Comparative study of classification techniques for indoor localization of mobile devices

GPS and GLONASS are used worldwide to locate the devices using satellites but it cannot locate objects under the roof. Therefore different sensors are required to be deployed inside for indoor localization of the devices. Different techniques have been developed including angle of arrival technique, triangulation, trilateration, Artificial Neural Networks, KNN Classification techniques and Bayesian classification techniques. One of the most popular technique known as Naive Bayes Technique is mostly used for indoor localization of the objects and devices. Naive Bayes classifier assumes conditional independence between the attributes but in real world this is not the case. In order to overcome dependence and zero probability issue of Naive Bayes algorithm, different variants of Naive Bayes technique have been developed. In this paper we have done a comparative study of different Naive Bayes theorem based classification techniques and some other classification techniques for location estimation of device in indoor environment are done. The accuracy and efficiency of different techniques including SVM, SMO, Random Forest, Random Trees, Augmented Naive Bayes, Hidden Naive Bayes, Fine Grained Naive Bayes and Multinomial Naive Bayes technique are compared to find the best location estimation algorithm.