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Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)—i.e., one of the state-of-the-art multi-building and multi-floor localization models—and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of "by a single building", where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42   m. [ABSTRACT FROM AUTHOR]

Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)—i.e., one of the state-of-the-art multi-building and multi-floor localization models—and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of “by a single building”, where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42 m.

Establishing a highly accurate positioning of radio sources for radio wave monitoring and frequency spectrum sharing is attracting considerable attention. Because the positioning method generally requires specific processing of the position target, it is not applicable when the position target cannot perform any processing. The location fingerprinting method uses multiple sensors to observe the received signal strength indication (RSSI) emitted by a radio source and estimates the position from the radio wave propagation characteristics. This does not require a certain process for the target position. However, it takes time to gather RSSI from many sensors by wireless communication. In this study, we propose an RSSI gathering method using physical wireless parameter conversion sensor networks (PhyC-SN) for the high-speed positioning of radio sources. In the proposed method, each sensor selects the radio carrier frequency corresponding to its measured RSSI and transmits the signal. Projecting the RSSI distribution of each sensor onto the frequency distribution of the received signal enables the center to detect the RSSI of multiple sensors simultaneously. Furthermore, to improve the gathering accuracy, we established a sensor group method by considering the regional characteristics and access timing control based on each sensor group. Computer simulations and experimental evaluations show that the proposed method significantly reduces the data-gathering time compared with conventional packet communications and achieves a high positioning accuracy.

In recent years, aged care living has drawn attention because of population aging and extension of average lifespan. Moreover, the rapid development of information communication technology and the internet of things lay the foundation for the application of sensor networks and cloud computing on medical care. Most elderly often suffer from chronic diseases due to weaker immunity causing a higher infection risk. The problem of infection controlling is an essential issue for residences living in aged care facilities. In the past, for infection control, medical personnel use the manual method of tracking, investigating, and interviewing patients to obtain patient contact list. This method cannot identify residents who have a high risk of infection, distinguish the distance between patients and other residents, and evaluate the risk of infection. To isolate all the residents who contact patients is the only solution that does not only cause repulsion of other residents but also waste medical resources. This study proposes a visual infection control positioning system for aged care facilities using the RFID (Radio Frequency Identification) technology and IoT technology. It can calculate the distance between users and reduce the positioning error. The system combines location fingerprinting with the K-nearest neighbor algorithm to fix errors caused by signal fluctuation of RFID to improve the positioning accuracy. The system records the indoor movement path of the residents in a real-time manner through the positioning function. When infectious diseases spread in aged care living residences, to help medical personnel, the system can screen out residents with a high risk of infection who closely contact with patients according to the movement path of the patients and other residents, and visually display them. For some infectious diseases, residents who live together with patients may be infected even if they do not have contact with the patients closely. This system can also identify the residents whose cumulative contact time exceeds the limit according to the medical definition of various infectious diseases and input time value. [ABSTRACT FROM AUTHOR]

Location Fingerprinting (LF) is a promising localization technique that enables enormous commercial and industrial Location-Based Services (LBS). Existing approaches either appeal to the simple Received Signal Strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer Channel State Information (CSI), whose intricate structure leads to an increased computational complexity. In this paper, we adopt Autoregressive (AR) modeling based entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while exploiting the most location-specific statistical channel information. On this basis, we design EntLoc, a CSI-based probabilistic indoor localization system using commercial off-the-shelf Wi-Fi devices. EntLoc is deployed in an office building covering over 200 m2. Extensive indoor scenario experiments corroborate that our proposed system yields superior localization accuracy over previous approaches even with only one signal transmitter.

