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A Survey of Indoor Localization Systems and Technologies
TL;DR: A detailed survey of different indoor localization techniques such as Angle of Arrival (AoA), Time of Flight (ToF), return time of flight (RTOF), Received Signal Strength (RSS); based on technologies such as WiFi, Radio Frequency Identification Device (RFID), Ultra Wideband (UWB), Bluetooth and systems that have been proposed in the literature is presented in this article.
Abstract: Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques such as Angle of Arrival (AoA), Time of Flight (ToF), Return Time of Flight (RTOF), Received Signal Strength (RSS); based on technologies such as WiFi, Radio Frequency Identification Device (RFID), Ultra Wideband (UWB), Bluetooth and systems that have been proposed in the literature. The paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.
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01 Jan 2014
TL;DR: This article surveys the new trend of channel response in localization and investigates a large body of recent works and classify them overall into three categories according to how to use CSI, highlighting the differences between CSI and RSSI.
Abstract: The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.
612Â citations
TL;DR: This survey gives a comprehensive review of state-of-the-art indoor localization methods and localization improvement methods using maps, spatial models, and landmarks.
Abstract: Indoor localization is essential for healthcare, security, augmented reality gaming, and many other location-based services. There is currently a wealth of relevant literature on indoor localization. This article focuses on recent advances in indoor localization methods that use spatial context to improve the location estimation. Spatial context in the form of maps and spatial models have been used to improve the localization by constraining location estimates in the navigable parts of indoor environments. Landmarks such as doors and corners, which are also one form of spatial context, have proved useful in assisting indoor localization by correcting the localization error. This survey gives a comprehensive review of state-of-the-art indoor localization methods and localization improvement methods using maps, spatial models, and landmarks.
117Â citations
TL;DR: Terahertz (THz)-based chipless RFID localization system that enables a smart object localizing itself using the infrastructure composed from reference chipless tags and can reach superior accuracy of millimeter-levels is proposed.
Abstract: Highly accurate indoor localization based on significantly low complex infrastructure has recently gained great interest for a variety of innovative location-based applications. In this regards, the chipless radio frequency identification (RFID) system is presented to be the low-cost solution, while time-based ranging using the ultrawide-band spectrum is promising to offer precise ranging capability. However, the current wide-band systems suffer from the spectrum and power limitations, which restrict the function of chipless RFID-based localization systems. Therefore, we propose terahertz (THz)-based chipless RFID localization system that enables a smart object localizing itself using the infrastructure composed from reference chipless tags. In more details, THz band offers huge bandwidth providing superior-resolution localization and large coding capacity. Moreover, we utilize the combination between dielectric resonator (DR) and lens to be designed as a frequency-coded chipless tag, where this combination increases the radar cross section of the chipless tags and, hence, extends their coverage zone. This cost-efficient design of the tag enables the dense deployment of low-cost infrastructure acting as reference anchors. Furthermore, we investigate the link budget of the proposed system in order to characterize the tag and distance-dependent spectral windows that are feasible for RFID-based localization. Afterward, the time-domain backscattered signal from a DR-Lens tag is analyzed in order to perform ranging and to calculate the relative distances between the DR-Lens tags and the reader leading to determining the reader position. Measurements are performed to prove the concept of the DR-Lens tag, while the numerical simulation is conducted to evaluate the proposed localization system. Simulation results show that the proposed system can reach superior accuracy of millimeter-levels.
76Â citations
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TL;DR: In this paper, a comprehensive survey is presented for multidimensional scaling and MDS based localization techniques in WSNs, Internet of Things (IoT), cognitive radio networks, and 5G networks.
Abstract: Current and future wireless applications strongly rely on precise real-time localization. A number of applications such as smart cities, Internet of Things (IoT), medical services, automotive industry, underwater exploration, public safety, and military systems require reliable and accurate localization techniques. Generally, the most popular localization/ positioning system is the Global Positioning System (GPS). GPS works well for outdoor environments but fails in indoor and harsh environments. Therefore, a number of other wireless local localization techniques are developed based on terrestrial wireless networks, wireless sensor networks (WSNs) and wireless local area networks (WLANs). Also, there exist localization techniques which fuse two or more technologies to find out the location of the user, also called signal of opportunity based localization. Most of the localization techniques require ranging measurements such as time of arrival (ToA), time difference of arrival (TDoA), direction of arrival (DoA) and received signal strength (RSS). There are also range-free localization techniques which consider the proximity information and do not require the actual ranging measurements. Dimensionality reduction techniques are famous among the range free localization schemes. Multidimensional scaling (MDS) is one of the dimensionality reduction technique which has been used extensively in the recent past for wireless networks localization. In this paper, a comprehensive survey is presented for MDS and MDS based localization techniques in WSNs, Internet of Things (IoT), cognitive radio networks, and 5G networks.
42Â citations
15 Jan 2019
34Â citations
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TL;DR: This survey is directed to those who want to approach this complex discipline and contribute to its development, and finds that still major issues shall be faced by the research community.
12,539Â citations
TL;DR: In this article, a description of the multiple signal classification (MUSIC) algorithm, which provides asymptotically unbiased estimates of 1) number of incident wavefronts present; 2) directions of arrival (DOA) (or emitter locations); 3) strengths and cross correlations among the incident waveforms; 4) noise/interference strength.
Abstract: Processing the signals received on an array of sensors for the location of the emitter is of great enough interest to have been treated under many special case assumptions. The general problem considers sensors with arbitrary locations and arbitrary directional characteristics (gain/phase/polarization) in a noise/interference environment of arbitrary covariance matrix. This report is concerned first with the multiple emitter aspect of this problem and second with the generality of solution. A description is given of the multiple signal classification (MUSIC) algorithm, which provides asymptotically unbiased estimates of 1) number of incident wavefronts present; 2) directions of arrival (DOA) (or emitter locations); 3) strengths and cross correlations among the incident waveforms; 4) noise/interference strength. Examples and comparisons with methods based on maximum likelihood (ML) and maximum entropy (ME), as well as conventional beamforming are included. An example of its use as a multiple frequency estimator operating on time series is included.
12,446Â citations
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
Abstract: Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.
11,409Â citations
26 Mar 2000
TL;DR: RADAR is presented, a radio-frequency (RF)-based system for locating and tracking users inside buildings that combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications.
Abstract: The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.
8,667Â citations
TL;DR: This review paper summarizes the current state-of-the-art IoT in industries systematically and identifies research trends and challenges.
Abstract: Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of radio-frequency identification (RFID), and wireless, mobile, and sensor devices. A wide range of industrial IoT applications have been developed and deployed in recent years. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art IoT in industries systematically.
4,145Â citations