Search

Searching for objects is a common task in daily life and work. For augmented reality (AR) devices without spatial perception systems, the image of the object's last appearance serves as a common search assistance. Compared to using only images as visual cues, videos capturing the process of object placement can provide procedural guidance, potentially enhancing users' search efficiency. However, complete video playback capturing the entire object placement process as visual cues can be excessively lengthy, requiring users to invest significant viewing time. To explore whether segmented or accelerated video playback can still assist users in object retrieval tasks effectively, we conducted a user study. The results indicated that when video playback is covering the first appearance of the object's destination to the object's final appearance (referred to as the destination appearance, DA) and playing at normal speed, search time and cognitive load were significantly reduced. Subsequently, we designed a second user study to evaluate the performance of video playback compared to image cues in object retrieval tasks. The results showed that combining the DA playback starting point with images of the object's last appearance further reduced search time and cognitive load. [ABSTRACT FROM AUTHOR]

An important capability of most smart, Internet-of-Things-enabled spaces (e.g., office, home, hospital, factory) is the ability to leverage context of use. Location is a key context element, particularly indoor location. Recent advances in radio ranging technologies, such as Wi-Fi RTT, promise the availability of low-cost, near-ubiquitous time-of-flight-based ranging estimates. In this paper, we build on prior work to enhance this ranging technology's ability to provide useful location estimates. For further improvements, we model user motion behavior to estimate the user motion state by taking the temporal measurements available from time-of-flight ranging. We select the velocity parameter of a particle-filter-based on this motion state. We demonstrate meaningful improvements in coordinate-based estimation accuracy and substantial increases in room-level estimation accuracy. Furthermore, insights gained in our real-world deployment provide important implications for future Internet-of-Things applications and their supporting technology deployments such as social interaction, workflow management, inventory control, or healthcare information tools. [ABSTRACT FROM AUTHOR]

The technological diversity applied in practical examples entails the identification and the analysis of different implementation options, thus opening the way to a world of technology. The goal of this paper is to spot novel prospects for incorporating sensors and Big Data techniques for better property administration strategies, through an indoor localization and navigation architecture. The hereby analysis uses an approach that combines Radio Frequency Identification technology, performance efficient algorithms for calculating routes in very large datasets, methodologies for optimizing spatial representations and Internet of Things specific hardware distribution techniques to properly address challenging logistics issues and complex facilities management. The proposed solution is tailored for a large underground parking area in a commercial center, enabling easy car location identification, automatic parking time calculation and dynamically determined occupancy levels, along with descriptive statistics about busy time intervals or visiting patterns. [ABSTRACT FROM AUTHOR]

To achieve low-cost and robustness, an indoor location system using simple visual tags is designed by comprehensively considering accuracy and computation complexity. Only the color and shape features are used for tag detection, by which both algorithm complexity and data storage requirement are reduced. To manage the nonunique problem caused by the simple tag features, a fast query and matching method is further presented by using the view field of the camera and the tag azimuth. Then, based on the relationship analysis between the spatial distribution of tags and location error, a pose and position estimation method using the weighted least square algorithm is designed and works together with the interactive algorithm by the designed switching strategy. By using the techniques presented, a favorable balance is achieved between the algorithm complexity and the location accuracy. The simulation and experiment results show that the proposed method can manage the singular problem of the overdetermined equations effectively and attenuate the negative effect of unfavorable label groups. Compared with the ultrawide band technology, the location error is reduced by more than 62%. [ABSTRACT FROM AUTHOR]

As an indispensable type of information, location data are used in various industries. Ultrawideband (UWB) technology has been used for indoor location estimation due to its excellent ranging performance. However, the accuracy of the location estimation results is heavily affected by the deployment of base stations; in particular, the base station deployment space is limited in certain scenarios. In underground mines, base stations must be placed on the roof to ensure signal coverage, which is almost coplanar in nature. Existing indoor positioning solutions suffer from both difficulties in the correct convergence of results and poor positioning accuracy under coplanar base-station conditions. To correctly estimate position in coplanar base-station scenarios, this paper proposes a novel iterative method. Based on the Newton iteration method, a selection range for the initial value and iterative convergence control conditions were derived to improve the convergence performance of the algorithm. In this paper, we mathematically analyze the impact of the localization solution for coplanar base stations and derive the expression for the localization accuracy performance. The proposed method demonstrated a positioning accuracy of 5 cm in the experimental campaign for the comparative analysis, with the multi-epoch observation results being stable within 10 cm. Furthermore, it was found that, when base stations are coplanar, the test point accuracy can be improved by an average of 63.54% compared to the conventional positioning algorithm. In the base-station coplanar deployment scenario, the upper bound of the CDF convergence in the proposed method outperformed the conventional positioning algorithm by about 30%. [ABSTRACT FROM AUTHOR]