Location fingerprinting is a technique widely suggested for challenging indoor positioning. Despite the significant benefits of this technique, it needs a considerable amount of time and energy to measure the Received Signal Strength (RSS) at Reference Points (RPs) and build a fingerprinting database to achieve an appropriate localization accuracy. Reducing the number of RPs can reduce this cost, but it noticeably degrades the accuracy of positioning. In order to alleviate this problem, this paper takes the interior architecture of the indoor area and signal propagation effects into account and proposes two novel recovery methods for creating the reconstructed database instead of the measured one. They only need a few numbers of RPs to reconstruct the database and even are able to produce a denser database. The first method is a new zone-based path-loss propagation model which employs fingerprints of different zones separately and the second one is a new interpolation method, zone-based Weighted Ring-based (WRB). The proposed methods are compared with the conventional path-loss model and six interpolation functions. Two different test environments along with a benchmarking testbed, and various RPs configurations are also utilized to verify the proposed recovery methods, based on the reconstruction errors and the localization accuracies they provide. The results indicate that by taking only 11% of the initial RPs, the new zone-based path-loss model decreases the localization error up to 26% compared to the conventional path-loss model and the proposed zone-based WRB method outperforms all the other interpolation methods and improves the accuracy by 40%.

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. This paper provides a comprehensive review of deep learning methods in indoor positioning. First, the advantages and disadvantages of various fingerprint types for indoor positioning are discussed. The solutions proposed in the literature are then analyzed, categorized, and compared against various performance evaluation metrics. Since data is key in fingerprinting, a detailed review of publicly available indoor positioning datasets is presented. While incorporating deep learning into fingerprinting has resulted in significant improvements, doing so, has also introduced new challenges. These challenges along with the common implementation pitfalls are discussed. Finally, the paper is concluded with some remarks as well as future research trends. [ABSTRACT FROM AUTHOR]

Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data and Wi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and trilateration for Wi-Fi positioning. Additionally, a pedestrian dead reckoning (PDR) algorithm is used for position estimation in indoor scenarios. The proposed framework achieves a maximum of 1.17 m localization error for the rectangular motion of a pedestrian and a maximum of 0.44 m localization error for linear motion. [ABSTRACT FROM AUTHOR]

A guidance and information service for a University library based on Wi-Fi signals using fingerprinting as chosen localization method is under development at TU Wien. After a thorough survey of suitable location technologies for the application it was decided to employ mainly Wi-Fi for localization. For that purpose, the availability, performance, and usability of Wi-Fi in selected areas of the library are analyzed in a first step. These tasks include the measurement of Wi-Fi received signal strengths (RSS) of the visible access points (APs) in different areas. The measurements were carried out in different modes, such as static, kinematic and in stop-and-go mode, with six different smartphones. A dependence on the positioning and tracking modes is seen in the tests. Kinematic measurements pose much greater challenges and depend significantly on the duration of a single Wi-Fi scan. For the smartphones, the scan durations differed in the range of 2.4 to 4.1 s resulting in different accuracies for kinematic positioning, as fewer measurements along the trajectories are available for a device with longer scan duration. The investigations indicated also that the achievable localization performance is only on the few meter level due to the small number of APs of the University own Wi-Fi network deployed in the library. A promising solution for performance improvement is the foreseen usage of low-cost Raspberry Pi units serving as Wi-Fi transmitter and receiver.

Indoor positioning poses a number of challenges, especially in large and complex buildings. Several effects, such as signal attenuation, signal fluctuations, interference, and multipath play a decisive role in signal propagation. The severity of each challenge depends on the method and technology adopted to perform user localization. Wi-Fi is a popular method because of its ubiquity with already available public and private infrastructure in many environments and the ability for mobile clients, such as smartphones, to receive these signals. In this contribution, the fundamental concepts and basics and the evolution of Wi-Fi as the most widely used indoor positioning technology are reviewed and demonstrated using four different conducted case studies. Starting from an analysis of the properties of Wi-Fi signals and their propagation, suitable techniques are identified. The mathematical models of location fingerprinting and lateration are consolidated and assessed as well as new technology directions and developments highlighted. Results of the case studies demonstrate the capability of Wi-Fi for continuous user localization also in dynamic environments and kinematic mode where the user walks with a usual step speed. However, to achieve acceptable localization accuracy, calibration of the devices is required to mitigate the variance problems due to the device heterogeneity.

