The smartphone-based Wi-Fi fine time measurement (FTM) technique has provided a new approach for Wi-Fi-based indoor location on mobile phones since the 2018 release of the Google Android Pie system, which supports the IEEE 802.11-2016 protocol and can directly measure the distance between the initiator and the responder. This paper studies in detail the properties of mobile phone Wi-Fi ranging and positioning performance. Considering non-line-of-sight (NLOS) error identification, a real-time ranging error compensation model based on the least-squares (LS) method and an adaptive Wi-Fi FTM positioning algorithm (AWFP) utilizing the weighted least-squares (WLS) method are devised. To improve accuracy, a new tightly coupled fusion positioning algorithm integrating Wi-Fi FTM and built-in mobile phone sensors based on the extended Kalman filter (EKF) is constructed. The experimental results show that the ranging precision and Wi-Fi positioning accuracy are improved. Based on the high-precision Wi-Fi ranging and positioning results, the final location accuracy of the proposed fusion method is 0.98 m, and the root-mean-square error (RMSE) is 1.10 m, which are better than those of the PDR, Wi-Fi FTM and loosely coupled PDR/Wi-Fi FTM integration based on the EKF.

The human body has a great influence on Wi-Fi signal power. A fixed K value leads to localization errors for the K-nearest neighbor (KNN) algorithm. To address these problems, we present an adaptive weighted KNN positioning method based on an omnidirectional fingerprint database (ODFD) and twice affinity propagation clustering. Firstly, an OFPD is proposed to alleviate body’s sheltering impact on signal, which includes position, orientation and the sequence of mean received signal strength (RSS) at each reference point (RP). Secondly, affinity propagation clustering (APC) algorithm is introduced on the offline stage based on the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed for estimating user’s position during online stage. K initial RPs can be obtained by KNN, then they are clustered by APC algorithm based on their position-domain distances. The most probable sub-cluster is reserved by the comparison of RPs’ number and signal-domain distance between sub-cluster center and the online RSS readings. The weighted average coordinates in the remaining sub-cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 m, the root mean square error of 1.5 m. Experimental results show that our proposed method outperforms traditional fingerprinting methods.

A novel two-way ranging approach was introduced into the Wireless Fidelity (WiFi) standard, and its ranging accuracy reached one meter in a low multipath environment. However, in harsh environments due to multipath or non-line of sight (NLOS), the range measurement based on the WiFi round trip time (RTT) usually has low accuracy and cannot maintain the one-meter accuracy. Thus, this paper proposes an indoor positioning method based on Gaussian process regression (GPR) for harsh environments. There are two stages in the proposed method: construction of a positioning model and location estimation. In the model construction stage, based on known positions of access points (APs), we can determine the position coordinates of some ground points and the reference distances between them and the APs, and the offline ranging difference fingerprints can be generated by the reference distances, which means that there is no need to collect data. Gaussian process regression (GPR) utilizes offline ranging difference fingerprints based on the reference distance to establish a positioning model, and the particle swarm optimization (PSO) algorithm is employed to estimate the GPR hyperparameters. In the location estimation stage, the gathered actual range measurements generate the online ranging difference fingerprint, which is the input data of the positioning model. The output of the model is the estimated position of the smartphone. Experimental results show that the mean errors (MEs) of the proposed method and Least Squares (LS) algorithm are 1.097 and 3.484 meters, respectively, in a harsh environment, and the positioning accuracy of the proposed method improved by 68.5% compared with the LS algorithm.

Adaptation and recovery are essential for businesses to survive crises and disasters. Drawing on the concepts of business resilience and hospitality service delivery practices, this study explored strategies employed by owners of Vietnamese homestay businesses for adapting to and recovering from the COVID-19 pandemic. Semi-structured interviews with 17 purposefully selected homestay owners were conducted during the pandemic. The thematic analysis of the data revealed three major strategies: adapting operational protocols, modifying products and service offerings, and reinstating the authenticity of the homestay experience. A conceptual model illustrating the transformations in homestay businesses was developed from the findings. As the study particularly revealed that the selected Vietnamese homestay businesses showed a strong capacity for adaptation, we discuss several factors influencing the implementation of adaptation and recovery strategies. This study contributes to the understanding of how micro, small, and medium-sized enterprises can adapt to external shocks and new externalities, and provides practical implications for homestay owners and stakeholders in relation to their post-pandemic business recovery.

Taking into consideration the scarcity of research on volunteering at conferences, this study aims to examine conference volunteers’ motivations by using the push–pull framework. This study explores the motivations for volunteering in a sample of 26 volunteers at the First World Conference on Tourism for Development by using semi-structured interviews. The data are analyzed by thematic analysis. The findings show that conference volunteers are driven by push factors of self-development and utilitarian and pull factors of learning opportunity, experience and reputation. The results indicate relationships between push and pull factors of conference volunteers. Implications for research and practice are provided.

Trusted positioning data are very important for the fusion of Bluetooth fingerprint positioning (BFP) and Wi-Fi fingerprint positioning (WFP). This paper proposes an adaptive Bluetooth/Wi-Fi fingerprint positioning method based on Gaussian process regression (GPR) and relative distance (RD), which can choose trusted positioning results for fusion. In the offline stage, measurements of the Bluetooth and Wi-Fi received signal strength (RSS) were collected to construct Bluetooth and Wi-Fi fingerprint databases, respectively. Then, fingerprint positioning error prediction models were built with GPR and data from the fingerprint databases. In the online stage, online Bluetooth and Wi-Fi RSS readings were matched with the fingerprint databases to get a Bluetooth fingerprint positioning result (BFPR) and a Wi-Fi fingerprint positioning result (WFPR). Then, with the help of RD and fingerprint positioning error prediction models, whether the positioning results are trusted was determined. The trusted result is selected as the position estimation result when there is only one trusted positioning result among the BFPR and WFPR. The mean is chosen as the position estimation result when both the BFPR and WFPR results are trusted or untrusted. Experimental results showed that the proposed method was better than BFP and WFP, with a mean positioning error of 2.06 m and a root-mean-square error of 1.449 m.