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BLEMAT-Context Modeling and Machine Learning for Indoor Positioning Systems
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Abstract
In this paper, we propose BLEMAT, a space-agnostic, context-aware fog computing system that performs real-time indoor positioning, fingerprinting and floor plan layout detection. BLEMAT acquires high accuracy and precision in position estimation while maintaining low resource utilization.
This is a preprint of an article submitted for consideration in
International Journal on Artificial Intelligence Tools © 2019 (copyright World Scientific Publishing Company: https://www.worldscientific.com/worldscinet/ijait).
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For all of types of applications in wireless sensor networks (WSNs), coverage is a fundamental and hot topic research issue. To monitor the interest field and obtain the valid data, the paper proposes a wireless sensor network coverage optimization model based on improved whale algorithm. The mathematic model of node coverage in wireless sensor networks is developed to achieve full coverage for the interest area. For the model, the idea of reverse learning is introduced into the original whale swarm optimization algorithm to optimize the initial distribution of the population. This method enhances the node search capability and speeds up the global search. The experiment shows that this algorithm can effectively improve the coverage of nodes in wireless sensor networks and optimize the network performance.
In this paper we present a Bluetooth Low Energy Microlocation Asset Tracking system (BLEMAT) that performs real-time position estimation and asset tracking based on BLE beacons and scanners. It is built on a context-aware fog computing system comprising Internet of Things controllers, sensors and a cloud platform, helped by machine-learning models and techniques. The BLEMAT system offers detecting signal propagation obstacles, performing signal perturbation correction and beacon paths exploration as well as auto discovery and onboarding of fog controller devices. These are the key characteristics of semi-supervised indoor position estimation services. In this paper we have shown there are solid basis that a fog computing system can efficiently carry out semi-supervised machine learning procedures for high-precision indoor position estimation and space modeling without the need for detailed input information (i.e. floor plan, signal propagation map, scanner position). In addition, the fog computing system inherently brings high level of system robustness, integrity, privacy and trust.
Recently, the problem of indoor localization based on WLAN signals is attracting increasing attention due to the development of mobile devices and the widespread construction of networks. However, no definitive solution for achieving a low-cost and accurate positioning system has been found. In most traditional approaches, solving the indoor localization problem requires the availability of a large number of labeled training samples, the collection of which requires considerable manual effort. Previous research has not provided a means of simultaneously reducing human calibration effort and improving location accuracy. This paper introduces fusion semi-supervised extreme learning machine (FSELM), a novel semi-supervised learning algorithm based on the fusion of information from Wi-Fi and Bluetooth Low Energy (BLE) signals. Unlike previous semi-supervised methods, which consider multiple signals individually, FSELM fuses multiple signals into a unified model. When applied to sparsely calibrated localization problems, our proposed method is advantageous in three respects. First, it can dramatically reduce the human calibration effort required when using a semi-supervised learning framework. Second, it utilizes fused Wi-Fi and BLE fingerprints to markedly improve the location accuracy. Third, it inherits the beneficial properties of ELMs with regard to training and testing speeds because the input weights and biases of hidden nodes can be generated randomly. As demonstrated by experimental results obtained on practical indoor localization datasets, FSELM possesses a better semi-supervised manifold learning ability and achieves higher location accuracy than several previous batch supervised learning approaches (ELM, BP and SVM) and semi-supervised learning approaches (SELM, S-RVFL and FS-RVFL). Moreover, FSELM needs less training and testing time, making it easier to apply in practice. We conclude through experiments that FSELM yields good results when applied to a multi-signal-based semi-supervised learning problem. The contributions of this paper can be summarized as follows: First, the findings indicate that effective multi-data fusion can be achieved not only through data-layer fusion, feature-layer fusion and decision-layer fusion but also through the fusion of constraints within a model. Second, for semi-supervised learning problems, it is necessary to combine the advantages of different types of data by optimizing the model’s parameters.
