The emerging technologies related to mobile data especially CDR data has great potential for mobility and transportation applications. However, it presents some challenges due to its spatio-temporal characteristics and sparseness. Therefore, in this article, we introduced a new model to refine the positioning accuracy of mobile devices using only CDR data and coverage areas locations. The adopted method has three steps: first, we discovered which model of movement (Move, Stay) is associated with the coverage areas where the mobile device was connected using a Kalman filter. Then, simultaneously we estimated the location or the position of the device. Finally, we applied map-matching to bring the positioning to the right road segment. The results are very encouraging; nevertheless, there is some enhancement that can be done at the level of movement models and map matching. For example by introducing more sophisticated movement model based on data-driven modeling and a map matching that uses the movement model type detected by matching "Stay" location to buildings and "Move" model to roads.
Being applicable for almost every scenario, mobile localization based on cellular network has gained increasing interest in recent years. Since received signal strength indication (RSSI) information is available in all mobile phones, RSSI-based techniques have become the preferred method for GSM localization. Although the GSM standard allows for a mobile phone to receive signal strength information from up to seven base stations (BSs), most of mobile phones only use the information of the associated cell as its estimated position. Therefore, the accuracy of GSM localization is seriously limited. In this paper, an algorithm for GSM localization is proposed with RSSI and Pearson's correlation coefficient (PCC). The information of seven cells, including the serving cell and six neighboring cells, is used to accurately estimate the mobile location. With redundant information, the proposed algorithm restrains the error of Cell-ID and shows good robustness against environmental change. Without any additional device or prior statistical knowledge, the proposed algorithm is implementable on common mobile devices. Furthermore, in the practical test, its maximum error is below 550 m, which is 100 m better than that of Cell-ID, and the mean error is below 150 m, which is 250 m better than Cell-ID. Localization schemes for wireless sensor networks can be classified as range-based or range-free. They differ in the information used for localization. Range-based methods use range measurements, while range-free techniques only use the content of the messages. None of the existing algorithms evaluate both types of information. Most of the localization schemes do not consider mobility. In this paper, a Sequential Monte Carlo Localization Method is introduced that uses both types of information as well as mobility to obtain accurate position estimations, even when high range measurement errors are present in the network and unpredictable movements of the nodes occur. We test our algorithm in various environmental settings and compare it to other known localization algorithms. The simulations show that our algorithm outperforms these known range-oriented and range-free algorithms for both static and dynamic networks. Localization improvements range from 12 to 49 in a wide range of conditions.