Article

WinIPS: WiFi-Based Non-Intrusive Indoor Positioning System With Online Radio Map Construction and Adaptation

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Abstract

WiFi fingerprinting-based Indoor Positioning System (IPS) has become the most promising solution for indoor localization. However, there are two major drawbacks that hamper its large-scale implementation. Firstly, an offline site survey process is required which is extremely time-consuming and labor-intensive. Secondly, the RSS fingerprint database built offline is vulnerable to environmental dynamics. To address these issues comprehensively, in this paper, we propose WinIPS, a WiFi-based non-intrusive IPS that enables automatic online radio map construction and adaptation, aiming for calibration-free indoor localization. WinIPS can capture data packets transmitted in existing WiFi traffic and extract the RSS and MAC addresses of both WiFi Access Points (APs) and mobile devices in a nonintrusive manner. APs can be used as online reference points for radio map construction. A novel Gaussian process regression model is proposed to approximate the non-uniform RSS distribution of an indoor environment. Extensive experiments were conducted, which demonstrated that WinIPS outperforms existing solutions in terms of both RSS estimation accuracy and localization accuracy. IEEE

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... The former is addressed at skipping the construction of the fingerprint database by estimating the values that should be observed in a discrete set of virtual reference points (VRPs) and then interpolate the remaining ones until obtaining the desired precision. This approach is taken by the authors in [12], who proposed estimating the fingerprints at the VRPs through a nonzero mean Gaussian process regression trained from a few actual measurements made at the APs. Similarly, a basic radio map is built in [13] to subsequently expand it by using the Biharmonic Spline Interpolation (BSI) method. ...
... In this study, approximately 32 h were required to gather all the data (i.e., 100 samples from 8 APs at 25 RPs). There are some approaches that could be taken in order to alleviate the cost of building the fingerprinting database, such as the one proposed in [12], but this is out of the scope of this paper. ...
... One of them is the cost of calibration, i.e., the offline stage during which the fingerprinting database is built. Some proposals have been presented to alleviate this time-demanding task, most of them consisting of reducing the fingerprinting survey to just few RPs and then interpolating the remaining data [12]. This interpolation is quite hard to achieve when the RSS is used, since radio models are quite complex and deeply bound with the environment where the fingerprinting is going to be deployed. ...
Article
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Received signal strength (RSS) has been one of the most used observables for location purposes due to its availability at almost every wireless device. However, the volatile nature of RSS tends to yield to non-reliable location solutions. IEEE 802.11mc enabled the use of the round trip time (RTT) for positioning, which is expected to be a more consistent observable for location purposes. This approach has been gaining support from several companies such as Google, which introduced that feature in the Android O.S. As a result, RTT estimation is now available in several recent off-the-shelf devices, opening a wide range of new approaches for computing location. However, RTT has been traditionally addressed to multilateration solutions. Few works exist that assess the feasibility of the RTT as an accurate feature in positioning methods based on classification algorithms. An attempt is made in this paper to fill this gap by investigating the performance of several classification models in terms of accuracy and positioning errors. The performance is assessed using different AP layouts, distinct AP vendors, and different frequency bands. The accuracy and precision of the RTT-based position estimation is always better than the one obtained with RSS in all the studied scenarios, and especially when few APs are available. In addition, all the considered ML algorithms perform pretty well. As a result, it is not necessary to use more complex solutions (e.g., SVM) when simpler ones (e.g., nearest neighbor classifiers) achieve similar results both in terms of accuracy and location error.
... Zou et al. [24] applied the wk-NN with a novel weighting scheme, where the computation was leveraged to the Signal Tendency Index (STI) instead of the raw Received Signal Strength (RSS). The distance among fingerprints was calculated with the Euclidean distance over STI features and the weights for centroid computation corresponded to the inverse of the distance of the selected neighbours. ...
... , 0.99, 1.00]); M 6−7 ) SAWKNN [48] with k max = 51 and optimal k max based on wk-NN, both with optimal threshold (γ th ∈ [0.01, 0.02, . . . , 0.99, 1.00]); M 8 ) STIWKNN [24] with optimal k; M 9−10 ) DWFWKNN [4] with the physical distance provided by authors and our implementation; and M 11−13 ) ARWKNN [49] with optimal k for City Block, Min-Max and Clark distances. In M 4−7 , optimal refers to the value for a hyperparameter providing the lowest error. ...
... The use of radio environment maps (REMs) [6] presents an effective approach for constructing dynamic interference maps within an RDZ, which can be generated for each location and frequency of interest. These radio maps are generated by collecting signal power data from deployed sensors and incorporating their corresponding location information. ...
... The antenna gain effect of a transmitter and a receiver in the received signal is captured in the path loss model in (6), using G bs (ϕ, θ), G uav (ϕ, θ). In typical terrestrial communications, the antenna gain is simply modeled by a constant gain. ...
Preprint
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Radio dynamic zones (RDZs) are geographical areas within which dedicated spectrum resources are monitored and controlled to enable the development and testing of new spectrum technologies. Real-time spectrum awareness within an RDZ is critical for preventing interference with nearby incumbent users of the spectrum. In this paper, we consider a 3D RDZ scenario and propose to use unmanned aerial vehicles (UAVs) equipped with spectrum sensors to create and maintain a 3D radio map of received signal power from different sources within the RDZ. In particular, we introduce a 3D Kriging interpolation technique that uses realistic 3D correlation models of the signal power extracted from extensive measurements carried out at the NSF AERPAW platform. Using C-Band signal measurements by a UAV at altitudes between 30 m-110 m, we first develop realistic propagation models on air-to-ground path loss, shadowing, spatial correlation, and semi-variogram, while taking into account the knowledge of antenna radiation patterns and ground reflection. Subsequently, we generate a 3D radio map of a signal source within the RDZ using the Kriging interpolation and evaluate its sensitivity to the number of measurements used and their spatial distribution. Our results show that the proposed 3D Kriging interpolation technique provides significantly better radio maps when compared with an approach that assumes perfect knowledge of path loss.
... At the same time, the precision of inconsistency detection is ensured [17To increase localization stability, Xie et al. [18] employed Spearman distance based on RSSI ranking during the period from the APs. Even if the pure RSSI readings of a set of APs in a given location may be drastically different, their ranks are considerably more likely to remain consistent, allowing a consistent fingerprint to be produced, according to [19]. The problem is that the quantity of accessible APs limits this strategy. ...
... To account for the diverse nature of the appliance, Zou et al. [19] presented the signal tendency index -weighted KNN (STI-WKNN), which improves localisation precision by using the resemblance index STI among RSSI bend forms. The raw RSSI signal is converted into a controlled object using the Procrustes analysis (PA) method [20]. ...
Conference Paper
Simultaneous Localization and Mapping (SLAM) is a mission or task that involves estimating a robot's location and reconstructing its surroundings based on sensor data. For autonomous mobile robots, the capacity to learn a regular model of its environment is a prerequisite. The fact that loops in the environment generate stimulating data association challenges is a particularly difficult problem in obtaining surroundings maps of closing loops. One of the most difficult aspects of SLAM research is loop closing. The increasing uncertainty in local mapping and the productivity of the local map representation contribute to a given environment's difficulties in loop closures. The most difficult aspect of SLAM is management uncertainty. False matches caused by a lack of clarity in the environment are one of the most significant obstacles to properly closing huge loops. When evaluating whether or not to accept a map-match, there are a variety of methodologies or similarity metrics to consider. In order to determine the least map-match error, this study examined different similarity metrics such as ((Jaccard, Euclidean, Cityblock, Chebyshev, Cosine, Spearman, Variable, Correlation). When comparing the various similarity metrics, the Cosine technique had the lowest inaccuracy of all the methods, while the Correlation method had the fastest execution time.