A large number of indoor positioning systems have recently been developed to cater for various location-based services. Indoor maps are a prerequisite of such indoor positioning systems; however, indoor maps are currently non-existent for most indoor environments. Construction of an indoor map by external experts excludes quick deployment and prevents widespread utilization of indoor localization systems. Here, we propose an algorithm for the automatic construction of an indoor floor plan, together with a magnetic fingerprint map of unmapped buildings using crowdsourced smartphone data. For floor plan construction, our system combines the use of dead reckoning technology, an observation model with geomagnetic signals, and trajectory fusion based on an affinity propagation algorithm. To obtain the indoor paths, the magnetic trajectory data obtained through crowdsourcing were first clustered using dynamic time warping similarity criteria. The trajectories were inferred from odometry tracing, and those belonging to the same cluster in the magnetic trajectory domain were then fused. Fusing these data effectively eliminates the inherent tracking errors originating from noisy sensors; as a result, we obtained highly accurate indoor paths. One advantage of our system is that no additional hardware such as a laser rangefinder or wheel encoder is required. Experimental results demonstrate that our proposed algorithm successfully constructs indoor floor plans with 0.48 m accuracy, which could benefit location-based services which lack indoor maps.
Bluetooth positioning is an important and challenging topic in indoor positioning. Although a lot of algorithms have been proposed for this problem, it is still not solved perfectly because of the instable signal strengths of Bluetooth. To improve the performance of Bluetooth positioning, this article proposes a coarse-to-fine positioning method based on weighted K-nearest neighbors and adaptive bandwidth mean shift. The method first employs weighted K-nearest neighbors to generate multi-candidate locations. Then, the testing position is obtained by applying adaptive bandwidth mean shift to the multi-candidate locations, which is used to search for the maximum density of the candidate locations. Experimental result indicates that the proposed method improves the performance of Bluetooth positioning.
Developing a large, but smart environment is a complex task that requires the collaboration of experts of different disciplines. How to successfully attain such collaboration is not a trivial matter. The paper illustrates the problem with a case study where the manager of the facility intends to influence pedestrians so that they choose a task that requires certain effort, e.g. using staircases, instead of the current one that requires less effort, e.g. using the elevator. Defining requirements for such scenarios requires a strong multidisciplinary collaboration which is not currently well supported. This paper contributes with an approach to provide non-technician experts with tools so that they can provide feedback on the requirements and verify them in a systematic way.
In this paper, we consider the route coordination problem in emergency evacuation of large smart buildings. The building evacuation time is crucial in saving lives in emergency situations caused by imminent natural or man-made threats and disasters. Conventional approaches to evacuation route coordination are static and predefined. They rely on evacuation plans present only at a limited number of building locations and possibly a trained evacuation personnel to resolve unexpected contingencies. Smart buildings today are equipped with sensory infrastructure that can be used for an autonomous situation-aware evacuation guidance optimized in real time. A system providing such a guidance can help in avoiding additional evacuation casualties due to the flaws of the conventional evacuation approaches. Such a system should be robust and scalable to dynamically adapt to the number of evacuees and the size and safety conditions of a building. In this respect, we propose a distributed route recommender architecture for situation-aware evacuation guidance in smart buildings and describe its key modules in detail. We give an example of its functioning dynamics on a use case.
The paper describes basic principles of a radio-based indoor localization and focuses on the improvement of its results with the aid of a new Bluetooth Low Energy technology. The advantage of this technology lies in its support by contemporary mobile devices, especially by smartphones and tablets. We have implemented a distributed system for collecting radio fingerprints by mobile devices with the Android operating system. This system enables volunteers to create radio-maps and update them continuously. New Bluetooth Low Energy transmitters (Apple uses its “iBeacon” brand name for these devices) have been installed on the floor of the building in addition to existing WiFi access points. The localization of stationary objects based on WiFi, Bluetooth Low Energy, and their combination has been evaluated using the data measured during the experiment in the building. Several configurations of the transmitters’ arrangement, several ways of combination of the data from both technologies, and other parameters influencing the accuracy of the stationary localization have been tested.
The presentation of Bluetooth Low Energy (BLE; e.g., Bluetooth 4.0) makes Bluetooth based indoor positioning have extremely broad application prospects. In this paper, we propose a received signal strength indication (RSSI) based Bluetooth positioning method. There are two phases in the procedure of our positioning: offline training and online locating. In the phase of offline training, we use piecewise fitting based on the lognormal distribution model to train the propagation model of RSSI for every BLE reference nodes, respectively, in order to reduce the influence of the positioning accuracy because of different locations of BLE reference nodes. Here we design a Gaussian filter to pre-process the receiving signals in different sampling points. In the phase of online locating, we use weighted sliding window to reduce fluctuations of the real-time signals. In addition, we propose a distance weighted filter based on triangle trilateral relations theorem, which can reduce the influence of positioning accuracy due to abnormal RSSI and improve the location accuracy effectively. Besides, in order to reduce the errors of targets coordinates caused by ordinary least squares method, we propose a collaborative localization algorithm based on Taylor series expansion. Another important feature of our method is the active learning ability of BLE reference nodes. Every reference node adjusts its pre-trained model according to the received signals from detecting nodes actively and periodically, which improve the accuracy of positioning greatly. Experiments show that the probability of locating error less than 1.5 meter is higher than 80% using our positioning method.