Signal strength-based positioning in wireless sensor networks is a key technology for seamless, ubiquitous localization, especially in areas where Global Navigation Satellite System (GNSS) signals propagate poorly. To enable wireless local area network (WLAN) location fingerprinting in larger areas while maintaining accuracy, methods to reduce the effort of radio map creation must be consolidated and automatized. Gaussian process regression has been applied to overcome this issue, also with auspicious results, but the fit of the model was never thoroughly assessed. Instead, most studies trained a readily available model, relying on the zero mean and squared exponential covariance function, without further scrutinization. This paper studies the Gaussian process regression model selection for WLAN fingerprinting in indoor and outdoor environments. We train several models for indoor/outdoor- and combined areas; we evaluate them quantitatively and compare them by means of adequate model measures, hence assessing the fit of these models directly. To illuminate the quality of the model fit, the residuals of the proposed model are investigated, as well. Comparative experiments on the positioning performance verify and conclude the model selection. In this way, we show that the standard model is not the most appropriate, discuss alternatives and present our best candidate.

The Internet of Things (IoT) paradigm aims to interconnect a variety of heterogeneous Smart Objects (SO) using energy-efficient methodologies and standard communication protocols. A majority of consumer devices sold today come equipped with wireless LAN and cellular technology to connect with the world-wide network. To discover Wi-Fi hot spots, there is a need for constant scanning of Wi-Fi radio in these devices and results in significant battery drain. We present PRiSM, a practical system to automatically locate Wi-Fi hotspots while Wi-Fi radio is turned off, by using the statistical characteristics of cellular signals. Cellular signals are received at zero extra cost in mobile devices and hence PRiSM is highly energy-efficient. It is a lightweight client-side only implementation and needs no prior knowledge on floor plans or wireless infrastructure. We implement PRiSM on Android-based devices and show up to 96% of energy savings in Wi-Fi sensing operations which is equivalent to saving up to 16% of total battery capacity, together with an average prediction accuracy of up to 98%.

Location fingerprinting locates devices based on pattern matching signal observations to a pre-defined signal map. This paper introduces a technique to enable fast signal map creation given a dedicated surveyor with a smartphone and floorplan. Our technique (PFSurvey) uses accelerometer, gyroscope, and magnetometer data to estimate the surveyor's trajectory post-hoc using Simultaneous Localization and Mapping and particle filtering to incorporate a building floorplan. We demonstrate conventional methods can fail to recover the survey path robustly and determine the room unambiguously. To counter this, we use a novel loop closure detection method based on magnetic field signals and propose to incorporate the magnetic loop closures and straight-line constraints into the filtering process to ensure robust trajectory recovery. We show this allows room ambiguities to be resolved. An entire building can be surveyed by the proposed system in minutes rather than days. We evaluate in a large office space and compare to state-of-the-art approaches. We achieve trajectories within 1.1 m of the ground truth 90 percent of the time. Output signal maps well approximate those built from conventional, laborious manual survey. We also demonstrate that the signal maps built by PFSurvey provide similar or even better positioning performance than the manual signal maps. [ABSTRACT FROM AUTHOR]

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Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data and Wi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and trilateration for Wi-Fi positioning. Additionally, a pedestrian dead reckoning (PDR) algorithm is used for position estimation in indoor scenarios. The proposed framework achieves a maximum of 1.17 m localization error for the rectangular motion of a pedestrian and a maximum of 0.44 m localization error for linear motion.