The positioning of indoor electronic devices is an essential part of human–computer interaction, and the accuracy of positioning affects the level of user experience. Most existing methods for RF-based device localization choose to ignore or remove the impact of multipath effects. However, exploiting the multipath effect caused by the complex indoor environment helps to improve the model's localization accuracy. In response to this question, this paper proposes a multipath-assisted localization (MAL) model based on millimeter-wave radar to achieve the localization of indoor electronic devices. The model fully considers the help of the multipath effect when describing the characteristics of the reflected signal and precisely locates the target position by using the MAL area formed by the reflected signal. At the same time, for the situation where the radar in the traditional Single-Input Single-Output (SISO) mode cannot obtain the 3D spatial position information of the target, the advantage of the MAL model is that the 3D information of the target can be obtained after the mining process of the multipath effect. Furthermore, based on the original hardware, it can achieve a breakthrough in angular resolution. Experiments show that our proposed MAL model enables the millimeter-wave multipath positioning model to achieve a 3D positioning error within 15 cm. [ABSTRACT FROM AUTHOR]

Indoor localization using fine time measurement (FTM) round-trip time (RTT) with respect to cooperating Wi-Fi access points (APs) has been shown to work well and provide 1–2 m accuracy in both 2D and 3D applications. This approach depends on APs implementing the IEEE 802.11-2016 (also known as IEEE 802.11mc) Wi-Fi standard ("two-sided" RTT). Unfortunately, the penetration of this Wi-Fi protocol has been slower than anticipated, perhaps because APs tend not to be upgraded as often as other kinds of electronics, in particular in large institutions—where they would be most useful. Recently, Google released Android 12, which also supports an alternative "one-sided" RTT method that will work with legacy APs as well. This method cannot subtract out the "turn-around" time of the signal, and so, produces distance estimates that have much larger offsets than those seen with two-sided RTT—and the results are somewhat less accurate. At the same time, this method makes possible distance measurements for many APs that previously could not be used. This increased accessibility can compensate for the decreased accuracy of individual measurements. We demonstrate here indoor localization using one-sided RTT with respect to legacy APs that do not support IEEE 802.11-2016. The accuracy achieved is 3–4 m in cluttered environments with few line-of-sight readings (and using only 20 MHz bandwidths). This is not as good as for two-sided RTT, where 1–2 m accuracy has been achieved (using 80 MHz bandwidths), but adequate for many applications A wider Wi-Fi channel bandwidth would increase the accuracy further. As before, Bayesian grid update is the preferred method for determining position and positional accuracy, but the observation model now is different from that for two-sided RTT. As with two-sided RTT, the probability of an RTT measurement below the true distance is very low, but, in the other direction, the range of measurements for a given distance can be much wider (up to well over twice the actual distance). We describe methods for formulating useful observation models. As with two-sided RTT, the offset or bias in distance measurements has to be subtracted from the reported measurements. One difference is that here, the offsets are large (typically in the 2400–2700 m range) because of the "turn-around time" of roughly 16 μs (i.e., about two orders of magnitude larger than the time of flight one is attempting to measure). We describe methods for estimating these offsets and for minimizing the effort required to do so when setting up an installation with many APs. [ABSTRACT FROM AUTHOR]

To solve the problem of poor accuracy of the current GPS technology of indoor positioning, 5G technology is combined with convolutional neural network algorithm to proposes an indoor positioning scheme based on 5G new radio（NR）parameters. By collecting 5G NR data, the fingerprint data is formed with the reference point number and stored in the fingerprint database. Taking the precision, recall and micro-score value as the evaluation index, the convolutional neural network algorithm was used to train the fingerprint database to obtain the location model, and the Adam method was used to optimize the model. The total data set used in this scheme is 2 400, of which the training set size is 2 160 and the test set size is 240. The positioning model was used for 1 000 training sessions, with 512 training data for each batch, and the indoor positioning effect with an average error of 1.33 m was finally achieved. [ABSTRACT FROM AUTHOR]

