvey of Indoor Positioning Technologies and Systems,вЂќ Life System Modeling and Simulation, Vol. Keywords: Access Point Selection, Adaptive Pattern-matching, Indoor Positioning, Received Signal Strength, Box Plot These findings show that ASAPM could reduce the computational overhead moreover, it is suitable as an indoor-positioning service for mobile devices. The simulation results revealed that the average positioning error, average maximum positioning error, and average pattern-matching times of ASAPM were 36%, 51%, and 57% lower than the three compared strategies, respectively. Moreover, we analyzed the history of the positioning results to estimate the direction and distance of users in subsequent positions in order to reduce the pattern-matching time and computational overhead of the positioning system. Subsequently, we analyzed the RSS variations for selecting the top-N APs with the least interference. In the proposed ASAPM, a box plot algorithm is used to remove received signal strength (RSS) outliers in samples received from APs in order to smooth the RSS. This study proposes a method based on access point (AP) selection and adaptive pattern-matching for Wi-Fi indoor positioning (ASAPM).

Padmanabhan krishnaveni