... Moreover, the localization accuracy of fingerprinting-based IPSs can typically be deteriorated owing to environmental dynamics. For example, RSS instability in an indoor environment is caused by various factors such as variation in temperature, humidity [10], people's movement, and occupancy changes [11]. Additionally, WiFi access points (AP) may be substituted, added, or removed owing to the renovation of the building [12,13]. ...
... Fingerprinting algorithms can generally be divided into two categories: those being deterministic and probabilistic [10,20]. In the deterministic approach, the online RSS values are matched to the recorded fingerprint in the database based on a similarity metric. ...
Article
Owing to the ubiquity of wireless networks, WiFi fingerprinting is widely applied in indoor localization. However, constructing a comprehensive radio map for WiFi indoor localization is labor intensive and time consuming. Additionally, the acquired radio map requires frequent updates owing to the dynamic nature of indoor radio environments. In this study, we aim to contribute to the alleviation of these shortcomings. We proposed a crowdsourcing landmark-based approach for radio map creation and update. First, we utilize Bluetooth low-energy beacons as landmark proximity for labeling the WiFi signals. Second, we employed a deep learning approach to generate a landmark classification model. Third, by using only a few known labeled fingerprints from the detected/classified landmarks, we utilized Gaussian process regression to interpolate a detailed radio map, and thereby, overcoming the drawbacks of sparse training data. Consequently, we can reduce the time consumed and cost associated with creating and updating traditional radio maps. Finally, we evaluated our crowdsource-based indoor localization system via real-time experiments using three different tag devices (smartphones from different vendors). The results demonstrated that the maximum likelihood estimation approach for crowdsourcing landmark-assisted localization can realize a localization accuracy of 5 m at 50% for the three devices. Furthermore, experiments show that the proposed system delivers consistent localization accuracy in a heterogeneous device environment while reducing the offline fingerprinting cost.
... STI-WKNN incorporates a similarity index, STI, which compares the RSSI vector shape of the test point with the fingerprint database. By introducing this novel weighting scheme to WKNN, STI-WKNN significantly enhances positioning accuracy, outperforming the original WKNN according to experimental results [49]. Some researchers argue that the Euclidean distance is not a reliable measure of correlation between two variables. ...
Article
Full-text available
In the indoor positioning method based on traditional KNN, the Received Signal Strength Indicator (RSSI) is commonly utilized as fingerprint information for measuring similarity, with the selection of the most matching K reference points (RPs) for positioning. However, ensuring the accuracy of the KNN fingerprint positioning method requires the collection of a substantial amount of fingerprint information and is susceptible to the complexity and stability of the indoor environment. Consequently, we propose a novel algorithm called Brownian Motion Restricted K-Nearest Neighbor (BMR-KNN). In the BMR-KNN method, we leverage the assumption that the tester’s activity exhibits a degree of adherence to the principles of Brownian motion. We utilize this assumption as prior knowledge to correct the results obtained from the KNN positioning algorithm based on RSSI. Furthermore, we propose a dynamic K value allocation algorithm (DKAA) for automatic optimization of the K value within the KNN positioning algorithm. Despite utilizing the previous location and time information, BMR-KNN achieves real-time positioning without requiring knowledge of the user’s exact moving speed and direction. Experimental evaluations conducted on two public datasets demonstrate that the new algorithm outperforms other advanced methods, including the optimal traditional KNN, and reduces the average positioning error to 3.31 m to the greatest extent.
... Research has focused on data augmentation for fingerprint generation to address the challenge of acquiring sufficient radio fingerprints for indoor localization under labor and time constraints. Various approaches have been proposed, such as Gaussian process regression (GPR) [15], [38], [39], inverse distance weighting (IDW) [40], and linear interpolation (LI) [41]. However, these methods have limitations in accurately augmenting fingerprinting data for indoor localization because of their assumptions and limited ability to capture complex spatial relationships and nonlinearities in radio signal propagation. ...
Article
Full-text available
Location estimation in indoor environments using radiofrequency (RF) has garnered considerable attention in recent years owing to the widespread adoption of mobile devices. RF-based fingerprinting—a direct approach that allows location estimation based on observed signals—relies on manual surveys during the offline phase to create a radio map with coordinates and RF measurements at multiple locations. The accuracy of RF fingerprint-based localization is related to the number of reference points. However, conventional site survey procedures tend to incur substantial expenses. To alleviate the workload of site surveys and address the challenge of incomplete fingerprint databases, we propose a data-augmentation method to complement existing fingerprint data. Our approach leverages a conditional generative adversarial network with long short-term memory (CGAN-LSTM) prediction model to effectively learn the intricate patterns inherent in the initial training data and generate high-quality synthetic data that align with the underlying data distribution. In an experimental evaluation conducted on a real testbed, our data augmentation framework increased the average localization accuracy by 15.74% compared with fingerprinting without data augmentation. Furthermore, experiments conducted in two typical indoor environments using sparse data highlighted the significant performance enhancement of the proposed approach in reducing localization error and was comparable to state-of-the-art data-augmentation methods.
... The Wi-Fi RSS fingerprint and the barometer are used for floor detection, initial position estimation, and position correction. RSS is susceptible to various environmental changes, e.g., concrete walls, moving humans, temperature and humidity [29]. Compared with PDR, Wi-Fi localization is less accurate. ...
Article
Full-text available
Indoor positioning is a thriving research area which is slowly gaining market momentum. Its applications are mostly customised, ad hoc installations; ubiquitous applications analogous to GNSS for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the Indoor Positioning and Indoor Navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing Tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site Tracks. In contrast with on-site Tracks where competitors bring their systems on site - which were impossible to organise during 2021 and 2022 - in off-site Tracks competitors download pre-recorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, Tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.
... The researchers first thought of calibrationfree schemes that combat dynamic fingerprint changes without manual intervention. Zou et al. [24] used a custom base station to collect tagged Received Signal Strength (RSS) measurements and applied Gaussian process regression for calibration-free localization. Wu et al. [25] reduced RSS fingerprint uncertainty by analyzing spatial gradients across multiple locations, enhancing localization reliability. ...
Article
Full-text available
In recent years, the rise of location-based service applications such as cashier-less shopping, mobile advertisement targeting, and geo-based augmented reality (AR) has been remarkable. These applications offer convenient and interactive experiences by utilizing indoor localization technology. One popular research area in indoor localization is passive fingerprinting localization based on Channel State Information (CSI), which uses general-purpose Wi-Fi platforms and “unconscious cooperative sensing” to achieve device-free localization. However, existing studies face challenges related to inadequate fingerprint richness, limited distinguishability, and inconsistent fingerprint features in real-world dynamic environments. To address these challenges, we prpose MFFLoc in this paper. MFFLoc extracts and processes amplitude and phase information from CSI in a 2D manner. It then fuses the amplitude and phase information using multimodal fusion representation, resulting in rich and distinguishable fused fingerprint features. This approach allows MFFLoc to achieve satisfactory accuracy with just one communication link, reducing deployment costs. To overcome the issue of inconsistent fingerprint features in dynamic environments, MFFLoc proposes an unsupervised domain adaptation method. It employs a dual-flow structure, with one flow operating in the source domain and the other in the target domain. The adaptation layer, with correlated weights, remains unshared between the two flows. Meta-learning is also used to automatically determine the most suitable adaptation layer. Through extensive 6-day experiments conducted in a dynamic indoor environment, MFFLoc showcases superior performance compared to state-of-the-art systems. It demonstrates higher localization accuracy and robustness, making it a promising solution for indoor localization applications.