This paper proposes two schemes for indoor positioning by fusing Bluetooth beacons and a pedestrian dead reckoning (PDR) technique to provide meter-level positioning without additional infrastructure. As to the PDR approach, a more effective multi-threshold step detection algorithm is used to improve the positioning accuracy. According to pedestrians’ different walking patterns such as walking or running, this paper makes a comparative analysis of multiple step length calculation models to determine a linear computation model and the relevant parameters. In consideration of the deviation between the real heading and the value of the orientation sensor, a heading estimation method with real-time compensation is proposed, which is based on a Kalman filter with map geometry information. The corrected heading can inhibit the positioning error accumulation and improve the positioning accuracy of PDR. Moreover, this paper has implemented two positioning approaches integrated with Bluetooth and PDR. One is the PDR-based positioning method based on map matching and position correction through Bluetooth. There will not be too much calculation work or too high maintenance costs using this method. The other method is a fusion calculation method based on the pedestrians’ moving status (direct movement or making a turn) to determine adaptively the noise parameters in an Extended Kalman Filter (EKF) system. This method has worked very well in the elimination of various phenomena, including the “go and back” phenomenon caused by the instability of the Bluetooth-based positioning system and the “cross-wall” phenomenon due to the accumulative errors caused by the PDR algorithm. Experiments performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building in the China University of Mining and Technology (CUMT) campus showed that the proposed scheme can reliably achieve a 2-meter precision.
This paper discusses low-cost 3D indoor positioning with Bluetooth smart device and least square methods. 3D indoor location has become more and more attractive and it hasn't been well resolved. Almost each smart phone has a Bluetooth component and it can be used for indoor positioning and navigation in the nature of things. Least square algorithms are the powerful tools for linear and nonlinear parameters estimation. Various linear and nonlinear least square methods and their theoretical basics and application performance for indoor positioning have been studied. Simulation and hardware experiments results prove that nonlinear least square method is suitable for parameters estimation of Bluetooth signal propagation, and generalized least square method has better performance than total least square methods. Simulation and hardware experiments results also show that proposed method has the advantages of low cost, lost power consumption, perfect availability and high location accuracy.
The lack of floor plans is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and time-consuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this paper, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes and shapes. Our experiments on 3 stories of 2 large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about 1~2m and 5~9°, while the hallway connectivity is 100% correct.
Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past two decades. Most radio-based solutions require a process of site survey, in which radio signatures are collected and stored for further comparison and matching. Site survey involves intensive costs on manpower and time. In this work, we study unexploited RF signal characteristics and leverage user motions to construct radio floor plan that is previously obtained by site survey. On this basis, we design WILL, an indoor localization approach based on off-the-shelf WiFi infrastructure and mobile phones. WILL is deployed in a real building covering over 1600m2, and its deployment is easy and rapid since site survey is no longer needed. The experiment results show that WILL achieves competitive performance comparing with traditional approaches.
In this self-contained survey/review paper, we system- atically investigate the roots of Bayesian filtering as well as its rich leaves in the literature. Stochastic filtering theory is briefly reviewed with emphasis on nonlinear and non-Gaussian filtering. Following the Bayesian statistics, different Bayesian filtering techniques are de- veloped given different scenarios. Under linear quadratic Gaussian circumstance, the celebrated Kalman filter can be derived within the Bayesian framework. Optimal/suboptimal nonlinear filtering tech- niques are extensively investigated. In particular, we focus our at- tention on the Bayesian filtering approach based on sequential Monte Carlo sampling, the so-called particle filters. Many variants of the particle filter as well as their features (strengths and weaknesses) are discussed. Related theoretical and practical issues are addressed in detail. In addition, some other (new) directions on Bayesian filtering are also explored.