Reliable location estimation has been a key enabler of many applications in the UbiComp space. Much progress has been made on the development of accurate of indoor location systems, which form the foundation of many interesting applications, particularly in consumer scenarios. However, many location-based applications in enterprise settings also require addressing another facet of reliability: assurance. Without having strong guarantees of a location estimate's legitimacy, stakeholders must explicitly balance the advantages offered with the risks of falsification. In this space, there are two key threats: replay attacks, where signal and sensor information is collected in one location and replayed in another to falsify a location estimation later in time; and wormhole attacks, where signal and sensor information is forwarded to a remote location by a colluding device to falsify location estimation in real-time. In this work, we improve upon the state of the art in wormhole-resistant location estimation techniques. Specifically, we present the Location Anchor, which leverages a combination of technical solutions and social contracts to provide high-assurance proofs of device location that are resistant to wormhole attacks. Unlike existing work, the Location Anchor has minimal hardware costs, supports a rich tapestry of applications, and is compatible with commodity smartphone and tablet platforms. We show that the Location Anchor can extend existing replay-resistant location systems into wormhole-resistant location systems, even in the face of very aggressive attacker assumptions. We describe the protocols underlying the Location Anchor, as well as report on the efficacy of a prototype implementation. [ABSTRACT FROM AUTHOR]

Fingerprint-based indoor localization has become one of the most attractive and promising techniques; however, one primary concern for this technology to be fully practical is to maintain the fingerprint database to combat harsh indoor environmental dynamics, especially in the large-scale and long-term deployment. In this article, focusing on three key problems now existing in fingerprint database updating approaches such as the mechanism for triggering updates, the collection of new fingerprints and determination of fingerprints’ location information, we propose a fuzzy map mechanism and decision methods of neighbours’ fingerprints in response to all kinds of changes in indoor environments. Meanwhile, we design a static data collecting mechanism to filter reliable information from numerous users’ inputs and propose a neighbours’ fingerprint-assisted technique to calculate the location of fingerprints. Experimental results demonstrate that the proposed solution not only improves the performance of updating the fingerprint database in real time and robustness by 40% and 50%, respectively, but also reduces the update frequency and improves mean location accuracy by over 40%. [ABSTRACT FROM AUTHOR]

Traditional indoor location technologies such as infrared technology and ultrasonic technology are complex, expensive, or having unsatisfactory location accuracy. Radio frequency identification (RFID) technologies are very popular in many areas since their costs are very low. The tag in such technologies acts as the transmitter, and the radio signal strength indicator (RSSI) information is measured at the reader. However, RSSI information suffers strictly from the multipath circumstance and circumferential elements. Therefore, the localization accuracy of the boundary will be affected severely. In order to solve this problem, we introduce the boundary virtual reference label (BVIRE) algorithm to well utilize RFID techniques for locating the tracking object, which inserts some virtual reference tags on the boundary by establishing a linear regression model that eliminates unwanted tag information from the estimation procedure. The positioning accuracy of the boundary tags and stability have been improved significantly, at least 78%, without adding extra reference tags or radio frequency interference. Also, the estimation errors of our improved BVIRE are much smaller compared to the virtual reference label, location identification based on the dynamic active RFID calibration (LANDMARC), ultrawide band, RADAR, and PinPoint algorithms. [ABSTRACT FROM AUTHOR]

The combination of IoT and accurate location-finding technologies will enable a wide range of novel context-aware services to automate tasks and support everyday work practice. [ABSTRACT FROM AUTHOR]

The indoor location of individuals is a key contextual variable for commercial and assisted location-based services and applications. Commercial centers and medical buildings (e.g., hospitals) require location information of their users/patients to offer the services that are needed at the correct moment. Several approaches have been proposed to tackle this problem. In this paper, we present the development of an indoor location system which relies on the human activity recognition approach, using sound as an information source to infer the indoor location based on the contextual information of the activity that is realized at the moment. In this work, we analyze the sound information to estimate the location using the contextual information of the activity. A feature extraction approach to the sound signal is performed to feed a random forest algorithm in order to generate a model to estimate the location of the user. We evaluate the quality of the resulting model in terms of sensitivity and specificity for each location and we also perform out-of-bag error estimation. Our experiments were carried out in five representative residential homes. Each home had four individual indoor rooms. Eleven activities (brewing coffee, cooking, eggs, taking a shower, etc.) were performed to provide the contextual information. Experimental results show that developing an indoor location system (ILS) that uses contextual information from human activities (identified with data provided from the environmental sound) can achieve an estimation that is 95% correct. [ABSTRACT FROM AUTHOR]

