RoC: Robust and Low-Complexity Wireless Indoor Positioning Systems for Multifloor Buildings Using Location Fingerprinting Techniques

Abstract

Most existing wireless indoor positioning systems have only success performance requirements in normal operating situations whereby all wireless equipment works properly. There remains a lack of system reliability that can support emergency situations when there are some reference node failures, such as in earthquake and fire scenarios. Additionally, most systems do not incorporate environmental information such as temperature and relative humidity level into the process of determining the location of objects inside the building. To address these gaps, we propose a novel integrated framework for wireless indoor positioning systems based on a location fingerprinting technique which is called the Robust and low Complexity indoor positioning systems framework (RoC framework). Our proposed integrated framework consists of two essential indoor positioning processes: the system design process and the localization process. The RoC framework aims to achieve robustness in the system design structure and reliability of the target location during the online estimation phase either under a normal situation or when some reference nodes (RNs) have failed. The availability of low-cost temperature and relative humidity sensors can provide additional information for the location fingerprinting technique and thereby reduce location estimation complexity by including this additional information. Experimental results and comparative performance evaluation revealed that the RoC framework can achieve robustness in terms of the system design structure, whereby it was able to provide the highest positioning performance in either fault-free or RN-failure scenarios. Moreover, in the online estimation phase, the proposed framework can provide the highest reliability of the target location under the RN-failure scenarios and also yields the lowest computational complexity in online searching compared to other techniques. Specifically, when compared to the traditional weighted k-nearest neighbor techniques (WKNN) under the 30% RN-failure scenario at Building B, the proposed RoC framework shows 74.1% better accuracy performance and yields 55.1% lower computational time than the WKNN.

Towards Accelerated Localization Performance Across Indoor Positioning Datasets

“…The proposed cascade of models offers a stable separation of samples based on building and floor labels into pre-defined number of classes. While AP-wise clustering methods [8], [9], [19] separate the space into clusters based on samples' features, the proposed cascade splits the data based on their labels and thus ensures more balanced distribution of samples per classifier. As a result, each considered individual prediction model (e.g.…”

“…Their solution outperforms benchmark solutions in terms of both Prediction Time (PT) and positioning accuracy. Similarly, [9] used classification algorithms to reduce the complexity in the online phase of Wi-Fi fingerprinting. They included environmental sensors (humidity and temperature sensors) to provide extra information to the fingerprinting techniques, allowing to reduce the location complexity.…”

“…They included environmental sensors (humidity and temperature sensors) to provide extra information to the fingerprinting techniques, allowing to reduce the location complexity. In the framework of [9], two classification techniques were used to first determine the floor where the target node is located, and the second to filter the location of the fingerprint in the radio map. As a result, the authors improved positioning accuracy.…”

Performance Improvement Design of Bluetooth Low Energy-Based Wireless Indoor Positioning Systems

“…us, the value of its reliability index R � 0. More details and explanations about the operation of the R-MSMR design can be found in [11,29]. e input parameters of the R-MSMR placement design problem are summarized in Table 3.…”

Evaluation and Comparison of 5G, WiFi, and Fusion With Incomplete Maps for Indoor Localization

“…Fingerprinting exhibits several primary drawbacks. It notably demonstrates high sensitivity to disparities between training and testing conditions arising from dynamic propagation attributes such as temperature, humidity, and obstacles [19]- [21]. Additionally, it mandates an extensive preliminary map construction phase, which necessitates thoroughness [22].…”