Positioning of mobile users in indoor environments with Wireless Fidelity (Wi-Fi) has become very popular whereby location fingerprinting and trilateration are the most commonly employed methods. In both the received signal strength (RSS) of the surrounding access points (APs) are scanned and used to estimate the user's position. Within the scope of this study the advantageous qualities of both methods are identified and selected to benefit their combination. By a fusion of these technologies a higher performance for Wi-Fi positioning is achievable. For that purpose, a novel approach based on the well-known Differential GPS (DGPS) principle of operation is developed and applied. This approach for user localization and tracking is termed Differential Wi-Fi (DWi-Fi) by analogy with DGPS. From reference stations deployed in the area of interest differential measurement corrections are derived and applied at the mobile user side. Hence, range or coordinate corrections can be estimated from a network of reference station observations as it is done in common CORS GNSS networks. A low-cost realization with Raspberry Pi units is employed for these reference stations. These units serve at the same time as APs broadcasting Wi-Fi signals as well as reference stations scanning the receivable Wi-Fi signals of the surrounding APs. As the RSS measurements are carried out continuously at the reference stations dynamically changing maps of RSS distributions, so-called radio maps, are derived. Similar as in location fingerprinting this radio maps represent the RSS fingerprints at certain locations. From the areal modelling of the correction parameters in combination with the dynamically updated radio maps the location of the user can be estimated in real-time. The novel approach is presented and its performance demonstrated in this paper. [ABSTRACT FROM AUTHOR]

For Wi-Fi positioning location fingerprinting is one of the most commonly employed localization technique. To achieve an acceptable level of positioning accuracy on the few meter level, i.e., to provide at least room resolution in buildings, such an approach is very labour consuming as it requires a high density of reference points. Thus, the novel approach developed aims at a significant reduction of workload for the training phase. The basic idea is to intelligently choose waypoints along possible users' trajectories in the indoor environment. These waypoints are termed intelligent checkpoints (iCPs) and serve as reference points for the fingerprinting localization approach. They are selected along the trajectories in such a way that they define a logical sequence with their ascending order. Thereby, the iCPs are located, for instance, at doors at entrances to buildings, rooms, along corridors, etc., or in low density along the trajectory to provide a suitable absolute user localization. Continuous positioning between these iCPs is obtained with the help of the smartphones' inertial sensors. While walking along a selected trajectory to the destination a dynamic recognition of the iCPs is performed and the drift of the inertial sensors is reduced as the iCP recognition serves as absolute position update. Conducted experiments in a multi-storey office building have shown that positioning accuracy of around 2.0 m are achievable which goes along with a reduction of workload by three quarter using this novel approach. The iCP concept and performance are presented and demonstrated in this paper. [ABSTRACT FROM AUTHOR]

For Wi-Fi positioning location fingerprinting is very common but has the disadvantage that it is very labour consuming for the establishment of a database (DB) with received signal strength (RSS) scans measured on a large number of known reference points (RPs). To overcome this drawback a novel approach is developed which uses a logical sequence of intelligent checkpoints (iCPs) instead of RPs distributed in a regular grid. The iCPs are the selected RPs which have to be passed along the way for navigation from a start point A to the destination B. They are twofold intelligent because of the fact that they depend on their meaningful selection and because of their logical sequence in their correct order. Thus, always the following iCP is known due to a vector graph allocation in the DB and only a small limited number of iCPs needs to be tested when matching the current RSS scans. This reduces the required processing time significantly. It is proven that the iCP approach achieves a higher success rate than conventional approaches. In average correct matching results of 90.0% were achieved using a joint DB including RSS scans of all employed smartphones. An even higher success rate is achieved if the same mobile device is used in both the training and positioning phase. [ABSTRACT FROM AUTHOR]

Radio-tag signals from fixed-position antennas are most often used to indicate presence or absence of individuals, or to estimate individual activity levels from signal strength variation within an antenna's detection zone. The potential of such systems to provide more precise information on tag location and movement has not been explored in great detail in an ecological setting., By reversing the roles that transmitters and receivers play in localization methods common to the telecommunications industry, we present a new telemetric tool for accurately estimating the location of tagged individuals from received signal strength values. The methods used to characterize the study area in terms of received signal strength are described, as is the random forest model used for localization. The resulting method is then validated using test data before being applied to true data collected from tagged individuals in the study site., Application of the localization method to test data withheld from the learning dataset indicated a low average error over the entire study area (<1 m), whereas application of the localization method to real data produced highly probable results consistent with field observations., This telemetric approach provided detailed movement data for tagged fish along a single axis (a migratory path) and is particularly useful for monitoring passage along migratory routes. The new methods applied in this study can also be expanded to include multiple axes ( x, y, z) and multiple environments (aquatic and terrestrial) for remotely monitoring wildlife movement. [ABSTRACT FROM AUTHOR]