Background: Typical radio frequency identification (RFID) access control system can be ineffective if an unauthorized person tailgates an authorized person through an access area.Objective: To propose a system by using indoor locating and tracking techniques address this problem, which is to prevent unauthorized Alzheimer's and dementia patients from getting lost including by tailgating.Methods: To achieve accurate target location, passive RFID deployment strategy is studied and a fingerprinting based passive RFID localization algorithm is proposed. The proposed system was evaluated in a building environment to simulate the performance of access control. RFID reader was installed on ceiling near the access area and tags were stitched on both shoulders of the experiment subject's garments.Results: The probability of the error distance within 0.3 m achieved 97% in the warning area; the location precision achieved 97% within 0.4 m in the monitoring area.Conclusions: The result showed that if an unauthorized person enters the restricted area, the system can initiate an alert signal accurately. Therefore, the proposed system is very suitable to be used in nursing home or hospital to prevent unauthorized personnel and assets entering/exiting a confined location. [ABSTRACT FROM AUTHOR]

This paper integrates UWB (ultra-wideband) and IMU (Inertial Measurement Unit) data to realize pedestrian positioning through a particle filter in a non-line-of-sight (NLOS) environment. After the acceleration and angular velocity are integrated by the ZUPT-based algorithm, the velocity and orientation of the feet are obtained, and then the velocity and orientation of the whole body are estimated by a virtual odometer method. This information will be adopted as the prior information for the particle filter, and the observation value of UWB will act as the basis for weight updating. According to experimental results, the prior information provided by an IMU can be used to restrain the observation error of UWB under an NLOS condition, and the positioning precision can be improved from the positioning error of 1.6 m obtained using the pure UWB-based algorithm to approximately 0.7 m. Moreover, with high computational efficiency, this algorithm can achieve real-time computing performance on ordinary embedded devices. [ABSTRACT FROM AUTHOR]

The increase in the number of large-scale events held indoors (i.e., conferences and business events) opens new opportunities for crowd monitoring and access controlling as a way to prevent risks and provide further information about the event's development. In addition, the availability of already connectable devices among attendees allows to perform non-intrusive positioning during the event, without the need of specific tracking devices. We present an algorithm for overcrowding detection based on passive Wi-Fi requests capture and a platform for event monitoring that integrates this algorithm. The platform offers access control management, attendees monitoring and the analysis and visualization of the captured information, using a scalable software architecture. In this paper, we evaluate the algorithm in two ways: First, we test its accuracy with data captured in a real event, and then we analyze the scalability of the code in a multi-core Apache Spark-based environment. The experiments show that the algorithm provides accurate results with the captured data, and that the code scales properly. [ABSTRACT FROM AUTHOR]

This paper presents statistical analysis of RSSI read-outs recorded in indoor environment. Many papers concerning indoor location, based on RSSI measurement, assume its normal probability density function (PDF). This is partially excused by relation to PDF of radio-receiver's noise and/or together with influence of AWGN (average white Gaussian noise) radio-channel - generally modelled by normal PDF. Unfortunately, commercial (usually unknown) methods of RSSI calculations, typically as 'side-effect' function of receiver's AGC (automatic gain control), results in PDF being from Gaussian PDF. This paper presents results of RSSI measurements in selected ISM bands: 433/868 MHz and 2.4/5 GHz. The measurements have been recorded using low-cost integrated RF modules (at 433/868 MHz and 2.4 GHz) and 802.11 WLAN access points (at 2.4/5 GHz). Then estimated PDF of collected data is shown and compared to normal (Gaussian) PDF. [ABSTRACT FROM AUTHOR]

