A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

A Testbed for Real-Time Performance Evaluation of RSS-based Indoor Geolocation Systems in Laboratory Environment

Recently, there has been an enormous growth of interests in geolocation applications that demand an accurate estimation of the user’s location in indoor areas. The traditional geolocation system, GPS, which was designed for being used in outdoor environments, does not perform well in indoor areas, causing frequent inaccuracies in location estimation. Therefore the need for more accurate positioning systems and even positioning techniques is a motivation for researchers to turn their attention into indoor positioning systems. In this thesis we present a unique testbed for indoor geolocation system’s real-time performance evaluation. Then we present a real-time performance evaluation of a sample indoor positioning system. We make a comparison between the simulated results of the performance evaluation of the positioning engine and the real-time performance evaluation of the positioning system. Finally, we perform a sensitivity analysis for Ekahauâ ¾¢ indoor positioning engine. We show that the simulation with the introduced testbed yields the same results as one would obtain by evaluating the performance of the positioning system by means of massive measurement campaigns. Running the testbed for several measurement campaigns for different scenarios enabled us to compare the results and study the effect of selected parameters on the performance of the positioning system. We also perform primitive error analysis in terms of distance error to verify the validity of the result obtained with the testbed. We show that under the same configuration both real-time performance evaluation and simulated performance evaluation will yield same result with respect to position error. We also use error modeling to determine which error model is best matched to the observed indoor positioning error. Amongst all of the possibilities of choosing methods of positioning, we focused on the Received Signal Strength (RSS) based method along with fingerprinting. Briefly said, profiles previously gathered by measurement or simulation will decide on the location of mobile terminal if a new profile comes in. It is worth mentioning that previous work similar to this testbed has been done for outdoor areas according to Ekahau\u27s white paper. Their work is mainly focused on outdoor environment, in which multipath does not exist. In this research effort we tried to analyze the effect of different parameters on sensitivity of indoor positioning systems who suffer from multipath. Different setups for simulating real-time radio channels have been studied in literature, but still not focused on indoor areas

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

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

Joint received signal strength, angle-of-arrival, and time-of-flight positioning

This paper presents a software positioning framework that is able to jointly use measured values of three parameters: the received signal strength, the angle-of-arrival, and the time-of-flight of the wireless signals. Based on experimentally determined measurement accuracies of these three parameters, results of a realistic simulation scenario are presented. It is shown that for the given configuration, angle-of-arrival and received signal strength measurements benefit from a hybrid system that combines both. Thanks to their higher accuracy, time-of-flight systems perform significantly better, and obtain less added value from a combination with the other two parameters

Design of improved IR protocol for LED indoor positioning system

In this work, we design an infrared protocol (IRP) for light emitting diode (LED) based indoor positioning. The designed IRP compensates for the shortcomings of other existing protocols when applied to the multiple LED estimation indoor positioning model (MLEM). MLEM uses overlap of LED beams to increase accuracy of positioning. The overlap sets up a multipoint-to-point optical communication channel. The existing protocols which are designed for point-to-point links, when modified to suit the MLEM overlapping region, show a high positioning time between 3 s and 4.5 s. These values are not desirable for real time tracking. A new protocol is therefore designed to reduce the positioning time. The protocol is implemented in an experimental MLEM design using ATmega 328 microcontroller hardware. The experimental results show the new protocol reduces the positioning time to 0.5 s