With the rapid growing market of wireless devices, positioning systems that make use of the signal strength of wireless devices are gaining more interest nowadays. Being able to track the location of a Wi-Fi or Radio Frequency Identification device could improve the quality of services in various sectors, including security, warehouse, logistic management, and healthcare. As compared with outdoor environment, positioning systems face a greater challenge in indoor environment because wireless signal is significantly influenced by building layout and surrounding objects, for which a location fingerprinting approach is needed. Moreover, the signal strength of a wireless device may also change over time, which is known as temporal variation, and therefore a reliable location estimation system must have the ability to learn and adapt with temporal changes. However, if the learning process is highly complex and requires long processing time, deploying the system into a larger scale would not be feasible. In recent years, Extreme Learning Machine (ELM) has surfaced as a viable alternative that challenged the norm of iterative and progressive learning. ELM has also been considered as a solution for indoor location fingerprinting. However, there has not been a comprehensive review on how the ELM-based approaches are linked with existing location fingerprinting techniques. Here we discuss some major location fingerprinting techniques, which are nearest-neighbor, LANDMARC, and LEMT, and formulate a new framework for systematically translating the techniques into ELM-based methods. [ABSTRACT FROM AUTHOR]

Nowadays, there is a great interest in developing accurate wireless indoor localization mechanisms enabling the implementation of many consumer-oriented services. Among the many proposals, wireless indoor localization mechanisms based on the Received Signal Strength Indication (RSSI) are being widely explored. Most studies have focused on the evaluation of the capabilities of different mobile device brands and wireless network technologies. Furthermore, different parameters and algorithms have been proposed as a means of improving the accuracy of wireless-based localization mechanisms. In this paper, we focus on the tuning of the RSSI fingerprint to be used in the implementation of a Bluetooth Low Energy 4.0 (BLE4.0) Bluetooth localization mechanism. Following a holistic approach, we start by assessing the capabilities of two Bluetooth sensor/receiver devices. We then evaluate the relevance of the RSSI fingerprint reported by each BLE4.0 beacon operating at various transmission power levels using feature selection techniques. Based on our findings, we use two classification algorithms in order to improve the setting of the transmission power levels of each of the BLE4.0 beacons. Our main findings show that our proposal can greatly improve the localization accuracy by setting a custom transmission power level for each BLE4.0 beacon. [ABSTRACT FROM AUTHOR]

The development of the Internet of Things (IoT) has accelerated research in indoor navigation systems, a majority of which rely on adequate wireless signals and sources. Nonetheless, deploying such a system requires periodic site-survey, which is time consuming and labor intensive. To address this issue, in this paper we present Canoe, an indoor navigation system that considers shopping mall scenarios. In our system, we do not assume any prior knowledge, such as floor-plan or the shop locations, access point placement or power settings, historical RSS measurements or fingerprints, etc. Instead, Canoe requires only that the shop owners collect and publish RSS values at the entrances of their shops and can direct a consumer to any of these shops by comparing the observed RSS values. The locations of the consumers and the shops are estimated using maximum likelihood estimation. In doing this, the direction of the target shop relative to the current orientation of the consumer can be precisely computed, such that the direction that a consumer should move can be determined. We have conducted extensive simulations using a real-world dataset. Our experiments in a real shopping mall demonstrate that if 50% of the shops publish their RSS values, Canoe can precisely navigate a consumer within 30 s, with an error rate below 9%. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology，indoor localization technology has become one o£ the most important research hotspots in location based service (LBS). Hybrid algorithms combining the received signal strength (RSS) location fingerprinting and pedestrian dead reckoning (PDR) can effectively improve the localization accuracy，but existing algorithms can hardly achieve higher localization accuracy and smaller calculation simultaneously. The common Kalman filter algorithm has limit localization accuracy while a large amount of calculation is required by the particle filter algorithm. We propose a hybrid localization algorithm based on multi-fingerprint union matching，which fuses the inertial sensor and the RSS fingerprint information effectively. Under the premise of low calculation amount， it can achieve higher precision of localization. Experimental results show that，80% of the errors remain within lm and the average accuracy reaches as high as 0. 77m. [ABSTRACT FROM AUTHOR]

