Design of RSSI based fingerprinting with reduced quantization measures

Abstract

This paper investigates the role of quantization in the Received Signal Strength Indicator (RSSI) information\ud used for fingerprinting (FP) applications. One of the common drawbacks of FP is the large data size and consequently the large search space and computational load as a result of either vastness of the positioning area or the finer resolution in the FP grid map: this limits the application of FP to small environments or scenarios with largely spaced grid points leading to poor localization performance. We show that the computational complexity\ud can be limited adapting the RSSI quantization w.r.t. the variance of the measured RSSI error at the target. This approach turns out to be advantageous for the deployment of FP systems based on beacons equipped with inexpensive technologies, in which the measures precision loss could be compensated by larger numbers of beacons. An appropriate quantization and design of RSSI signatures will make possible the deployment of FP in larger areas maintaining the same computational load and/or the desired localization performance

Comparing the Performance of Indoor Localization Systems through the EvAAL Framework

“…According to Table 1 , each research work uses specific hardware even when they use the same base technology for positioning. A Huawei Mate smartphone was used in [ 37 ], six different devices were used in [ 38 ], and a simulation was carried out in [ 41 ]. Using multiple devices in the set-up might be more challenging than using a single device or simulated data.…”

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

“…Additionally, references [40][41][42] discussed different ways to build empirical models by using multi-points, crowdsourcing, and so on.…”

Analysis of Received Signal Strength Quantization in Fingerprinting Localization

“…Since a fingerprint has many reference AP, instead of fine-granular RSS, coarse-granular RSS could be able to provide the same positioning accuracy. Moreover, storing, exchanging, and processing raw RSS value can occupy a considerable amount of memory [17,18], as well as be a potential threat to user's location privacy [19,20]. In this case, a quantization method can be useful.…”