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RSSI quantization for indoor localization services

Received Signal Strength Indication (RSSI) measurements are known to be noisy and to exhibit high degree of variability, yet massive sets of RSSI data are streamed and collected with the purpose of using them for localization. In this paper we determine that the full resolution of RSSI measurements is unnecessary and propose, based on an evaluation of indoor localization experiments, a technique of modest computational effort, using Genetic Algorithms, to determine how to best quantize RSSI measurements. Our approach treats the localization algorithm as a “black box” and, hence, its applicability is broad. However, for the purpose of exposition and to generate absolute performance metrics, a particular profiling-based localization algorithm is used. Results from experiments involving real collected RSSI values indicate that it is possible to reduce the RSSI data volume by approximately 72% with no noticeable reduction in localization accuracy.