The major problem of Wi-Fi fingerprint-based positioning technology is the signal strength fingerprint database creation and maintenance. The significant temporal variation of received signal strength (RSS) is the main factor responsible for the positioning error. A probabilistic approach can be used, but the RSS distribution is required. The Gaussian distribution or an empirically-derived distribution (histogram) is typically used. However, these distributions are either not always correct or require a large amount of data for each reference point. Double peaks of the RSS distribution have been observed in experiments at some reference points. In this paper a new algorithm based on an improved double-peak Gaussian distribution is proposed. Kurtosis testing is used to decide if this new distribution, or the normal Gaussian distribution, should be applied. Test results show that the proposed algorithm can significantly improve the positioning accuracy, as well as reduce the workload of the off-line data training phase.

Localization systems for indoor areas have recently been suggested that make use of existing wireless local areanetwork (WLAN) infrastructure and location fingerprinting approach. However, most existing research work ignores channel interference between wireless infrastructures and this could affect accurate and precise positioning. A better understanding of the properties of channel interference could assist in improving the positioning accuracy while saving significant amounts of resources in the location-aware infrastructure. This paper investigates to what extent the positioning accuracy is affected by channel interference between access points. Two sets of experiments compare how the positioning accuracy is affected in three different channel assignment schemes: ad-hoc, sequential, and orthogonal data is analyzed to understand what features ofchannel interference affect positioning accuracy. The results show that choosing an appropriate channel assignment scheme could make localization 10% more accurate and reduces the number of access points that are required by 15%. The experimental analysis also indicates that the channel interference usually obeys a right-skewed distribution and positioning accuracy is heavily dependent on channel interference between access points (APs).

A large scale WLAN infrastructure requires the placement of many thousands of access points (APs). The current approach is to deploy these in an empirical and ad-hoc manner. However, this deployment results in poor resource utilization and inaccurate positioning due to signal overlap and black spots. In this paper, we propose three structured approaches to WLAN infrastructure deployment that would allow better positioning accuracy and optimal coverage. These three approaches make use of triangular, square, and hexagonal configurations. Our results show that all three are more effective in both 2-D and 3-D space than any of the current ad-hoc or empirical approaches to AP deployment. Overall, the hexagonal approach is the most cost effective and accurate. It allows better positioning with fewer APs than are normally used. As a further contribution, 3-D rendering of buildings and wireless signal coverage could give engineers a concrete visualization that helps them to foresee where the blind spots are in advance and how signal varied across multi-story buildings, such that engineers could estimate the optimal number of APs and where they should be placed.

Location aware applications are constantly under development. By using an Indoor Location System (ILS), a user can localize himself, plan an indoor route and destination, or receive useful information and services in malls, airports, shopping centers, etc. To this purpose, various signal strength or timing based localization methods exist, each one having their own advantages and disadvantages. Localization based on the Received Signal Strength (RSS) methods, typically require only off-theshelf equipment to operate. Moreover, they can utilize the existing infrastructure. This paper presents a new method on how to use the captured RSS values for localization purposes. The proposed method analyses a captured signal over a short distance and stores it into the fingerprint database for later comparison, rather than using an average value obtained from static measurements. Performance of proposed method is compared with a typical RSS-based localization method. Real-world measurements are used in order to validate our approach. [ABSTRACT FROM AUTHOR]

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 \sim\! 600\ \mboxm^2 testbed to position a consumer device. We demonstrate the high susceptibility of BLE to fast fading, show how to mitigate this, and 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\ \mboxm^2), compared to < 4.8 m for a reduced density (1 beacon per 100\ \mboxm^2) and < 8.5 m for an established WiFi network in the same area. [ABSTRACT FROM AUTHOR]