... WiFi has emerged as a highly promising RF-based alternative for occupancy sensing, given its prevalence in smart buildings and extensive research exploring its feasibility for occupancy detection. For instance, WinIPS presented an occupancy scheme utilizing RSS from existing WiFi infrastructure and mobile devices [35], but it relies on occupants carrying a device for effective detection, limiting its practicality in some scenarios. Depatla et al. proposed a device-free system based on RSS measurements [12], but it lacks the adaptability to new environments. ...
Conference Paper
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Occupancy detection is vital in optimizing smart building applications , such as automatic heating/cooling, lighting systems, and energy management. Conventional occupancy sensing approaches have limitations, such as inadequate performance with stationary occupants, susceptibility to environmental factors, and limited adaptability to various indoor environments. To address these limitations , we propose FTM-Sense, a real-time adaptable sensor-free occupancy detection system by employing the WiFi fine time measurement (FTM) protocol. Our proposed system leverages the variations in FTM measurements caused by human body presence for occupancy detection purposes. FTM-Sense has the advantage of accurately detecting static occupants, and demonstrating robustness and adaptability to various indoor environments. Experimental results show that FTM-Sense achieves a 97.72% overall accuracy, with 97.68% accuracy in detecting static occupants. Furthermore, the system dynamically adapts to room configuration changes within 80 seconds without compromising detection performance. FTM-Sense is tested in both residential and office buildings, collecting over 17 hours of data in seven different rooms with varying sizes and characteristics while also evaluating three different materials for blockage in non-line-of-sight (NLOS) scenarios. This research presents a promising solution for reliable and adaptable sensor-free occupancy detection, contributing to energy management and building cost reduction. CCS CONCEPTS • Computer systems organization → Embedded systems, Sensor networks.
... The accuracy of fingerprint-based indoor localization systems is proportional to the sizes of their fingerprint databases [21]. Several interpolation-based approaches have been proposed to reduce the labor workload and time-intensive site surveys, such as Gaussian process regression (GPR) [22]- [24], linear regression [25] and inverse distance weighting [26], [27]. The GPR method [23] infers the posterior received mean RSS and its variance for sparse RPs in order to construct a fingerprint database. ...
Article
The popularity of radio frequency (RF)-based fingerprinting for indoor localization has grown owing to its relatively low cost of equipment deployment and satisfactory accuracy. However, generating a complete radio map by associating unlabeled RF signals with the corresponding location information remains challenging, especially in crowdsourcing-based fingerprinting. In this paper, we propose a semi-crowdsourced radio map construction method based on Bluetooth low energy (BLE) landmarks that harnesses reference points in the radio map for coarse localization and facilitates the labeling of location information to WiFi signals. Principally, we acquire RF-received signal strength (RSS) measurements annotating them with location coordinates recorded while a user is walking to provide an efficient method of data collection. Furthermore, we introduce a generative adversarial network (GAN)-based method to increase the amount of training data collected at each reference point and reduce human effort by augmenting the fingerprint database. Our proposed method demonstrates promising results, including improved localization accuracy and localization performance comparable to that of traditional site surveys while reducing measurement time and human effort.
... In outdoor environments, mature global navigation satellite systems (GNSSs) can achieve satisfactory accuracy, fulfilling the requirements for most outdoor localization [2]. However, indoor localization still faces numerous challenges, including localization accuracy, implementation complexity, and the stability of localization in dynamically changing environments, due to the complexity of indoor environments [3]. ...
Article
Full-text available
Given that fingerprint localization methods can be effectively modeled as supervised learning problems, machine learning has been employed for indoor localization tasks based on fingerprint methods. However, it is often challenging for popular machine learning models to effectively capture the unstructured data features inherent in fingerprint data that are generated in diverse propagation environments. In this paper, we propose an indoor localization algorithm based on a high-order graph neural network (HoGNNLoc) to enhance the accuracy of indoor localization and improve localization stability in dynamic environments. The algorithm first designs an adjacency matrix based on the spatial relative locations of access points (APs) to obtain a graph structure; on this basis, a high-order graph neural network is constructed to extract and aggregate the features; finally, the designed fully connected network is used to achieve the regression prediction of the location of the target to be located. The experimental results on our self-built dataset show that the proposed algorithm achieves localization accuracy within 1.29 m at 80% of the cumulative distribution function (CDF) points. The improvements are 59.2%, 51.3%, 36.1%, and 22.7% compared to the K-nearest neighbors (KNN), deep neural network (DNN), simple graph convolutional network (SGC), and graph attention network (GAT). Moreover, even with a 30% reduction in fingerprint data, the proposed algorithm exhibits stable localization performance. On a public dataset, our proposed localization algorithm can also show better performance.
... In a larger operational area with an uneven distribution of reference locations and variable density of fingerprints, a dynamic approach would fit better [54], [55]. Nevertheless, we evaluated other alternatives including Weighted k-Nearest Neighbors (wkNN), Adaptive Weighted k-Nearest Neighbors (awkNN) [54], Self-Adaptive Weighted k-Nearest Neighbors (sawkNN) [55], Signal Tendency Index -Weighted k-Nearest Neighbors (sti-kNN) [56] and Distance & Feature Weighted k-Nearest Neighbors (dwfwkNN) [57] on the measured RM, producing differences of the mean positioning error around 1 cm to 2 cm in the best cases. ...
Article
Indoor positioning and navigation increasingly have become popular, and there are many different approaches, using different technologies. In nearly all of the approaches, the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation radio map (RM) by analyzing the environment. As this is usually done on a regular grid, the collection of received signal strength indicator (RSSI) data at every reference point (RP) of an RM is a time-consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful, as it allows researchers to spend more time with research instead of data collection. In this article, we analyze the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using five ESP32 microcontrollers working in monitoring mode and placed around our office. We then analyze the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian process regression (GPR) to find balance among final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.
... Furthermore, static fingerprint database may be unreliable, which requires repeated data collection to maintain a satisfactory positioning accuracy. The works of [22]- [24] have conceived different methods to solve the above-mentioned problems. For example, the authors in [22] estimate the RSS at non-site-surveyed positions and utilize the support vector regression (SVR) to improve the resolution of the radio map. ...
Preprint
Full-text available
Wireless indoor localization has attracted significant amount of attention in recent years. Using received signal strength (RSS) obtained from WiFi access points (APs) for establishing fingerprinting database is a widely utilized method in indoor localization. However, the time-variant problem for indoor positioning systems is not well-investigated in existing literature. Compared to conventional static fingerprinting, the dynamicallyreconstructed database can adapt to a highly-changing environment, which achieves sustainability of localization accuracy. To deal with the time-varying issue, we propose a skeleton-assisted learning-based clustering localization (SALC) system, including RSS-oriented map-assisted clustering (ROMAC), cluster-based online database establishment (CODE), and cluster-scaled location estimation (CsLE). The SALC scheme jointly considers similarities from the skeleton-based shortest path (SSP) and the time-varying RSS measurements across the reference points (RPs). ROMAC clusters RPs into different feature sets and therefore selects suitable monitor points (MPs) for enhancing location estimation. Moreover, the CODE algorithm aims for establishing adaptive fingerprint database to alleviate the timevarying problem. Finally, CsLE is adopted to acquire the target position by leveraging the benefits of clustering information and estimated signal variations in order to rescale the weights fromweighted k-nearest neighbors (WkNN) method. Both simulation and experimental results demonstrate that the proposed SALC system can effectively reconstruct the fingerprint database with an enhanced location estimation accuracy, which outperforms the other existing schemes in the open literature.
