Fuzzy Aggregation for Multi-Sensor Indoor Localization: Integrating Heterogeneous Data Sources

Abstract

Indoor localization is a crucial technology for applications such as intelligent building management, security, navigation, etc. However, the presence of complexities and uncertainties in the sensor data invites multiple challenges while integrating the heterogeneous data from several sensors such as Wi-Fi, RFID, Bluetooth, UWB, etc. This article proposes a novel idea of the aggregation operators (AOs) for the interval-valued circular intuitionistic fuzzy set (IVCIFS) to deal with the uncertainties and complexities in the integrated data from multiple sensors. The interval-valued circular intuitionistic fuzzy (IVCIF) Muirhead mean (IVCIFMM) and IVCIF dual Muirhead mean (IVCIFDMM) operators are defined to deal with uncertain and complex information to ensure accuracy. Defining the interval-valued circular intuitionistic fuzzy (IVCIF) weighted Muirhead mean (IVCIFWMM) and IVCIF dual weighted Muirhead mean (IVCIFDWMM) operators, the weights of the multiple factors are considered significant in fuzzy aggregation of information obtained from multi-sensors. The defined aggregation models are significantly applied in handling uncertainties in heterogeneous data obtained from different indoor localizations.