The Open Geospatial Consortium (OGC) Geography Markup Language (GML) standard provides basic types and a framework for defining geo-informational data models such as CityGML and IndoorGML, which provide standard information models for 3D city modelling and lightweight indoor network navigation. Location information, which is the semantic engine that fuses big geo-information data, is however, discarded in these standards. The Chinese national standard of Indoor Multi-Dimensional Location GML (IndoorLocationGML) presented in this study can be used in ubiquitous indoor location intelligent applications for people and robots. IndoorLocationGML is intended as an indoor multi-dimensional location information model and exchange data format standard, mainly for indoor positioning and navigation. This paper introduces the standard's main features: (1) terminology; (2) indoor location information model using a Unified Modeling Language (UML) class diagram; (3) indoor location information markup language based on GML; and (4) use cases. A typical application of the standard is then discussed. This standard is applicable to the expression, storage, and distribution of indoor multi-dimensional location information, and to the seamless integration of indoor-outdoor location information. The reference and basis are therefore relevant to publishers, managers, users, and developers of indoor navigation and location-based services (LBS). [ABSTRACT FROM AUTHOR]

With the intense deployment of wireless systems and the widespread use of intelligent equipment, the requirement for indoor positioning services is increasing, and Wi-Fi fingerprinting has emerged as the most often used approach to identifying indoor target users. The construction time of the Wi-Fi received signal strength (RSS) fingerprint database is short, but the positioning performance is unstable and susceptible to noise. Meanwhile, to strengthen indoor positioning precision, a fingerprints algorithm based on a convolution neural network (CNN) is often used. However, the number of reference points participating in the location estimation has a great influence on the positioning accuracy. There is no standard for the number of reference points involved in position estimation by traditional methods. For the above problems, the grayscale images corresponding to RSS and angle of arrival are fused into RGB images to improve stability. This paper presents a position estimation method based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm, which can select appropriate reference points according to the situation. DBSCAN analyses the CNN output and can choose the number of reference points based on the situation. Finally, the position is approximated using the weighted k-nearest neighbors. The results show that the calculation error of our proposed method is at least 0.1–0.3 m less than that of the traditional method. [ABSTRACT FROM AUTHOR]

RFID technology has been widely used for object tracking in indoor environment due to their low cost and convenience for deployment. Existing RFID localization approaches rely on signal strength to measure the distance between RFID reader and tags. However, because of the environmental complexity and inferences, the measurement of distance by signal strength is not accurate, which causes large error in localization. In this paper we develop a novel algorithm to improve the RFID localization accuracy. Our algorithm is based on particle swarm optimization. More importantly, we add reference tags in the deployment, and design a parameter named Correction Factor in PSO to measure the distances more accurately by signal strength. The result shows that compared with the previous method without the correction factor, our proposed approach can increase the accuracy by 50%. This method has good application prospect in equipment management. [ABSTRACT FROM AUTHOR]

RFID technology has been widely used for object tracking in indoor environment due to their low cost and convenience for deployment. Existing RFID localization approaches rely on signal strength to measure the distance between RFID reader and tags. However, because of the environmental complexity and inferences, the measurement of distance by signal strength is not accurate, which causes large error in localization. In this paper we develop a novel algorithm to improve the RFID localization accuracy. Our algorithm is based on particle swarm optimization. More importantly, we add reference tags in the deployment, and design a parameter named Correction Factor in PSO to measure the distances more accurately by signal strength. The result shows that compared with the previous method without the correction factor, our proposed approach can increase the accuracy by 50%. This method has good application prospect in equipment management. [ABSTRACT FROM AUTHOR]

Indoor fingerprinting localization approaches estimate the location of a mobile object by matching observations of received signal strengths (RSS) from access points (APs) with fingerprint records. In real WLAN environments, there are more and more APs available, with interference between them, which increases the localization difficulty and computational consumption. To cope with this, a novel AP selection method, LocalReliefF-C(a novel method based on ReliefF and correlation coefficient), is proposed. Firstly, on each reference location, the positioning capability of APs is ranked by calculating classification weights. Then, redundant APs are removed via computing the correlations between APs. Finally, the set of best-discriminating APs of each reference location is obtained, which will be used as the input features when the location is estimated. Furthermore, an effective clustering method is adopted to group locations into clusters according to the common subsets of the best-discriminating APs of these locations. In the online stage, firstly, the sequence of RSS observations is collected to calculate the set of the best-discriminating APs on the given location, which is subsequently used to compare with cluster keys in order to determine the target cluster. Then, hidden naive Bayes (HNB) is introduced to estimate the target location, which depicts the real WLAN environment more accurately and takes into account the mutual interaction of the APs. The experiments are conducted in the School of Environmental Science and Spatial Informatics at the China University of Mining and Technology. The results validate the effectiveness of the proposed methods on improving localization accuracy and reducing the computational consumption. [ABSTRACT FROM AUTHOR]

