A Survey of Positioning Techniques and Location Based Services in Wireless Networks

International audiencePositioning techniques are known in a wide variety of wireless radio access technologies. Traditionally, Global Positioning System (GPS) is the most popular outdoor positioning system. Localization also exists in mobile networks such as Global System for Mobile communications (GSM). Recently, Wireless Local Area Networks (WLAN) become widely deployed, and they are also used for localizing wireless-enabled clients. Many techniques are used to estimate client position in a wireless network. They are based on the characteristics of the received wireless signals: power, time or angle of arrival. In addition, hybrid positioning techniques make use of the collaboration between different wireless radio access technologies existing in the same geographical area. Client positioning allows the introduction of numerous services like real-time tracking, security alerts, informational services and entertainment applications. Such services are known as Location Based Services (LBS), and they are useful in both commerce and security sectors. In this paper, we explain the principles behind positioning techniques used in satellite networks, mobile networks and Wireless Local Area Networks. We also describe hybrid localization methods that exploit the coexistence of several radio access technologies in the same region, and we classify the location based services into several categories. When localization accuracy is improved, position-dependant services become more robust and efficient, and user satisfaction increases

Technologies and solutions for location-based services in smart cities: past, present, and future

Location-based services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS rely on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This paper elaborates upon the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas

Hybrid cargo-level tracking system for logistics

In this paper, we propose a hybrid cargo-level tracking system for logistics. We highlight the special system requirements, discuss the design issues and identify the design principles. Then we propose an innovative hybrid system. As far as we know, this is the first system that exploits both infrastructure-based and infrastructure-less positioning schemes for practical cargo-level tracking. Compared with existing systems, the proposed system provides a ubiquitous cargo-level tracking solution with enhanced availability, reliability, and lower total costs. © 2010 IEEE.published_or_final_versionThe 71st IEEE Vehicular Technology Conference (VTC2010-Spring), Taipei, Taiwan, 16-19 May 2010. In Proceedings of 71st IEEE-VTC (Spring), 2010, p. 1-

Improvement of mobile trilateration accuracy with modified geo-location techniques.

Masters Degree. University of KwaZulu-Natal, Durban.Abstract available in pdf

Software-only TDOA/RTF positioning for 3G WCDMA wireless network

A hybrid location finding technique based oil time difference of arrival (TDOA) with round-trip time (RTT) measurements is proposed for a wideband code division Multiple access (WCDMA) network. In this technique, a mobile station measures timing from at least three base stations using user equipment receive-transmit (UE Rx-Tx) time difference and at least three base stations measure timing from the mobile station using RTT. The timing measurements of mobile and base stations are then combined to solve for both the location of the mobile and the synchronization offset between base stations. A software-only geolocation system based on the above mobile/base stations timing measurements is implemented in Matlab platform and the performance of the system is investigated using large-scale propagation models

A survey on wireless indoor localization from the device perspective

With the marvelous development of wireless techniques and ubiquitous deployment of wireless systems indoors, myriad indoor location-based services (ILBSs) have permeated into numerous aspects of modern life. The most fundamental functionality is to pinpoint the location of the target via wireless devices. According to how wireless devices interact with the target, wireless indoor localization schemes roughly fall into two categories: device based and device free. In device-based localization, a wireless device (e.g., a smartphone) is attached to the target and computes its location through cooperation with other deployed wireless devices. In device-free localization, the target carries no wireless devices, while the wireless infrastructure deployed in the environment determines the target’s location by analyzing its impact on wireless signals. This article is intended to offer a comprehensive state-of-the-art survey on wireless indoor localization from the device perspective. In this survey, we review the recent advances in both modes by elaborating on the underlying wireless modalities, basic localization principles, and data fusion techniques, with special emphasis on emerging trends in (1) leveraging smartphones to integrate wireless and sensor capabilities and extend to the social context for device-based localization, and (2) extracting specific wireless features to trigger novel human-centric device-free localization. We comprehensively compare each scheme in terms of accuracy, cost, scalability, and energy efficiency. Furthermore, we take a first look at intrinsic technical challenges in both categories and identify several open research issues associated with these new challenges.</jats:p

Applying Rprop Neural Network for the Prediction of the Mobile Station Location

Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) environments, then one need to integrate all available heterogeneous measurements to increase the location accuracy. In this paper we propose a novel algorithm that combines both time of arrival (TOA) and angle of arrival (AOA) measurements to estimate the MS in NLOS environments. The proposed algorithm utilizes the intersections of two circles and two lines, based on the most resilient back-propagation (Rprop) neural network learning technique, to give location estimation of the MS. The traditional Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have convergence problems, and even if the measurements are fairly accurate, the performance of these algorithms depends highly on the relative position of the MS and BSs. Different NLOS models were used to evaluate the proposed methods. Numerical results demonstrate that the proposed algorithms can not only preserve the convergence solution, but obtain precise location estimations, even in severe NLOS conditions, particularly when the geometric relationship of the BSs relative to the MS is poor