Location Tracking

Location-based social networks (LBSNs) feature friend discovery by location proximity that has attracted hundreds of millions of users world-wide. While leading LBSN providers claim the well-protection of their users' location privacy, for the first time we show through real world attacks that these claims do not hold. In our identified attacks, a malicious individual with the capability of no more than a regular LBSN user can easily break most LBSNs by manipulating location information fed to LBSN client apps and running them as location oracles. We further develop an automated user location tracking system and test it on leading LBSNs including Wechat, Skout, and Momo. We demonstrate its effectiveness and efficiency via a 3 week real-world experiment on 30 volunteers and show that we could geo-locate any target with high accuracy and readily recover his/her top 5 locations. Finally, we also develop a framework that explores a grid reference system and location classifications to mitigate the attacks. Our result serves as a critical security reminder of the current LBSNs pertaining to a vast number of users.

Existing researches on location tracking focus either entirely on indoor or entirely on outdoor by using different devices and techniques. Several solutions have been proposed to adopt a single location sensing technology that fits in both situations. This paper aims to track a user position in both indoor and outdoor environments by using a single wireless device with minimal tracking error. RSSI (received signal strength indication) technique together with enhancement algorithms is proposed to cater this solution. The proposed RSSI-based tracking technique is divided into two main phases, namely the calibration of RSSI coefficients (deterministic phase) and the distance along with position estimation of user location by iterative trilateration (probabilistic phase). A low complexity RSSI smoothing algorithm is implemented to minimize the dynamic fluctuation of radio signal received from each reference node when the target node is moving. Experiment measurements are carried out to analyze the sensitivity of RSSI. The results reveal the feasibility of these algorithms in designing a more accurate real-time position monitoring system.

The research on indoor localization has received great interest in recent years. This has been fueled by the ubiquitous distribution of electronic devices equipped with a radio frequency (RF) interface. Analyzing the signal fluctuation on the RF-interface can, for instance, solve the still open issue of ubiquitous reliable indoor localization and tracking. Device bound and device free approaches with remarkable accuracy have been reported recently. In this paper, we present an accurate device-free passive (DfP) indoor location tracking system that adopts channel state information (CSI) readings from off-the-shelf WiFi 802.11n wireless cards. The fine-grained subchannel measurements for multiple input multiple output orthogonal frequency-division multiplexing PHY layer parameters are exploited to improve localization and tracking accuracy. To enable precise positioning in the presence of heavy multipath effects in cluttered indoor scenarios, we experimentally validate the unpredictability of CSI measurements and suggest a probabilistic fingerprint-based technique as an accurate solution. Our scheme further boosts the localization efficiency by using principal component analysis to filter the most relevant feature vectors. Furthermore, with Bayesian filtering, we continuously track the trajectory of a moving subject. We have evaluated the performance of our system in four indoor environments and compared it with state-of-the-art indoor localization schemes. Our experimental results demonstrate that this complex channel information enables more accurate localization of nonequipped individuals.

Indoor location tracking of mobile robots or transport vehicles using wireless technology is attractive for many applications. IEEE 802.15.4a wireless networks offer an inexpensive facility for localizing mobile devices by time-based range measurements. The main problems of time-based range measurements in indoor environments are errors by multipath and non-line-of-sight (NLOS) signal propagation. This paper describes indoor tracking using range measurements and an Extended Kalman Filter with NLOS mitigation. The commercially available nanoLOC wireless network is utilized for range measurements. The paper presents experimental results of tracking a forklift truck in an industrial environment.

Nowadays, as smartphones are becoming more and more powerful, applications providing location based services have been increasingly popular. Many, if not all, smartphones are equipped with a powerful sensor set (GPS, WiFi, the acceleration sensor, the orientation sensor, etc.), which makes them capable of accomplishing complicated tasks. Unfortunately, as the core enabler of most location tracking applications on smartphones, GPS incurs an unacceptable energy cost that can cause the complete battery drain within a few hours. Although GPS is often preferred over its alternatives, the coverage areas of GPS are still limited (GPS typically cannot function indoors). To this end, our goal in this paper is to improve the energy-efficiency of traditional location tracking service as well as to expand its coverage areas. In this paper, we introduce SensTrack, a location tracking service that leverages the sensor hints on the smartphone to reduce the usage of GPS. SensTrack selectively executes a GPS sampling using the information from the acceleration and orientation sensors and switches to the alternate location sensing method based on WiFi when users move indoors. A machine learning technique, Gaussian process regression, is then employed to reconstruct the trajectory from the recorded location samples. We implemented a prototype on an Android smartphone that can sample the related sensors during the user's movement and collect the sensor data for further processing on PCs. Evaluation on traces from real users demonstrates that SensTrack can significantly reduce the usage of GPS and still achieve a high tracking accuracy.

