The expansion of location-based services (LBS) and their applications has led to a growing interest in localization, which can be done on the smartphone platform. Various positioning techniques can be used for indoor or outdoor positioning. Indoor positioning systems have been one of the most challenging technologies in location-based services over the past decade. Considering the increase of people activities inside buildings such as offices, hospitals, and large stores, determining the position and guidance of people inside these buildings is one of the most urgent and important issues to be discussed and challenged in the area of Location-based Services (LBS). There are various ways to determine the position inside a building. The method(s) used to determine the position in an indoor environment depends on several factors such as cost, accuracy, independence of, or dependence on the infrastructure, security, and system scalability. This study focuses on the infrastructure requirements necessary to determine the position of individuals thorough a comprehensive study of previous studies. Moreover, focusing on the Pedestrian Dead Reckoning positioning method using smartphones as an infrastructure-free method, several effective aspects of the accuracy and positioning process are examined. The effective measures examined include the use of a variety of noise filtering, combined filters (Particle filter, Kalman filter), the criterion of the of sensor data classification algorithm, the criterion of the initial point determination, the use of landmarks as checkpoints and plot maps for setting the estimated position, the detection criteria and estimation of the length of the step, and the user direction estimation criteria. The particle filter has good accuracy in small-scale areas, but in large-scale areas, it is out of date and has problems due to the limited source of the smartphone. In studies, Kalman filter has been used to integrate the information of different sensors, some of which have reached the desired accuracy according to the state model and the measurement model. Given that the generalized Kalman filter has a simple formula for nonlinear estimation, the linearization of the positioning problem causes an error in the Jacobi Matrix model and reduces the accuracy of the estimate, which negatively affects the cost of calculations and system timeliness. Step length varies from person to person. In fact, there should be a variable associated with pedestrians in the step estimation model. Also, a person's walking rate during a walk is not constant. Accordingly, assuming a constant value of step length for users causes an error during positioning and a large drift at the end of the path. Determining the heading is one of the most challenging parts of the PDR system because the heading error leads to a quick increase in the positioning error. It is difficult to determine the reliable heading in the environments with high magnetic disturbances. Another problem is that the heading of the smartphone may vary with the heading of the pedestrian movement. Therefore, two main tasks must be performed before implementing indoor positioning. One of them is to determine the heading of the smartphone. Another is to infer the offset heading between the smartphone and the pedestrian movement. Therefore, determining the state of the smartphone is necessary for specifying the heading of the pedestrian movement.  Finally, the advantages and disadvantages of each of the infrastructure-based and infrastructure-free methods are compared and evaluated. Also, the research uses algorithms such as Naive Baye, MLP, SVM, DT and KNN to classify the type of user movement and phone holding mode.