The positioning methods based on received signal strength (RSS) measurements, link the RSS values to the position of the mobile station(MS) to be located. Their accuracy depends on the suitability of the propagation models used for the actual propagation conditions. In indoor wireless networks, these propagation conditions are very difficult to predict due to the unwieldy and dynamic nature of the RSS. In this paper, we present a novel method which dynamically estimates the propagation models that best fit the propagation environments, by using only RSS measurements obtained in real time. This method is based on maximizing compatibility of the MS to access points (AP) distance estimates. Once the propagation models are estimated in real time, it is possible to accurately determine the distance between the MS and each AP. By means of these distance estimates, the location of the MS can be obtained by trilateration. The method proposed coupled with simulations and measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms conventional RSS-based indoor location methods without using any radio map information nor a calibration stage.
Positioning objects has been an important topic since it is needed to locate people, guide them to a certain place, and assist companies and organizations with their assets management. Several systems and algorithms were proposed to solve the positioning problem and to enhance existing systems. In this paper, we survey various indoor positioning systems to explore the related challenges that exist in this area and evaluate some proposed solutions. We provide a categorization and classification of the current indoor positioning systems and identify some possible areas of enhancements.
In this paper, we study the positioning schemes in Zigbee sensor networks. Basing on different conditions of indoor and outdoor environment, we propose two different types of positioning approaches. Considering the arrangement of the building, the indoor schemes cluster the localization area into many subzones, while the outdoor schemes use the estimated distance to determine the position of the sensed node. Our approaches bases on the receiving strength of the signal and does not need any location fingerprinting process in advance or location database. We also conduct field tests to see the performance of the proposed schemes. The results of the experiments show that our methods can achieve good accuracy no matter in indoor or outdoor environments.
This article presents an overview of the technical aspects of the
existing technologies for wireless indoor location systems. The two
major challenges for accurate location finding in indoor areas are the
complexity of radio propagation and the ad hoc nature of the deployed
infrastructure in these areas. Because of these difficulties a variety
of signaling techniques, overall system architectures, and location
finding algorithms are emerging for this application. This article
provides a fundamental understanding of the issues related to indoor
geolocation science that are needed for design and performance
evaluation of emerging indoor geolocation systems
Indoor positioning system (IPS) allows an object to be located and tracked within an indoor environment. With the introduction of Internet of Things (IoT), the business interest in location-based application and services has also increased. Hence, the demand for accurate indoor localization services has become important. Until now, researches related to IPS are still being conducted with the objective to improve the performance of positioning techniques. Trilateration is one of the techniques available to determine the location of an object. This paper proposes an improved WiFi trilateration-based method for indoor positioning system. The improved model is based on the test results which was conducted by using Intel Galileo (Gen2) board as an access point. The signal blocking problem caused by obstacles existed inside the building is resolved by improving received signal strength measurement. The proposed model includes implementation of trilateration technique to determine the position of users and then using specific reference points to improve the positioning results.
The complexity of indoor radio propagation has resulted in location-awareness being derived from empirical fingerprinting techniques, where positioning is performed via a previously-constructed radio map, usually of WiFi signals. The recent introduction of the Bluetooth Low Energy (BLE) radio protocol provides new opportunities for indoor location. It supports portable, battery-powered beacons that can be easily distributed at low cost, giving it distinct advantages over WiFi. However, its differing use of the radio band brings new challenges too. In this work we provide a detailed study of BLE fingerprinting using 19 beacons distributed around a 600 m2 testbed to position a consumer device. We demonstrate the high susceptibility of BLE to fast fading; show how to mitigate this; quantify the true power cost of continuous BLE scanning. We further investigate the choice of key parameters in a BLE positioning system, including beacon density, transmit power, and transmit frequency. We also provide quantitative comparison with WiFi fingerprinting. Our results show advantages to the use of BLE beacons for positioning. For one-shot (push-to-fix) positioning we achieve <2.6 m error 95% of the time for a dense BLE network (1 beacon per 30 m2), compared to <4.8 m for a reduced density (1 beacon per 100 m2) and <8.5 m for an established WiFi network in the same area.
