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. 2018 Nov 4;18(11):3766.
doi: 10.3390/s18113766.

A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building

Soumya Prakash Rana  1 , Javier Prieto  2 , Maitreyee Dey  3 , Sandra Dudley  4 , Juan Manuel Corchado  5
Affiliations

A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building

Soumya Prakash Rana et al. Sensors (Basel). .

Abstract

Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved.

Keywords: fingerprinting; indoor localization; machine learning; received signal strength indicator.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Analysis of publication statistics in the IPS research field: (a) publication statistics of IPSs during last five year; and (b) publication statistics of IPSs where RSSI has been considered as potential solution.
Figure 2
Figure 2
Proposed multi-agent architecture.
Figure 3
Figure 3
Actual online path obtained from RSS values and their distribution in 2D plane: (a) the actual online trajectory; and (b) the dots with the same colors are received RSS values at the same position.
Figure 4
Figure 4
Prediction results obtained from DT classifier: (a) the route predicted by DT classifier; and (b) 2D decision boundary formed by DT classifier.
Figure 5
Figure 5
The occurrence of error in each coordinate from DT classification during online phase.
Figure 6
Figure 6
Prediction results obtained from 3NNEu classifier: (a) the route predicted by 3NNEu classifier; and (b) 2D decision boundary formed by 3NNEu classifier.
Figure 7
Figure 7
The occurrence of error in each coordinate from 3NNEu classification during online phase.
Figure 8
Figure 8
Predicted Paths by SVM: (a) the route predicted by SVMRBF classifier; and (b) 2D decision boundary formed by SVMRBF classifier.
Figure 9
Figure 9
The occurrence of error in each coordinate from SVMRBF classification during online phase.
Figure 10
Figure 10
Prediction results obtained from 1NNJa classifier: (a) the route predicted by 1NNJa classifier; and (b) 2D decision boundary formed by 1NNJa classifier.
Figure 11
Figure 11
The occurrence of error in each coordinate from 1NNJa classification during online phase.
Figure 12
Figure 12
Comparison of predicted paths attained from different ML algorithms.
Figure 13
Figure 13
Comparison of proposed methods based on different statistical metrics: (a) accuracy; (b) true positive rate; (c) true negative rate; (d) positive predictive value; and (e) negative predictive value.
See this image and copyright information in PMC

References

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