This paper presents comparison of prototype location system built with standard components of 2.4 and 5 GHz WLAN network infrastructure. The system can be used for personal or other objects' positioning, both for indoor and outdoor environments. The system is local, i.e. its operational area is limited to WLAN network operating range. The system is based on standard and widely available WLAN components (access points, network adapters). The goal is to avoid any hardware and software modifications. Also position calculation should not be power hungry operation. Method of location is based in Received Signal Strength Indication (RSSI) returned by most of RF ICs (including WLAN). The main focus is research of how much accuracy (and usefulness) can be expected from standard WLAN hardware. Both static and dynamic scenarios have been tested and compared. [ABSTRACT FROM AUTHOR]

The increasing use of mobile devices in indoor spaces brings challenges to location methods. This work presents a hybrid intelligent method based on data mining and Type-2 fuzzy logic to locate mobile devices in an indoor space by zones using Wi-Fi signals from selected access points (APs). This approach takes advantage of wireless local area networks (WLANs) over other types of architectures and implements the complete method in a mobile application using the developed tools. Besides, the proposed approach is validated by experimental data obtained from case studies and the cross-validation technique. For the purpose of generating the fuzzy rules that conform to the Takagi-Sugeno fuzzy system structure, a semi-supervised data mining technique called subtractive clustering is used. This algorithm finds centers of clusters from the radius map given by the collected signals from APs. Measurements ofWi-Fi signals can be noisy due to several factors mentioned in this work, so this method proposed the use of Type-2 fuzzy logic for modeling and dealing with such uncertain information. [ABSTRACT FROM AUTHOR]

In this paper, we present the development of an infrastructure-less indoor location system (ILS), which relies on the use of a microphone, a magnetometer and a light sensor of a smartphone, all three of which are essentially passive sensors, relying on signals available practically in any building in the world, no matter how developed the region is. In our work, we merge the information from those sensors to estimate the user's location in an indoor environment. A multivariate model is applied to find the user's location, and we evaluate the quality of the resulting model in terms of sensitivity and specificity. Our experiments were carried out in an office environment during summer and winter, to take into account changes in light patterns, as well as changes in the Earth's magnetic field irregularities. The experimental results clearly show the benefits of using the information fusion of multiple sensors when contrasted with the use of a single source of information. [ABSTRACT FROM AUTHOR]

Given the indispensable role of mobile phones in everyday life, phone-centric sensing systems are ideal candidates for ubiquitous observation purposes. This paper presents a novel approach for mobile phone-centric observation applied to indoor location. The approach involves a location fingerprinting methodology that takes advantage of the presence of magnetic field anomalies inside buildings. Unlike existing work on the subject, which uses the intensity of magnetic field for fingerprinting, our approach uses all three components of the measured magnetic field vectors to improve accuracy. By using adequate soft computing techniques, it is possible to adequately balance the constraints of common solutions. The resulting system does not rely on any infrastructure devices and therefore is easy to manage and deploy. The proposed system consists of two phases: the offline phase and the online phase. In the offline phase, magnetic field measurements are taken throughout the building, and 3D maps are generated. Then, during the online phase, the user's location is estimated through the best estimator for each zone of the building. Experimental evaluations carried out in two different buildings confirm the satisfactory performance of indoor location based on magnetic field vectors. These evaluations provided an error of (11.34 m, 4.78 m) in the (x; y) components of the estimated positions in the first building where the experiments were carried out, with a standard deviation of (3.41 m, 4.68 m); and in the second building, an error of (4 m, 2.98 m) with a deviation of (2.64 m, 2.33 m). [ABSTRACT FROM AUTHOR]

With the popularity of WLAN (Wireless Local Area Networks) indoors, mobile devices can easily get real-time access to it, which provides us an unprecedented opportunity to understand individual behavior in everyday life. Recently, mining persons, point of interest and behaviors attracts more attentions. A WeChat recommendation system based on indoor WLAN localization is proposed, which uses users' historical information to obtain users' interest from the overload information. Existing location services usually aim for users, outdoor location data, lack analyzing indoor data through mining, and ignore an amount of semantic information in users, location data. The users, activities are traced by the indoor positioning technology. According to the shops which users visited and the products users saw, users, interest is estimated so as to recommend users for personalized products that may interest them. Based on the above work, we design a personalized product recommendation system based on indoor WLAN localization and the WeChat platform. [ABSTRACT FROM AUTHOR]