... To achieve precise indoor localization, various technologies have been proposed, such as WiFi [4], ultrasonic [5], radio frequency identification (RFID) [6], Zigbee [7], Bluetooth [8], ultra-wideband [9], and infrared [10], aiming to overcome the limitations of traditional wireless localization techniques, including drawbacks such as high electromagnetic radiation interference, high deployment costs, and low positioning accuracy. WiFi, in particular, has gained significant attention due to its reliability, extensive coverage, and ability to fill the gaps left by satellitebased positioning systems. ...
Preprint
Given the rapid advancements in wireless communication and terminal devices, high-speed and convenient WiFi has permeated various aspects of people's lives, and attention has been drawn to the location services that WiFi can provide. Fingerprint-based methods, as an excellent approach for localization, have gradually become a hot research topic. However, in practical localization, fingerprint features of traditional methods suffer from low reliability and lacking robustness in complex indoor environments. To overcome these limitations, this paper proposes a innovative feature extraction-enhanced intelligent localization scheme named Secci, based on diversified channel state information (CSI). By modifying the device driver, diversified CSI data are extracted and transformed into RGB CSI images, which serve as input to a deep convolutional neural network (DCNN) with SE attention mechanism-assisted training in the offline stage. Employing a greedy probabilistic approach, rapid prediction of the estimated location is performed in the online stage using test RGB CSI images. The Secci system is implemented using off-the-shelf WiFi devices, and comprehensive experiments are carried out in two representative indoor environments to showcase the superior performance of Secci compared to four existing algorithms.
... As a result, the accuracy of the ALS could be significantly improved through the development of more precise trajectory data collection methods and optimization of the sensor technology in mobile devices. The existing problems include complex human motion modes [16] and a lack of an efficient combination of built-in sensor-based location sources and an existing indoor map or pedestrian network information [17], which are the main factors that affect the accuracy of ALS. ...
Article
Full-text available
Autonomous localization without local wireless facilities is proven as an efficient way for realizing location-based services in complex urban environments. The precision of the current map-matching algorithms is subject to the poor ability of integrated sensor-based trajectory estimation and the efficient combination of pedestrian motion information and the pedestrian indoor network. This paper proposes an autonomous multi-floor localization framework based on smartphone-integrated sensors and pedestrian network matching (ML-ISNM). A robust data and model dual-driven pedestrian trajectory estimator is proposed for accurate integrated sensor-based positioning under different handheld modes and disturbed environments. A bi-directional long short-term memory (Bi-LSTM) network is further applied for floor identification using extracted environmental features and pedestrian motion features, and further combined with the indoor network matching algorithm for acquiring accurate location and floor observations. In the multi-source fusion procedure, an error ellipse-enhanced unscented Kalman filter is developed for the intelligent combination of a trajectory estimator, human motion constraints, and the extracted pedestrian network. Comprehensive experiments indicate that the presented ML-ISNM achieves autonomous and accurate multi-floor positioning performance in complex and large-scale urban buildings. The final evaluated average localization error was lower than 1.13 m without the assistance of wireless facilities or a navigation database.
... Indoor Localization Systems: Traditional localization approaches that leverage on external setup, are affected by environmental uncertainties and lack robustness in different dynamic scenarios. For example, (a) WiFi-based indoor positioning technologies require mechanisms such as triangulation [34], accurate fingerprinting of the environment and multiple routers (to achieve high accuracy) [16,35,42], inter/extrapolation [30], or crowdsourcing-based technologies [33]. However, these approaches have its own limitations (based on a survey by Jang et al. [10]) especially in certain indoor environments (e.g., harsh radio environments in factories/warehouses, environments such as malls or performance arenas where the spatial layout changes varies frequently), where a LiLoc-like localization approach could prove to be more robust, (b) UWB localization [23] requires pre-installation and calibration of multiple anchors, (c) visible light-based localization [17,39] can work on existing infrastructure but requires complex programming and controlling of the individual bulbs in a wireless manner, (d) RFbased approaches (e.g., Zigbee [9], BLE [26], RFID [13]) are affected by environmental artefacts (i.e., weather change, dynamicity of the environment), and (e) pure vision-based systems [22] have significant privacy concerns, are affected by illumination change and often have poor accuracy in multi-occupant settings. ...
... Zou et al. [96] improved the performance of Wi-Fi fingerprinting by using autonomously constructed navigation database and adaptation model, and proposed a novel Gaussian process regression model, which effectively increased the accuracy of RSSI estimation and final localization. Wu et al. [98] comprehensively investigated the aspects which affect the accuracy of Wi-Fi fingerprinting, for example the RSSI continuity and pedestrian body blockages, and integrated these parameters by a unified model which covers both on-line and offline phases. ...
Thesis
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Location-based services (LBS) have become more and more important with the development of Internet of Things (IoT) technology and increasing popularity of IoT terminals in recent years. Global Navigation Satellite System (GNSS) is widely used for positioning outdoors while it is still challenging to realize autonomous, precise and universal indoor localization based on the existing devices. Among most indoor positioning technologies, the Wireless Fidelity (Wi-Fi) based positioning is regarded as an effective way for realizing ubiquitous and high-precision indoor navigation, especially the presentation of next generation Wi-Fi access point which supports the state-of-art Wi-Fi Fine Time Measurement (FTM) protocol. Micro-Electro-Mechanical System (MEMS) sensors can provide an accurate short-term navigation solution, which also provides a potential way for autonomously generating the crowdsourced Wi-Fi received signal strength indication (RSSI) based fingerprinting database, by collecting and mining the users’ daily-life trajectories and corresponding signals of opportunity. This thesis proposes an automatic and precision-controllable algorithm for multisource fusion based wireless positioning using the combination of Wi-Fi FTM, crowdsourced Wi-Fi RSSI fingerprinting, and IoT terminals integrated MEMS sensors, by which the realized ubiquitous positioning accuracy can reach 1.5~4.5m (within 75th percentile), and meter-level accuracy can be achieved under Wi-Fi FTM covered indoor scenes.
... Additionally, the Signal Trend Index (STI) has been used in some studies to compare the vector shape of the RSSI of the mobile terminal device during the online phase with that of the offline database. The Signal Trend Exponential Weighted K-Nearest Neighbor (STI-WKNN) algorithm [32] has been proposed as a solution with better performance targeting heterogeneous devices. ...
Article
Full-text available
The Weighted K Nearest Neighbor (WKNN) algorithm is a widely adopted lightweight methodology for indoor WiFi positioning based on location fingerprinting. Nonetheless, it suffers from the disadvantage of a fixed K value and susceptibility to incorrect reference point matching. To address this issue, we present a novel algorithm in this paper, referred to as Static Continuous Statistical Characteristics-Soft Range Limited-Self-Adaptive WKNN (SCSC-SRL-SAWKNN). Our algorithm not only takes into account location tracking in the motion state but also exploits the continuous statistical features of extended periods of inactivity to enhance localization. In the motion state, we initially employ the adaptive WKNN (SAWKNN) algorithm to determine the optimal K value, followed by the employment of the Soft Range Limited KNN (SR-KNN) algorithm to identify the correct reference point and ultimately estimate the position. When a prolonged stationary state is detected, we first utilize the moving window method to obtain a more stable position fingerprint (SCSC), and then proceed with the positioning process in the same motion state. Ultimately, we use Kalman filter to generate the location trajectory. Our experimental findings demonstrate that the proposed SCSC-SRL-SAWKNN algorithm outperforms traditional WKNN, SAWKNN, and SRL-KNN techniques in terms of localization accuracy and location trajectory. Specifically, the localization accuracy of our algorithm is 56.7% and 36.6% higher than that of traditional WKNN in the static state and overall situation, respectively.
