Efficient and Robust WiFi Indoor Positioning Using Hierarchical Navigable Small World Graphs

Lima, Max Willian Soares; Oliveira, Horácio A. B. F.; Santos, Eulanda M. dos; Moura, Edleno Silva de; Costa, Rafael Kohler; Levorato, Marco

doi:10.1109/nca.2018.8548076

Cited by 5 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, the most widespread technology is the Global Navigation Satellite Systems (GNSS), which includes the Global Positioning System (GPS). Unfortunately, GNSS does not perform well indoors, as it needs, among other factors, direct line of sight to the satellites and the device whose location one wants to know (Soares Lima et al, 2018).…”

Section: Contextmentioning

confidence: 99%

Robust RSSI-Based Indoor Positioning System Using K-Means Clustering and Bayesian Estimation

et al. 2021

View full text Add to dashboard Cite

This work proposes a new indoor positioning system, named KLIP, that uses the Kmeans clustering algorithm to split the environment into different sets of log-distance propagation models in order to better characterize the indoor environment and further improve the position estimation using Bayesian inference. The proposed method is validated in a large-scale, real-world scenario composed of Bluetooth Low Energy (BLE)-based devices. It is demonstrated, throughout the work, that the addition of location information of training points to the received signal strength indicator (RSSI) as an attribute for the clustering step improves the positioning accuracy. Moreover, the obtained results show that the solution outperforms the naive Bayesian estimation up to 12% -regarding the positioning accuracy -and the broadly deployed kNN for reduced training dataset size -regarding both accuracy and online processing time. In this sense, KLIP proves to be an efficient and scalable alternative when both site-survey effort and energy consumption constraints must be taken into account.

show abstract

Section: Contextmentioning

confidence: 99%

Robust RSSI-Based Indoor Positioning System Using K-Means Clustering and Bayesian Estimation

et al. 2021

View full text Add to dashboard Cite

show abstract

“…To illustrate how our simulation model works, a simple scenario is built according to Figure 2. The power signal P t is transmitted by the AP and values of RSS are registered into the variable r described in Equation 2concerning test points (1) and (2). The random component of the model σ is generated artificially using an adequate function from Octave [22].…”

Section: Construction Of the Simulation Environmentmentioning

confidence: 99%

“…In this case, the most widespread technology is the Global Positioning System (GPS). Unfortunately, GPS does not perform well indoors, as it needs, among other factors, direct line of sight to the satellites and the device whose location one wants to know [2]. An indoor positioning system must take into account some factors whose effects compromise the accuracy when estimating the location.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Approach for Optimal Design of Indoor Positioning Systems

Pinto¹,

Oliveira²,

Souto³

2020

Preprint

View full text Add to dashboard Cite

Indoor Positioning Systems (IPSs) are designed to provide solutions for location-based services. Wireless local area network (WLAN)-based positioning systems are the most widespread around the globe and are commonly found to have a ready-to-use infrastructure composed mostly of access points (APs). They provide useful information on signal strength to be processed by adequate location algorithms, which are not always capable of achieving the desired localization error only by themselves. In this sense, this paper proposes a new method to improve the accuracy of IPSs by optimizing some of their most relevant infrastructure components. Included are the arrangement of APs over the environment, the number of reference points (RPs), and the number of samples per location estimation test. A simulation environment is also proposed, in which the impact of key influencing factors on system accuracy is analyzed. Finally, a case study is simulated to validate an optimal combination of design parameters and its compliance with the requirements of localization error and the limited number of access points. Our simulation results clearly show that the desired localization accuracy, which is set as a goal, can be achieved while maintaining the factors already mentioned at minimal levels, which decreases both system deployment costs and computational effort.

show abstract

Indoor-Location Classification Using RF Signatures

Chawathe

2019

2019 IEEE 18th International Symposium on Network Computing and Applications (NCA)

View full text Add to dashboard Cite

Efficient and Robust WiFi Indoor Positioning Using Hierarchical Navigable Small World Graphs

Cited by 5 publications

References 18 publications

Robust RSSI-Based Indoor Positioning System Using K-Means Clustering and Bayesian Estimation

Robust RSSI-Based Indoor Positioning System Using K-Means Clustering and Bayesian Estimation

A Probabilistic Approach for Optimal Design of Indoor Positioning Systems

Indoor-Location Classification Using RF Signatures

Contact Info

Product

Resources

About