Nowadays, there is a great interest in developing accurate wireless indoor localization mechanisms enabling the implementation of many consumer-oriented services. Among the many proposals, wireless indoor localization mechanisms based on the Received Signal Strength Indication (RSSI) are being widely explored. Most studies have focused on the evaluation of the capabilities of different mobile device brands and wireless network technologies. Furthermore, different parameters and algorithms have been proposed as a means of improving the accuracy of wireless-based localization mechanisms. In this paper, we focus on the tuning of the RSSI fingerprint to be used in the implementation of a Bluetooth Low Energy 4.0 (BLE4.0) Bluetooth localization mechanism. Following a holistic approach, we start by assessing the capabilities of two Bluetooth sensor/receiver devices. We then evaluate the relevance of the RSSI fingerprint reported by each BLE4.0 beacon operating at various transmission power levels using feature selection techniques. Based on our findings, we use two classification algorithms in order to improve the setting of the transmission power levels of each of the BLE4.0 beacons. Our main findings show that our proposal can greatly improve the localization accuracy by setting a custom transmission power level for each BLE4.0 beacon.

Signal strength-based positioning in wireless sensor networks is a key technology for seamless, ubiquitous localization, especially in areas where Global Navigation Satellite System (GNSS) signals propagate poorly. To enable wireless local area network (WLAN) location fingerprinting in larger areas while maintaining accuracy, methods to reduce the effort of radio map creation must be consolidated and automatized. Gaussian process regression has been applied to overcome this issue, also with auspicious results, but the fit of the model was never thoroughly assessed. Instead, most studies trained a readily available model, relying on the zero mean and squared exponential covariance function, without further scrutinization. This paper studies the Gaussian process regression model selection for WLAN fingerprinting in indoor and outdoor environments. We train several models for indoor/outdoor- and combined areas; we evaluate them quantitatively and compare them by means of adequate model measures, hence assessing the fit of these models directly. To illuminate the quality of the model fit, the residuals of the proposed model are investigated, as well. Comparative experiments on the positioning performance verify and conclude the model selection. In this way, we show that the standard model is not the most appropriate, discuss alternatives and present our best candidate. [ABSTRACT FROM AUTHOR]

Received signal strength (RSS) in Wi-Fi networks is commonly employed in indoor positioning systems; however, variations in hardware challenge robustness of this approach. This paper proposes a novel positioning feature, which is called delta-fused principal strength (DFPS), to enhance the robustness of Wi-Fi localization against the problem of heterogeneous hardware. The proposed algorithm computes the pairwise delta RSS and then integrates with RSS using principal component analysis. This paper makes two principal contributions: First, the fused positioning feature embeds discriminative power within delta RSS to compensate for the problem of heterogeneous devices in a unified framework, and second, DFPS provides increased flexibility in determining the number of required components and achieves better computational efficiency. We applied DFPS to location fingerprinting systems in a realistic indoor Wi-Fi environment. Experimental results demonstrate the effectiveness of proposed algorithm, where DFPS outperforms previous robust positioning features for heterogeneous and homogeneous devices. [ABSTRACT FROM PUBLISHER]

Due to advances in mobile technology, context-aware applications are continuously growing in importance; therefore, the ability of developing accurate and reliable localization system has become a necessity. Since methods based on received signal strength (RSS) fingerprints are today widely adopted and most of mobile devices comprise different wireless access technologies, it is feasible to use fingerprints from heterogeneous wireless networks (HWN) for localization purposes. In this paper we propose a novel approach for localization based on searching the area which best matches the test RSS fingerprint. We evaluated the proposed method in realistic environment in WLAN, GSM and UMTS networks, and compared it with other commonly used approaches. The results showed that our method compares favorably to others, and practically always achieves the lowest localization error. We also extended the proposed system using a model of cooperative positioning by combining the estimates obtained in the heterogeneous wireless network. The obtained results showed that with combined location estimate, significant improvement over any single system was achieved. [ABSTRACT FROM AUTHOR]