Design guideline of Wi-Fi fingerprinting in indoor localization using invariant Received Signal Strength

Abstract

Location-based services application in indoor environment utilizing Wi-Fi Received Signal Strength (RSS) is recently prevalent in pervasive computing applications. It is used as an enabler of various location based personal services with handheld and wearable communication devices. This paper present a design guideline for the benefits of the society who wish to employ invariant RSS-based localization using Wi-Fi fingerprinting in any indoor environment. This useful guideline relates statistically the different levels of the random spatiotemporal disturbances inducing RSS instability to the minimum number of Wi-Fi sources required for achieving a certain class separation degree under the given number of calibration locations to be identified. We developed an algorithm to simulate the invariant reference RSS propagations, spontaneous RSS propagations, identify the effective invariant RSS after applying spatiotemporal disturbances, and compute the class separation degree of the calibrated reference locations. An instance from the result shows that to get a class separation degree of above 90% with 35% random spatiotemporal disturbances when the number of calibrated locations is 20, the optimum number of obtainable Wi-Fi signal sources should be at least 50.