... specially in the large-scale indoor environment. Some complex algorithms and their variants have also been explored to further enhance positioning performance, such as support vector machine (SVM), Gaussian process, random forest, artificial neural network (ANN), and extreme learning machine (L. Li et al., 2019;Tao, Zhao, Shen, Chen, & Zhang, 2020;H. Zou, Jin, Jiang, Xie, & Spanos, 2017). Of course, hot deep learning algorithms are also introduced to extract and learn complex features to obtain more accurate positioning results. Oh (Oh & Kim, 2021) explored the deep neural network (DNN) model achieving the mean absolute error (MAE) of 3.6-3.8 m in an indoor area of 6000 square meters. Khassanov (Khassanov, Nurpeiissov, ...
Article
Wireless fidelity (WiFi) indoor positioning has attracted the attention of thousands of researchers. It faces many challenges, and the primary problem is the low positioning accuracy, which hinders its widespread applications. To improve the accuracy, we propose a WiFi indoor positioning algorithm based on support vector regression (SVR) optimized by particle swarm optimization (PSO), termed PSOSVRPos. SVR algorithm devotes itself to solving localization as a regression problem by building the mapping between signal features and spatial coordinates in high dimensional space. PSO algorithm concentrates on the global-optimal parameter estimation of the SVR model. The positioning experiment is conducted on an open dataset (1511 samples, 154 features). The PSOSVRPos algorithm could achieve positioning accuracy with a mean absolute error of 1.040 meters, a root mean square error (RMSE) of 0.863 meters and errors within 1 meter of 59.8%. Experimental results indicate that the PSOSVRPos algorithm is a precise approach for WiFi indoor positioning as it reduces the RMSE (35%) and errors within 1 m (14%) compared with state-of-the-art algorithms such as convolutional neural network (CNN) based methods.
... To summarize, each of the aforementioned factors causes a change in RSS measurement at a given reference location under the same experimental conditions. Other research works propose techniques to deal with RSS measurement regardless of the factor contributing to the RSS fluctuation [29,48,[54][55][56][57][58][59][60][61][62], in addition to the solutions proposed by previous research works on dealing with each of the factors mentioned above. A summary of these techniques can be seen in Table 5. ...
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Chapter
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Chapter
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A cognitive approach is proposed to detect unknown beacons of terrestrial signals of opportunity (SOPs). Two scenarios are considered in the paper: (i) detection of unknown beacons with integer constraints (IC) and (ii) detection of unknown beacons with no integer constraint (NIC). An example of beacons with IC is the pseudo-noise (PN) sequences in cellular code division multiple access (CDMA) signals. On the other hand, the reference signals (RSs) in orthogonal frequency-division multiplexing (OFDM)-based systems can be considered as beacons signals with NIC. Matched subspace detectors are proposed for both scenarios, and it is shown experimentally that the proposed matched subspace detectors are capable of detecting cellular third-generation (3G) cdma2000 signals and fifth-generation (5G) OFDM signals. A low complexity method is derived to simplify the matched subspace detector with IC for M -ary phase shift keying ( M PSK) modulation. The effect of symbol errors in the estimated beacon signal on the carrier to noise ratio (CNR) is characterized analytically. Closed-form expressions for the asymptotic probability of detection and false alarm are derived. Experimental results are presented showing an application of the proposed cognitive approach by enabling an unmanned aerial vehicle (UAV) to detect and exploit terrestrial cellular signals for navigation purposes. In one experiment, the UAV achieved submeter-level accurate navigation over a trajectory of 1.72 km, by exploiting signals from four 3G cdma2000 transmitters. In another experiment, the UAV achieves a position root mean-squared error (RMSE) of 4.63 m over a trajectory of 416 m, by exploiting signals from two 5G transmitters.
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WiFi fingerprint-based localization has been intensive studies as a promising technology of ubiquitous location-based services. Two main concerns for its wide spread applications are to tackle with the cumbersome efforts of site survey and to combat vulnerable environment changes. To address these issues comprehensively, we propose a novel approach on adaptive fingerprint-based localization with less site survey, named as LESS, by exploring a new paradigm of radio map construction and adaptation with few-shot relation learning. Firstly, we extend sparsely collected fingerprints with the fingerprint augmentation method which produces new related data and derives their location information based on local proximity property in a low-dimensional manifold space. Then, LESS designs deep relation networks to learn not only the appropriate features but also a transferable deep-distance metric for modeling the fundamental relationships of the neighborhood fingerprints. Finally, once trained, LESS can quickly establish the neighborhood relationships among new fingerprints in the changed surroundings to realize adaptive location estimations, even without the network updating. The extensive experimental results demonstrate that LESS can achieve an attractive trade-off between the system overhead and the location performance with the superiorities over others in dynamic indoor environments.
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Radio map is of great importance to interference control, network planning and resource allocation in wireless communications. In this paper, we develop an accurate radio map reconstruction method based on k-nearest neighbors Gaussian process regression, which can exploit the relationship of the received signal strengths at adjacent locations efficiently. Numerical experiments demonstrate that our proposed method outperforms the state-of-the-art ones significantly.
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In this paper, Gaussian process regression (GPR) for fingerprinting based localization is presented. In contrast to general regression techniques, the GPR not only infers the posterior received signal strength (RSS) mean but also the variance at each fingerprint location. The GPR does take into account the variance of input i.e., noisy RSS data. The hyper-parameters of GPR are estimated using trust-region-reflective algorithm. The Cramér-Rao bound is analysed to highlight the performance of the parameter estimator. The posterior mean and variance of RSS data is utilized in fingerprinting based localization. The principal component analysis is employed to choose the k strongest wi-fi access points (APs). The performance of the proposed algorithm is validated using using real field deployments. Accuracy improvements of 10% and 30% are observed in two sites compared to the Horus fingerprinting approach.
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Identifying different floors in multi-story buildings is a very important task for precise indoor localization in industrial and commercial applications. The accuracy from existing studies is rather low especially in multi-story buildings with irregular structures, such as hollow areas, which is common in various industrial and commercial sites. As a better solution, this paper proposes a hybrid floor identification algorithm (HYFI) which exploits wireless access point (AP) distribution and barometric pressure information. It first extracts the distribution probability of APs scanned in different floors from offline training fingerprints and adopts Bayesian classification to accurately identify floor in well-partitioned zones without hollow areas. The floor information obtained from wireless AP distribution is then used to initialize and calibrate barometric pressure-based floor identification to compensate variable environmental effects. Extensive experiments confirms the HYFI approach significantly outperforms purely wireless fingerprinting-based or purely barometric pressure-based floor identification approaches. In our field tests in multi-story facilities with irregular hollow areas, it can identify the floor level with more than 96.1% accuracy.
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Indoor Positioning System (IPS) has become one of the most attractive research fields due to the increasing demands on Location Based Services (LBSs) in indoor environments. Various IPSs have been developed under different circumstances, and most of them adopt the fingerprinting technique to mitigate pervasive indoor multipath effects. However, the performance of the fingerprinting technique severely suffers from device heterogeneity existing across commercial off-the-shelf mobile devices (e.g. smart phones, tablet computers, etc.) and indoor environmental changes (e.g. the number, distribution and activities of people, the placement of furniture, etc.). In this paper, we transform the Received Signal Strength (RSS) to a standardized location fingerprint based on the Procrustes analysis, and introduce a similarity metric, termed Signal Tendency Index (STI), for matching standardized fingerprints. An analysis on the capability of the proposed STI in handling device heterogeneity and environmental changes is presented. We further develop a robust and precise IPS by integrating the merits of both the STI and Weighted Extreme Learning Machine (WELM). Finally, extensive experiments are carried out and a performance comparison with existing solutions verifies the superiority of the proposed IPS in terms of robustness to device heterogeneity.