In order to reduce environmental impact on indoor wireless location, and improve location accuracy and expand location area, this paper presented a novel WiFi location method based on SVM. This method firstly divided the indoor environment into plurality of small areas, established the nonlinear relationship between RSSI and location, and then made use of the great classification and regression capability of SVM to locate target. Experimental results show that the precision of this method is 1.83 meter, so it can conclude that zoning and nonlinear regression are effective in controlling location error range and improving location accuracy. [ABSTRACT FROM AUTHOR]

Monitoring and tracking people at home usually requires high cost hardware installations, which implies they are not affordable in many situations. This study/paper proposes a monitoring and tracking system for people with medical problems. A virtual organization of agents based on the PANGEA platform, which allows the easy integration of different devices, was created for this study. In this case, a virtual organization was implemented to track and monitor patients carrying a Holter monitor. The system includes the hardware and software required to perform: ECG measurements, monitoring through accelerometers and WiFi networks. Furthermore, the use of interactive television can moderate interactivity with the user. The system makes it possible to merge the information and facilitates patient tracking efficiently with low cost. [ABSTRACT FROM AUTHOR]

Abstract: Nowadays, organizations include a large number of physical resources (e.g., meeting rooms, classrooms, and auditoriums) and computing ones (e.g., scanners, plotters, and handheld devices) distributed among different offices and buildings. Typically, these resources have to be shared among colleagues, because it is impossible for each collaborator to own private instances of all the different resources present in the organization. In this way, resource sharing gives collaborators the opportunity of not just lending their resources to other collaborators but also benefitting from the usage of resources they do not own. However, finding shared resources in a huge organization, without a proper technological support, can be a challenge for a member of staff and obviously really hard or even impossible for an external person. The main contribution of this paper is a service-oriented architecture called Resource Availability Management Services (RAMS), which is intended to facilitate the development of groupware applications that manage the availability and suitability of human, physical and computing resources. In the case of physical resources, the RAMS architecture allows determining the available resources closest to the requesting collaborator that satisfy his/her requirements and provides information about their physical location and the shortest path to reach them. To accomplish these goals, the proposed architecture relies on the services provided by three main components: (1) a Human Face Recognizer that allows identifying and locating collaborators in an organization, (2) an Ontology-based Matchmaker, which is able to determine a set of available and accessible resources that can satisfy a collaborator’s request, and (3) a Physical Resource Locator that relies on building topologies and the walking distance method to calculate high precision relative distances between collaborators and resources. [Copyright &y& Elsevier]

Abstract: In this paper we propose a flexible Multi-Agent Architecture together with a methodology for indoor location which allows us to locate any mobile station (MS) such as a Laptop, Smartphone, Tablet or a robotic system in an indoor environment using wireless technology. Our technology is complementary to the GPS location finder as it allows us to locate a mobile system in a specific room on a specific floor using the Wi-Fi networks. The idea is that any MS will have an agent known at a Fuzzy Location Software Agent (FLSA) with a minimum capacity processing at its disposal which collects the power received at different Access Points distributed around the floor and establish its location on a plan of the floor of the building. In order to do so it will have to communicate with the Fuzzy Location Manager Software Agent (FLMSA). The FLMSAs are local agents that form part of the management infrastructure of the Wi-Fi network of the Organization. The FLMSA implements a location estimation methodology divided into three phases (measurement, calibration and estimation) for locating mobile stations (MS). Our solution is a fingerprint-based positioning system that overcomes the problem of the relative effect of doors and walls on signal strength and is independent of the network device manufacturer. In the measurement phase, our system collects received signal strength indicator (RSSI) measurements from multiple access points. In the calibration phase, our system uses these measurements in a normalization process to create a radio map, a database of RSS patterns. Unlike traditional radio map-based methods, our methodology normalizes RSS measurements collected at different locations on a floor. In the third phase, we use Fuzzy Controllers to locate an MS on the plan of the floor of a building. Experimental results demonstrate the accuracy of the proposed method. From these results it is clear that the system is highly likely to be able to locate an MS in a room or adjacent room. [Copyright &y& Elsevier]