Abstract Existing researches on location tracking focus either entirely on indoor or entirely on outdoor by using different devices and techniques. Several solutions have been proposed to adopt a single location sensing technology that fits in both situations. This paper aims to track a user position in both indoor and outdoor environments by using a single wireless device with minimal tracking error. RSSI (Received Signal Strength Indication) technique together with enhancement algorithms is proposed to cater this solution. The proposed RSSI-based tracking technique is divided into two main phases, namely the calibration of RSSI coefficients (deterministic phase) and the distance along with position estimation of user location by iterative trilateration (probabilistic phase). A low complexity RSSI smoothing algorithm is implemented to minimize the dynamic fluctuation of radio signal received from each reference node when the target node is moving. Experiment measurements are carried out to analyze the sensitivity of RSSI. The results reveal the feasibility of these algorithms in designing a more accurate real-time position monitoring system

The development pace of location tracking research is highly tied up with the advancement of wireless sensor network (WSN) and wireless technologies. As sensor nodes in WSN became smaller and stronger, the ability of processing information and managing network operation also became more intelligent. This can be observed from the application of tracking from coarse-grained to fine-grained advancement. In coarse-grained tracking such as (Zhao, et al., 2003), the location of target is just detected by two or more sensor nodes along the movement path of the target. The coordinate of the tracked target is then determined by averaging the location coordinates of those sensor nodes which are able to detect the target. Using this approach, the accuracy and resolution of location estimation is affected by the density of sensor nodes in the area. In fine-grained tracking such as (Smith, et al., 2004), three or more sensor nodes are responsible to track the target in the area. Instead of just detection, the distances between the target and the sensor nodes are measured. The determination of distance between two entities is called “ranging”. Using the measured distances, the exact location coordinate of the target can be computed by angulation or lateration techniques (Hightower, et al., 2001). Therefore, increasing the node density of the area does not really increase the accuracy of location estimation. It rather depends on the accuracy of the ranging method. This chapter presents the authors’ research investigation of developing an indoor tracking and localization system. The experimental system was tested and achieved in the laboratory of Dongseo University for supporting author’s PhD studies. The thesis (Pu, 2009) provides further technical details for the design and implementation of the tracking system. For the ease of reading, this chapter was organized as follows: section 1 gives overall fundamentals of location tracking systems, from every aspect of considerations. Section 2 analyzes the nature of wireless ranging using received signal strength indicator, especially for the case of indoor signal propagation and ranging. Section 3 provides the complete flow of designing and implementing indoor location system based on received signal strength. Finally, section 4 concludes the whole work. 11

Location tracking operations in a personal communications service (PCS) network are expensive. A location tracking algorithm called pointer forwarding has been proposed to reduce the location update cost. The key observation behind forwarding is that if users change PCS registration areas (RAs) frequently, but receive calls relatively infrequently, it should be possible to avoid registrations at the home-location register (HLR) database by simply setting up a forwarding pointer from the previous visitor-location register (VLR). Calls to a given user will first query the user's HLR to determine the first VLR, which the user was registered at, and then follow a chain of forwarding pointers to the user's current VLR. To reduce the "find" cost in call delivery, the PCS provider may distribute HLR databases in the network. This paper integrates the concept of distributed HLRs with pointer forwarding, and the new scheme is referred to as the pointer forwarding with distributed HLR (PFDHLR). Since no registration to the HLR is performed in the pointer forwarding scheme when a user moves to the new locations, the cost of updating multiple HLRs is eliminated in PFDHLR. Our study indicates that PFDHLR may significantly reduce the mobility management cost compared with the single HLR approach.

This paper describes a new method of time of arrival (TOA) estimation for OFDM signals in indoor multipath environments for application to location tracking systems based on time difference of arrival. The problem addressed is to estimate the line of sight (LOS) time delay between the transmitter and the receiver when the intervening multipath channel is not known. In our approach, we do not attempt to determine the detailed multipath structure of the channel but, instead, use statistical knowledge of the multipath environment to aid in the estimation of the single parameter of real interest. We separate the gross time delay (LOS time delay) from the fine multipath structure of the channel. We then assume a statistical model of the multipath structure and develop the maximum likelihood estimator of the LOS time delay under the assumed model. Analytical development reduces the estimator to an efficient form in which most of the computational effort is done off line. Simulation results are presented which compare the performance of the new estimator to one which uses the well-known MODE technique for estimating the TOA and the new estimator is shown to compare favorably, especially as the number of paths increases. The simulations also show that the estimator is robust to variations in the assumed statistical model.

We propose a cellular-based location tracking system to make the best use of the range measurements and the supplementary information obtainable in cellular systems. The tracking system employs a Kalman filter for its flexibility in taking intermittent measurements and auxiliary information. Past location estimates and sector information are used to relax the minimum number of range measurements required, and therefore broaden the location coverage area. We analyze the probability of resolving the ambiguity when only two range measurements are available. A non-line-of-sight (NLOS) mitigation algorithm developed by Pi-Chun Chen (see IEEE WCNC, 1999) is used to improve erroneous location estimates corrupted by NLOS errors. The performance of the proposed cellular based location tracking system is evaluated through simulations.