Along with the proliferation of mobile devices and wireless signal coverage, indoor localization based on Wi-Fi gets great popularity. Fingerprint based method is the mainstream approach for Wi-Fi indoor localization, for it can achieve high localization performance as long as labeled data are sufficient. However, the number of labeled data is always limited due to the high cost of data acquisition. Nowadays, crowd sourcing becomes an effective approach to gather large number of data; meanwhile, most of them are unlabeled. Therefore, it is worth studying the use of unlabeled data to improve localization performance. To achieve this goal, a novel algorithm Semi-supervised Deep Extreme Learning Machine (SDELM) is proposed, which takes the advantages of semi-supervised learning, Deep Leaning (DL), and Extreme Learning Machine (ELM), so that the localization performance can be improved both in the feature extraction procedure and in the classifier. The experimental results in real indoor environments show that the proposed SDELM not only outperforms other compared methods but also reduce the calibration effort with the help of unlabeled data.
AdaBoost is a popular method for vehicle detection, but the training process is quite time-consuming. In this paper, a rapid learning algorithm is proposed to tackle this weakness of AdaBoost for vehicle classification. Firstly, an algorithm for computing the Haar-like feature pool on a 32 × 32 grayscale image patch by using all simple and rotated Haar-like prototypes is introduced to represent a vehicle’s appearance. Then, a fast training approach for the weak classifier is presented by combining a sample’s feature value with its class label. Finally, a rapid incremental learning algorithm of AdaBoost is designed to significantly improve the performance of AdaBoost. Experimental results demonstrate that the proposed approaches not only speed up the training and incremental learning processes of AdaBoost, but also yield better or competitive vehicle classification accuracies compared with several state-of-the-art methods, showing their potential for real-time applications.
We propose a graph-based, low-complexity sensor fusion approach for ubiquitous pedestrian indoor positioning using mobile devices. We employ our fusion technique to combine relative motion information based on step detection with WiFi signal strength measurements. The method is based on the well-known particle filter methodology. In contrast to previous work, we provide a probabilistic model for location estimation that is formulated directly on a fully discretized, graph-based representation of the indoor environment. We generate this graph by adaptive quantization of the indoor space, removing irrelevant degrees of freedom from the estimation problem. We evaluate the proposed method in two realistic indoor environments using real data collected from smartphones. In total, our dataset spans about 20 kilometers in distance walked and includes 13 users and four different mobile device types. Our results demonstrate that the filter requires an order of magnitude less particles than state-of-the-art approaches while maintaining an accuracy of a few meters. The proposed low-complexity solution not only enables indoor positioning on less powerful mobile devices, but also saves much-needed resources for location-based end-user applications which run on top of a localization service.
People spend approximately 70% of their time indoors. Understanding the indoor environments is therefore important for a wide range of emerging mobile personal and social applications. Knowledge of indoor floorplans is often required by these applications. However, indoor floorplans are either unavailable or obtaining them requires slow, tedious, and error-prone manual labor.
This paper describes an automatic indoor floorplan construction system. Leveraging Wi-Fi fingerprints and user motion information, this system automatically constructs floorplan via three key steps: (1) room adjacency graph construction to determine which rooms are adjacent; (2) hallway layout learning to estimate room sizes and order rooms along each hallway, and (3) force directed dilation to adjust room sizes and optimize the overall floorplan accuracy. Deployment study in three buildings with 189 rooms demonstrates high floorplan accuracy. The system has been implemented as a mobile middleware, which allows emerging mobile applications to generate, leverage, and share indoor floorplans.
In this paper, we propose a novel location tracking system called SCaNME (Shotgun Clustering-aided Navigation in Mobile Environment) which iteratively sequences the clusters of sporadically recorded received signal strength (RSS) measurements and adaptively construct a mobility map of the environment for location tracking. In the SCaNME system, the location tracking problem is solved by first matching the people’s locations to the location points (LPs) with small Kullback–Leibler (KL) divergence. Then, Allen’s logics are applied to reveal the person’s activities, assist the on-line location tracking and finally obtain a refined path estimate. The experimental results conducted on the large-scale HKUST campus demonstrate that the SCaNME tracking system provides better precision and reliability than the conventional location tracking systems. Furthermore, the experiments of SCaNME tracking system show its capability of providing people’s real-time locations without fingerprint calibration in large-scale Wi-Fi environment.