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The main approach for a Wi-Fi indoor positioning system is based on the received signal strength (RSS) measurements, and the fingerprinting method is utilized to determine the user position by matching the RSS values with the pre-surveyed RSS database. To build a RSS fingerprint database is essential for an RSS based indoor positioning system, and building such a RSS fingerprint database requires lots of time and effort. As the range of the indoor environment becomes larger, labor is increased. To provide better indoor positioning services and to reduce the labor required for the establishment of the positioning system at the same time, an indoor positioning system with an appropriate spatial interpolation method is needed. In addition, the advantage of the RSS approach is that the signal strength decays as the transmission distance increases, and this signal propagation characteristic is applied to an interpolated database with the Kriging algorithm in this paper. Using the distribution of reference points (RPs) at measured points, the signal propagation model of the Wi-Fi access point (AP) in the building can be built and expressed as a function. The function, as the spatial structure of the environment, can create the RSS database quickly in different indoor environments. Thus, in this paper, a Wi-Fi indoor positioning system based on the Kriging fingerprinting method is developed. As shown in the experiment results, with a 72.2% probability, the error of the extended RSS database with Kriging is less than 3 dBm compared to the surveyed RSS database. Importantly, the positioning error of the developed Wi-Fi indoor positioning system with Kriging is reduced by 17.9% in average than that without Kriging.
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The need for accurate, fast, and reliable indoor localization using wireless sensor networks (WSNs) has recently grown in diverse areas of industry. Accurate localization in cluttered and noisy environments is commonly provided by means of a mathematical algorithm referred to as a state estimator or filter. The particle filter (PF), which is the most commonly used filter in localization, suffers from the sample impoverishment problem under typical conditions of real-time localization based on WSNs. This paper proposes a novel hybrid particle/FIR filtering algorithm for improving reliability of PF-based localization schemes under harsh conditions causing sample impoverishment. The hybrid particle/FIR filter detects the PF failures and recovers the failed PF by resetting the PF using the output of an auxiliary finite impulse response (FIR) filter. Combining the regularized particle filter (RPF) and the extended unbiased FIR (EFIR) filter, the hybrid RP/EFIR filter is constructed in this paper. Through simulations, the hybrid RP/EFIR filter demonstrates its improved reliability and ability to recover the RPF from failures.
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Fingerprints-based methods have been prevailing in indoor positioning systems, whereas they have certain drawbacks that fingerprints collection in the offline phase requires considerable manpower and time. Auto Planner for Efficient Configuration (APEC) systematically exploits router setups and fingerprints allocations over space by taking into account user preferences and budget constraints. The task of configuration is formulated as an optimization problem, whose objective is the expected loss based on the Hierarchical Bayesian Signal Model (HBSM) and theoretical results on the misclassification rates. To reduce the computational complexity of large-scale problems, two heuristics are employed, i.e., \emph{the coordinate descent} and the \emph{router-fingerprints decoupling}, which are validated by simulation analysis. Experiments with three mobile devices (Android, iPad, iPhone) in two setups (7 or 9 access points) verify that the expected loss is a reliable predictor of the actual loss of the system (\emph{objective consistency}), and that APEC outperforms the random and uniform approaches (\emph{solution superiority}). Since APEC focuses on the system configuration in the planning stage, it can be combined with other fingerprinting processes in the online phase to improve the utility of the system.
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With the rapid development of WIFI technology, WIFI-based indoor positioning technology has been widely studied for location-based services. To solve the problems related to the signal strength database adopted in the widely used fingerprint positioning technology, we first introduce a new system framework in this paper, which includes a modified AP firmware and some cheap self-made WIFI sensor anchors. The periodically scanned reports regarding the neighboring APs and sensor anchors are sent to the positioning server and serve as the calibration points. Besides the calculation of correlations between the target points and the neighboring calibration points, we take full advantage of the important but easily overlooked feature that the signal attenuation model varies in different regions in the regression algorithm to get more accurate results. Thus, a novel method called RSSI Geography Weighted Regression (RGWR) is proposed to solve the fingerprint database construction problem. The average error of all the calibration points’ self-localization results will help to make the final decision of whether the database is the latest or has to be updated automatically. The effects of anchors on system performance are further researched to conclude that the anchors should be deployed at the locations that stand for the features of RSSI distributions. The proposed system is convenient for the establishment of practical positioning system and extensive experiments have been performed to validate that the proposed method is robust and manpower efficient.
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We present the results, experiences and lessons learned from comparing a diverse set of technical approaches to indoor localization during the 2014 Microsoft Indoor Localization Competition. 22 different solutions to indoor localization from different teams around the world were put to test in the same unfamiliar space over the course of 2 days, allowing us to directly compare the accuracy and overhead of various technologies. In this paper, we provide a detailed analysis of the evaluation study's results, discuss the current state-of-the-art in indoor localization, and highlight the areas that, based on our experience from organizing this event, need to be improved to enable the adoption of indoor location services.
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Nowadays, developing indoor positioning systems (IPSs) has become an attractive research topic due to the increasing demands on location-based service (LBS) in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM) to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics.
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Growing convergence among mobile computing devices and embedded technology sparks the development and deployment of “context-aware” applications, where location is the most essential context. In this paper we present LANDMARC, a location sensing prototype system that uses Radio Frequency Identification (RFID) technology for locating objects inside buildings. The major advantage of LANDMARC is that it improves the overall accuracy of locating objects by utilizing the concept of reference tags. Based on experimental analysis, we demonstrate that active RFID is a viable and cost-effective candidate for indoor location sensing. Although RFID is not designed for indoor location sensing, we point out three major features that should be added to make RFID technologies competitive in this new and growing market.
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Fingerprint-based methods are widely adopted for indoor localization purpose because of their cost-effectiveness compared to other infrastructure-based positioning systems. However, the popular location fingerprint, Received Signal Strength (RSS), is observed to differ significantly across different devices' hardware even under the same wireless conditions. We derive analytically a robust location fingerprint definition, the Signal Strength Difference (SSD), and verify its performance experimentally using a number of different mobile devices with heterogeneous hardware. Our experiments have also considered both Wi-Fi and Bluetooth devices, as well as both Access-Point(AP)-based localization and Mobile-Node (MN)-assisted localization. We present the results of two well-known localization algorithms (K Nearest Neighbor and Bayesian Inference) when our proposed fingerprint is used, and demonstrate its robustness when the testing device differs from the training device. We also compare these SSD-based localization algorithms' performance against that of two other approaches in the literature that are designed to mitigate the effects of mobile node hardware variations, and show that SSD-based algorithms have better accuracy.
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One major bottleneck in the practical implementation of received signal strength (RSS) based indoor localization systems is the extensive deployment efforts required to construct the radio maps through fingerprinting. In this paper, we aim to design an indoor localization scheme that can be directly employed without building a full fingerprinted radio map of the indoor environment. By accumulating the information of localized RSSs, this scheme can also simultaneously construct the radio map with limited calibration. To design this scheme, we employ a source data set that possesses the same spatial correlation of the RSSs in the indoor environment under study. The knowledge of this data set is then transferred to a limited number of calibration fingerprints and one or several RSS observations with unknown locations, in order to perform direct localization of these observations using manifold alignment. We test two different source data sets, namely a simulated radio propagation map and the environments plan coordinates. For moving users, we exploit the correlation of their observations to improve the localization accuracy. The online testing in two indoor environments shows that the plan coordinates achieve better results than the simulated radio maps, and a negligible degradation with 70-85% reduction in calibration load.
