WiFi Based Indoor Localization: Application and Comparison of Machine Learning Algorithms

Because of increasing the use of smartphones, it has become easier to identify location of any user. The most popular technique for outdoor positioning is the GPS signal which is commonly used in smartphones and transport vehicles. However, position detection can not be achieved indoor with GPS. Therefore, in this study, a location determination based on WiFi signal strengths was performed indoor where user could not correctly receive the GPS signal. The data includes the strengths of seven WiFi signals that provide information about four different rooms. Based on the WiFi signal strength values coming from seven different sources to smartphone, the position of the user at which room can be determined. In this study, classification was achieved for the determination of the indoor room. Six different Machine Learning (ML) methods were applied to the classification. These methods are Artificial Neural Networks (ANN), K-Nearest Neighbors (k-NN), Decision Trees (DT), Naive Bayes (NB) Classifier, Extreme Learning Machine (ELM) and Support Vector Machines (SVM). Successful results were obtained from all the methods and these results were compared with each other.

[1]  D. Pokrajac,et al.  Classification of performers using support vector machines , 2008, 2008 9th Symposium on Neural Network Applications in Electrical Engineering.

[2]  Yi Liu,et al.  Indoor Fingerprint Positioning Based on Wi-Fi: An Overview , 2017, ISPRS Int. J. Geo Inf..

[3]  Gurneet Kaur,et al.  A REVIEW ARTICLE ON NAIVE BAYES CLASSIFIER WITH VARIOUS SMOOTHING TECHNIQUES , 2014 .

[4]  Fawzi Nashashibi,et al.  Indoor Intelligent Vehicle localization using WiFi received signal strength indicator , 2017, 2017 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM).

[5]  Hao Jiang,et al.  Accurate indoor localization and tracking using mobile phone inertial sensors, WiFi and iBeacon , 2017, 2017 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL).

[6]  Serkan Günal,et al.  A comparative study on machine learning algorithms for indoor positioning , 2015, 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA).

[7]  Sisi Zlatanova,et al.  Position, Location, Place and Area: AN Indoor Perspective , 2016 .

[8]  Kun Hou,et al.  Indoor location based on WiFi , 2015, 2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[9]  Kaveh Pahlavan,et al.  Identification of the Absence of Direct Path in ToA-Based Indoor Localization Systems , 2008, Int. J. Wirel. Inf. Networks.

[10]  Moustafa Youssef,et al.  Indoor Localization , 2017, Encyclopedia of GIS.

[11]  Yu-Liang Hsu,et al.  A Wearable Inertial Pedestrian Navigation System With Quaternion-Based Extended Kalman Filter for Pedestrian Localization , 2017, IEEE Sensors Journal.

[12]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[13]  Saurabh Srivastava,et al.  Comparative Analysis of Decision Tree Classification Algorithms , 2013 .

[14]  Kin K. Leung,et al.  A Survey of Indoor Localization Systems and Technologies , 2017, IEEE Communications Surveys & Tutorials.

[15]  Seung Hyong Rhee,et al.  Indoor positioning using Wi-Fi fingerprint with signal clustering , 2017, 2017 International Conference on Information and Communication Technology Convergence (ICTC).

[16]  Silvio Savarese,et al.  Comparing image classification methods: K-nearest-neighbor and support-vector-machines , 2012 .

[17]  Rajen B. Bhatt,et al.  User Localization in an Indoor Environment Using Fuzzy Hybrid of Particle Swarm Optimization & Gravitational Search Algorithm with Neural Networks , 2016, SocProS.

[18]  Neelam Sharma,et al.  INTRUSION DETECTION USING NAIVE BAYES CLASSIFIER WITH FEATURE REDUCTION , 2012 .

[19]  David Mascharka,et al.  Machine Learning for Indoor Localization Using Mobile Phone-Based Sensors , 2015, ArXiv.

[20]  Xueyuan Zhou,et al.  Feature Analysis in Indoor Positioning , 2017, 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks & 2017 11th International Conference on Frontier of Computer Science and Technology & 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC).

[21]  Kevin Leyton-Brown,et al.  Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms , 2012, KDD.

[22]  K. Thanushkodi,et al.  An Improved k-Nearest Neighbor Classification Using Genetic Algorithm , 2010 .

[23]  Muhammet Fatih Aslan,et al.  Different Wheat Species Classifier Application of ANN and ELM , 2017 .

[24]  Vandana,et al.  Survey of Nearest Neighbor Techniques , 2010, ArXiv.

[25]  Feng Zhao,et al.  A reliable and accurate indoor localization method using phone inertial sensors , 2012, UbiComp.

[26]  Joseph E. Beck,et al.  Naive Bayes Classifiers for User Modeling , 1999 .

[27]  Cong Chao,et al.  An Innovative Indoor Location Algorithm Based on Supervised Learning and WIFI Fingerprint Classification , 2017 .

[28]  Mohd Murtadha Mohamad,et al.  Wireless LAN/FM radio-based robust mobile indoor positioning: An initial outcome , 2014 .

[29]  Hao Jiang,et al.  An online sequential extreme learning machine approach to WiFi based indoor positioning , 2014, 2014 IEEE World Forum on Internet of Things (WF-IoT).

[30]  Shuichi Yoshino,et al.  A new in-door location detection method adopting learning algorithms , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[31]  Bosheng Zhou,et al.  Indoor Localization with Smart Antenna System: Multipath Mitigation with MIMO Beamforming Scheme , 2017, 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS).

[32]  Hao Jiang,et al.  A Fast and Precise Indoor Localization Algorithm Based on an Online Sequential Extreme Learning Machine † , 2015, Sensors.

[33]  Christian Poellabauer,et al.  Radio Frequency-Based Indoor Localization in Ad-Hoc Networks , 2017 .

[34]  Nong Ye,et al.  Naïve Bayes Classifier , 2013 .

[35]  Han Zhao,et al.  Extreme learning machine: algorithm, theory and applications , 2013, Artificial Intelligence Review.

[36]  Hong Zhao,et al.  Pedestrian Dead-Reckoning Indoor Localization Based on OS-ELM , 2018, IEEE Access.

[37]  Prabhat,et al.  Artificial Neural Network , 2018, Encyclopedia of GIS.