Indoor pedestrian tracking extends location-based services to indoor environments. Typical indoor positioning systems employ a training/positioning model using Wi-Fi fingerprints. While these approaches have practical results in terms of accuracy and coverage, they require an indoor map, which is typically not available to the average user and involves significant training costs. A practical indoor pedestrian tracking approach should consider the indoor environment without a pretrained database or floor plan. In this paper, we present an indoor pedestrian tracking system, called SmartSLAM, which automatically constructs an indoor floor plan and radio fingerprint map for anonymous buildings using a smartphone. The scheme employs odometry tracing using inertial sensors, an observation model using Wi-Fi signals, and a Bayesian estimation for floor-plan construction. SmartSLAM is a true simultaneous localization and mapping implementation that does not necessitate additional devices, such as laser rangefinders or wheel encoders. We implemented the scheme on off-the-shelf smartphones and evaluated the performance in our university buildings. Despite inherent tracking errors from noisy sensors, SmartSLAM successfully constructed indoor floor plans.
Indoor Navigation is becoming a more and more important topic. It is a key tool for the integration of disabled people into environments which they are not familiar with. With this paper we want to address the problem of indoor navigation being much more complex than outdoor navigation with a thorough modelling of queries which a indoor navigation GIS database shall be able to answer quickly and present an organisation of indoor navigation data tailored to scalable and flexible indoor navigation.
Abstract The tracking of the locations of moving objects in large indoor spaces is important, as it enables a range of appli- cations related to, e.g., security and indoor navigation and guidance. This paper presents a graph model based ap- proach to indoor tracking that offers a uniform data man- agement,infrastructure for different symbolic positioning technologies, e.g., Bluetooth and RFID. More specifically, the paper proposes a model of indoor space that comprises a base graph and mappings,that represent the topology of indoor space at different levels. The resulting model can be used for one or several indoor positioning technologies. Fo- cusing on RFID-based positioning, an RFID specific reader deployment graph model is built from the base graph model. This model is then used in several algorithms for construct- ing and refining trajectories from raw RFID readings. Em- pirical studies with implementations,of the models and al- gorithms suggest that the paper’s proposals are effective and efficient.
In this paper we identify the common techniques and technologies that are enabling location identification in a ubiquitous computing environment. We also address the important parameters for evaluating such systems. Through this survey, we explore the current trends in commercial products and research in the area of local- ization. Although localization is an old concept, further research is needed to make it really usable for ubiqui- tous computing. Therefore, we indicate future research directions and address localization in the framework of our Smart Surroundings project.
Analytical models to evaluate and predict "precision" performance of indoor positioning systems based on location fingerprinting are lacking. Such models can be used to improve the design of positioning systems, for example by eliminating some fingerprints and reducing the size of the location fingerprint database. In this paper, we develop a new analytical model that employs proximity graphs for predicting performance of indoor positioning systems based on location fingerprinting. The model allows computation of an approximate probability distribution of error distance given a location fingerprint database based on received signal strength and its associated statistics. The performance results from the simulation and the analytical model are found to be congruent. This model also allows us to perform analysis of the internal structure of location fingerprints. We employ the analysis of the internal structure to identify and eliminate unnecessary location fingerprints stored in the database, thereby saving on computation while performing location estimation.
Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.
We present a statistical path loss model derived from 1.9 GHz
experimental data collected across the United States in 95 existing
macrocells. The model is for suburban areas, and it distinguishes
between different terrain categories. Moreover, it applies to distances
and base antenna heights not well-covered by existing models. The
characterization used is a linear curve fitting the decibel path loss to
the decibel-distance, with a Gaussian random variation about that curve
due to shadow fading. The slope of the linear curve (corresponding to
the path loss exponent, γ) is shown to be a random variate from
one macrocell to another, as is the standard deviation σ of the
shadow fading. These two parameters are statistically modeled, with the
dependencies on base antenna height and terrain category made explicit.
The resulting path loss model applies to base antenna heights from 10 to
80 m, base-to-terminal distances from 0.1 to 8 km, and three distinct
terrain categories
The current products and the latest research in the field of location sensing systems for mobile computing applications are discussed. Three major techniques to determine a given location are identified. These include triangulation, which uses multiple distance measurements between known points; proximity measuring nearness to a known set of points; and scene analysis, which examines a view from a particular vintage point. Some of the location sensing systems discussed include: the Active Badge location system that uses diffuse infrared technology; the Active Bat location system, which uses an ultrasound time-of-flight lateration technique to provide physical positioning; and ad hoc location sensing.
Comparison of empirical propagation path loss models for fixed wireless access systems
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