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Many indoor localization methods are based on the association of 802.11 wireless RF signals from wireless access points (WAPs) with location labels. An "organic" RF positioning system relies on regular users, not dedicated surveyors, to build the map of RF fingerprints to location labels. However, signal variation due to device heterogeneity may degrade localization performance. We analyze the diversity of those signal characteristics perti- nent to indoor localization — signal strength and AP detection — as measured by a variety of 802.11 devices. We first analyze signal strength diversity, and show that pairwise linear trans- formation alone does not solve the problem. We propose kernel estimation with a wide kernel width to reduce the difference in probability estimates. We also investigate diversity in access point detection. We demonstrate that localization performance may degrade significantly when AP detection rate is used as a feature for localization, and correlate the loss of performance to a device dissimilarity measure captured by Kullback-Leibler divergence. Based on this analysis, we show that using only signal strength, without incorporating negative evidence, achieves good localization performance when devices are heterogeneous.
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We estimate the location of a WLAN user based on radio signal strength measurements performed by the user's mobile terminal. In our approach the physical properties of the signal propagation are not taken into account directly. Instead the location estimation is regarded as a machine learning problem in which the task is to model how the signal strengths are distributed in different geographical areas based on a sample of measurements collected at several known locations. We present a probabilistic framework for solving the location estimation problem. In the empirical part of the paper we demonstrate the feasibility of this approach by reporting results of field tests in which a probabilistic location estimation method is validated in a real-world indoor environment.
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The time-of-arrival (TOA), time-difference-of-arrival (TDOA) and signal strength (SS) methods have been widely accepted as three principal techniques for positioning a mobile station (MS) in a wireless communication system. To the best of our knowledge, previous studies tend to treat these methods separately, and less analytical results on their relationship have been reported. We first examine the link between the TOA and TDOA methods. We provide an analytical explanation for the claim that, given a set of BS locations and an MS position, the TOA method should achieve a higher positioning precision than its TDOA counterpart. However, the two positioning methods may attain the same level of accuracy under certain conditions. We then investigate the tradeoff between the accuracy limits of the TOA and SS based methods, which leads to our proposal of a hybrid distance estimation scheme that combines both TOA and SS data.
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Context-awareness and Location-Based-Services are of great importance in mobile computing environments. Although fingerprinting provides accurate indoor positioning in Wireless Local Area Networks (WLAN), difficulty of offline site surveys and the dynamic environment changes prevent it from being practically implemented and commercially adopted. This paper introduces a novel client/server-based system that dynamically estimates and continuously calibrates a fine radio map for indoor positioning without extra network hardware or prior knowledge about the area and without time-consuming offline surveys. A modified Bayesian regression algorithm is introduced to estimate a posterior signal strength probability distribution over all locations based on online observations from WLAN access points (AP) assuming Gaussian prior centered over a logarithmic pass loss mean. To continuously adapt to dynamic changes, Bayesian kernels parameters are continuously updated and optimized genetically based on recent APs observations. The radio map is further optimized by a fast features reduction algorithm to select the most informative APs. Additionally, the system provides reliable integrity monitor (accuracy measure). Two different experiments on IEEE 802.11 networks show that the dynamic radio map provides 2-3m accuracy, which is comparable to results of an up-to-date offline radio map. Also results show the consistency of estimated accuracy measure with actual positioning accuracy.
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Radio Frequency (RF) fingerprinting, based onWiFi or cellular signals, has been a popular approach to indoor localization. However, its adoption in the real world has been stymied by the need for sitespecific calibration, i.e., the creation of a training data set comprising WiFi measurements at known locations in the space of interest. While efforts have been made to reduce this calibration effort using modeling, the need for measurements from known locations still remains a bottleneck. In this paper, we present Zee -- a system that makes the calibration zero-effort, by enabling training data to be crowdsourced without any explicit effort on the part of users. Zee leverages the inertial sensors (e.g., accelerometer, compass, gyroscope) present in the mobile devices such as smartphones carried by users, to track them as they traverse an indoor environment, while simultaneously performing WiFi scans. Zee is designed to run in the background on a device without requiring any explicit user participation. The only site-specific input that Zee depends on is a map showing the pathways (e.g., hallways) and barriers (e.g., walls). A significant challenge that Zee surmounts is to track users without any a priori, user-specific knowledge such as the user's initial location, stride-length, or phone placement. Zee employs a suite of novel techniques to infer location over time: (a) placement-independent step counting and orientation estimation, (b) augmented particle filtering to simultaneously estimate location and user-specific walk characteristics such as the stride length,(c) back propagation to go back and improve the accuracy of ocalization in the past, and (d) WiFi-based particle initialization to enable faster convergence. We present an evaluation of Zee in a large office building.
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An indoor positioning system that uses a location fingerprinting technique based on the received signal strength of a wireless local area network is an enabler for indoor location-aware computing. Data analysis of the received signal strength indication is very essential for understanding the underlying location-dependent features and patterns of location fingerprints. This knowledge can assist a system designer in accurately modeling a positioning system, improving positioning performance, and efficiently designing such a system. This study investigates extensively through measurements, the features of the received signal strength indication reported by IEEE 802.11b/g wireless network interface cards. The results of the statistical data analysis help in identifying a number of phenomena that affect the precision and accuracy of indoor positioning systems.
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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.
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Precise localization has attracted considerable traction in the area of cooperative assignments for robots in indoor applications. When dealing with indoor applications we are limited to the type of signals that can be used for precise localization which is our prime goal. There are limitations to the well known GPS and also vision-based modality where the non-line-of-sight (NLOS) conditions can significantly degrade the results. Hence there is a need for alternative approaches for more precise indoor localization. Precise localization information is an enabler for better coordinated tasks where multiple robots are at play. In this paper, the hybrid cooperative localization accuracy for a multi-robot operation is examined. We use a mix of empirical and theoretical models for ranging estimates in a typical indoor environment on the third floor of the Atwater Kent Laboratory (AKL) at Worcester Polytechnic Institute. The two widely used ranging techniques are Time Of Arrival (TOA) using Ultra-wideband (UWB) and Received Signal Strength (RSS) using WiFi signals. The Cramér-Rao-Lower-Bound (CRLB) on the performance of hybrid localization techniques are determined in our multi-robot operation scenarios based on empirical data for UWB TOA-based and the theoretical WiFi RSS-based ranging. The performance of the hybrid localization of robots are examined when the robots are equipped with UWB radios and operate in cooperative mode using known WiFi Anchors.
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Accurately locating users in a wireless environment is an im- portant task for many pervasive computing and AI applica- tions, such as activity recognition. In a WiFi environment, a mobile device can be localized using signals received from various transmitters, such as access points (APs). Most lo- calization approaches build a map between the signal space and the physical location space in a offline phase, and then using the received-signal-strength (RSS) map to estimate the location in an online phase. However, the map can be out- dated when the signal-strength values change with time due to environmental dynamics. It is infeasible or expensive to repeat data calibration for reconstructing the RSS map. In such a case, it is important to adapt the model learnt in one time period to another time period without too much re- calibration. In this paper, we present a location-estimation approach based on Manifold co-Regularization, which is a machine learning technique for building a mapping function between data. We describe LeManCoR, a system for adapting the mapping function between the signal space and physical location space over different time periods based on Manifold Co-Regularization. We show that LeManCoR can effectively transfer the knowledge between two time periods without re- quiring too much new calibration effort.We illustrate LeMan- CoR's effectiveness in a real 802.11 WiFi environment.
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Available techniques for indoor object locating sys- tems, such as inertial sensor-based system or radio fingerprinting, hardly satisfy both cost-effectiveness and accuracy. In particular, inertial sensor-based locating systems are often supplemented with radio signals to improve localization accuracy. A radio-as- sisted localization system is still costly due to the infrastructure requirements and management overheads. In this paper, we propose a low-cost and yet accurate indoor pedestrian localization scheme with a small number of radio beacons whose location information is unknown. Our scheme applies the Simultaneous Location and Mapping (SLAM) technique used in robotics to mobile device, which is equipped with both inertial sensors and the IEEE802.15.4a Chirp Spread Spectrum (CSS) radio, to ob- tain accurate locations of pedestrians in indoor environment. The proposed system is validated with real implementations. The experiment results show approximately 1.5 m mean error observed during 276 m of pedestrian moving in a 380 indoor environment with five position-unknown beacons.
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With the technical advances in ubiquitous computing and wireless networking, there has been an increasing need to capture the context information (such as the location) and to figure it into applications. In this paper, we establish the theoretical base and develop a localization algorithm for building a zero-configuration and robust indoor localization and tracking system to support location-based network services and management. The localization algorithm takes as input the on-line measurements of received signal strengths (RSSs) between 802.11 APs and between a client and its neighboring APs, and estimates the location of the client. The on-line RSS measurements among 802.11 APs are used to capture (in real-time) the effects of RF multi-path fading, temperature and humidity variations, opening and closing of doors, furniture relocation, and human mobility on the RSS measurements, and to create, based on the truncated singular value decomposition (SVD) technique, a mapping between the RSS measure and the actual geographical distance. The proposed system requires zero-configuration because the on-line calibration of the effect of wireless physical characteristics on RSS measurement is automated and no on-site survey or initial training is required to bootstrap the system. It is also quite responsive to environmental dynamics, as the impacts of physical characteristics changes have been explicitly figured in the mapping between the RSS measures and the actual geographical distances. We have implemented the proposed system with inexpensive off-the-shelf Wi-Fi hardware and sensory functions of IEEE 802.11, and carried out a detailed empirical study in our departmental building, Siebel Center for Computer Science. The empirical results show the proposed system is quite robust and gives accurate localization results.
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Australian and New Zealand environmental economists have played a significant role in the development of concepts and their application across three fields within their subdiscipline: non-market valuation, institutional economics and bioeconomic modelling. These contributions have been spurred on by debates within and outside the discipline. Much of the controversy has centred on the validity of valuations generated through the application of stated preference methods such as contingent valuation. Suggestions to overcome some shortcomings in the work of environmental economists include the commissioning of a sequence of non-market valuation studies to fill existing gaps to improve the potential for benefit transfer. Copyright 2005 Australian Agricultural and Resource Economics Society Inc. and Blackwell Publishing Asia Pty Ltd..
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We present LEASE, a new system and framework for location estimation assisted by stationary emitters for indoor RF wireless networks. Unlike previous studies, we emphasize the deployment aspect of location estimation engines. Motivated thus, we present an adaptable infrastructure-based system that uses a small number of stationary emitters (SEs) and sniffers employed in a novel way to locate standard wireless clients in an enterprise. We present the components of the system and its architecture, and new non-parametric techniques for location estimation that work with a small number of SEs. Our techniques for location estimation can also be used in a client-based deployment. We present experimental results of using our techniques at two sites demonstrating the ability to perform location estimation with good accuracy in our new adaptable framework.
Conference Paper
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
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In wireless networks, a client's locations can be estimated using the signals received from various signal transmitters. Static fingerprint-based techniques are commonly used for location estimation, in which a radio map is built by calibrating signal-strength values in the offline phase. These values, compiled into deterministic or probabilistic models, are used for online localization. However, the radio map can be outdated when the signal-strength values change with time due to environmental dynamics, and repeated data calibration is infeasible or expensive. In this paper, we present a novel algorithm, known as LEMT (Location Estimation using Model Trees), to reconstruct a radio map using real-time signal- strength readings received at the reference points. This algorithm can take into account real-time signal-strength values at each time point and make use of the dependency between the estimated locations and reference points. We show that this technique can effectively accommodate the variations of signal strength over different time periods without the need to rebuild the radio maps repeatedly. We demonstrate the effectiveness of our proposed technique on realistic data sets collected from an 802.11b wireless network and a RFID-based network.
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WLAN location estimation based on 802.11 signal strength is becoming increasingly prevalent in today's pervasive computing applications. Among the well-established location determination approaches, probabilistic techniques show good performance and, thus, become increasingly popular. For these techniques to achieve a high level of accuracy, however, a large number of training samples are usually required for calibration, which incurs a great amount of offline manual effort. In this paper, we aim to solve the problem by reducing both the sampling time and the number of locations sampled in constructing a radio map. We propose a novel learning algorithm that builds location-estimation systems based on a small fraction of the calibration data that traditional techniques require and a collection of user traces that can be cheaply obtained. When the number of sampled locations is reduced, an interpolation method is developed to effectively patch a radio map. Extensive experiments show that our proposed methods are effective in reducing the calibration effort. In particular, unlabeled user traces can be used to compensate for the effects of reducing the calibration effort and can even improve the system performance. Consequently, manual effort can be reduced substantially while a high level of accuracy is still achieved
A system for lease: Location estimation assisted by stationary emitters for indoor RF wireless networks
  • P Krishnan
  • A Krishnakumar
  • W.-H Ju
  • C Mallows
  • S Gamt
P. Krishnan, A. Krishnakumar, W.-H. Ju, C. Mallows, and S. Gamt, "A system for lease: Location estimation assisted by stationary emitters for indoor RF wireless networks," in Proc. IEEE Annu. Joint Conf. Comput. Commun. Soc., vol. 2. Mar. 2004, pp. 1001-1011.
Adaptive localization in a dynamic wifi environment through multi-view learning
  • S J Pan
  • J T Kwok
  • Q Yang
  • J J Pan
S. J. Pan, J. T. Kwok, Q. Yang, and J. J. Pan, "Adaptive localization in a dynamic wifi environment through multi-view learning," in Proc. AAAI Conf. Artif. Intell., 2007, pp. 1108-1113.
OpenWrt: A Linux Distribution for Embedded Devices
  • Openwrt
Cramer-Rao bound analysis of localization using signal strength difference as location fingerprint
  • A M Hossain
  • W.-S Soh
A. M. Hossain and W.-S. Soh, "Cramer-Rao bound analysis of localization using signal strength difference as location fingerprint," in Proc. IEEE Annu. Joint Conf. Comput. Commun. Soc., Mar. 2010, pp. 1-9.
Bounds on performance of hybrid wifi-uwb cooperative rf localization for robotic applications
  • N Bargshady
  • N A Alsindi
  • K Pahlavan
  • Y Ye
N. Bargshady, N. A. Alsindi, K. Pahlavan, Y. Ye, and F. O. Akgul, "Bounds on performance of hybrid wifi-uwb cooperative rf localization for robotic applications," in Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications Workshops, pp. 277-282, 2010.
Implications of device diversity for organic localization
  • J G Park
  • D Curtis
  • S Teller
  • J Ledlie
J. G. Park, D. Curtis, S. Teller, and J. Ledlie, "Implications of device diversity for organic localization," in Proc. IEEE Annu. Joint Conf. Comput. Commun. Soc., 2011, pp. 3182-3190.
Zee: zeroeffort crowdsourcing for indoor localization
  • A Rai
  • K K Chintalapudi
  • V N Padmanabhan
  • R Sen
A. Rai, K. K. Chintalapudi, V. N. Padmanabhan, and R. Sen, "Zee: zeroeffort crowdsourcing for indoor localization," in Proceedings of the ACM Annual International Conference on Mobile Computing and Networking, pp. 293-304, 2012.