Nowadays society is demanding more and more smart healthcare services that allow monitoring patient status in a non-invasive way, anywhere and anytime. Thus, healthcare applications are currently facing important challenges guided by the u-health (ubiquitous health) and p-health (pervasive health) paradigms. New emerging technologies can be combined with other widely deployed ones to develop such next-generation healthcare systems. The main objective of this paper is to review and provide more details on the work presented in "LOBIN: E-Textile and Wireless-Sensor-Network-Based Platform for Healthcare Monitoring in Future Hospital Environments", published in the IEEE Transactions on Information Technology in Biomedicine, as well as to extend and update the comparison with other similar systems. As a result, the paper discusses the main advantages and disadvantages of using different architectures and communications technologies to develop wearable systems for pervasive healthcare applications. [ABSTRACT FROM AUTHOR]

Abstract: Due to the proliferation of ubiquitous computing services, locating a device in indoor scenarios has received special attention during recent years. A variety of algorithms are based on Wi-Fi measurements of the received signal strength and estimate the relation between this one and position using previous measurements at known locations. This problem naturally fits in well with learning algorithms such as neural networks, or support vector machines (SVM). However, existing machine learning techniques do not significantly outperform other simpler techniques, such as k-nn. This is mainly due to the fact that these solutions do not include significant a priori information. In this paper, we propose a technique to enhance these algorithms by including certain a priori information within the learning machine, using the spectral information of the training set, and a complex output to take advantage of the cross information in the two dimensions of the location. Specifically, we modify a SVM algorithm to obtain three advanced methods incorporating this information: one using an autocorrelation kernel, another using a complex output, and a third one combining both. These algorithms are compared to the k-nn and an SVM with Gaussian kernel, showing that including the a priori information improves the location performance. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a three-phase methodology (measurement, calibration and estimation) for locating mobile stations (MS) in an indoor environment using wireless technology. Our solution is a fingerprint-based positioning system that overcomes the problem of the relative effect of doors and walls on signal strength and is independent of network device manufacturers. In the measurement phase, our system collects received signal strength indicator (RSSI) measurements from multiple access points. In the calibration phase, our system utilizes these measurements in a normalization process to create a radio map, a database of RSS patterns. Unlike traditional radio map-based methods, our methodology normalizes RSS measurements collected at different locations (on a floor) and uses artificial neural network models (ANNs) to group them into clusters. In the third phase, we use data mining techniques (clustering) to optimize location results. Experimental results demonstrate the accuracy of the proposed method. From these results it is clear that the system is highly likely to be able to locate a MS in a room or nearby room. [Copyright &y& Elsevier]

Abstract: A human tracking system based on the integration of the measurements from an inertial motion capture system and a UWB (Ultra-Wide Band) location system has been developed. On the one hand, the rotational measurements from the inertial system are used to track precisely all limbs of the body of the human. On the other hand, the translational measurements from both systems are combined by three different fusion algorithms (a Kalman filter, a particle filter and a combination of both) in order to obtain a precise global localization of the human in the environment. Several experiments have been performed to compare their accuracy and computational efficiency. [Copyright &y& Elsevier]

In this paper, we present a two-way TOA technique for distance estimation (i.e., ranging) over IEEE 802.11 networks. It is based on repetitively measuring the Round Trip Time ( RTT) of IEEE 802.11 MAC frames and carrying out statistical processing in order to identify the multi-path condition of the channel and to improve accuracy. The study demonstrates that a multi-path condition has a negative impact on the ranging accuracy in Non-Dominant Direct Path (NDDP) and Undetectable Direct Path (UDP) situations. In this paper, an approach is presented to detect and overcome this impact. The proposed approach identifies the channel profile (i.e., the multi-path condition) from the statistical features of a set of RTT measurements and uses a ranging algorithm that is sensitive to multi-path. Results demonstrate the feasibility of channel profile identification in real time and a ranging accuracy of around one meter in all possible multi-path situations. [ABSTRACT FROM AUTHOR]

With the fast increase in the demand for location-based services and the proliferation of smartphones, the topic of indoor localization is attracting great interest. In indoor environments, users' performed activities carry useful semantic information. These activities can then be used by indoor localization systems to confirm users' current relative locations in a building. In this paper, we propose a deep-learning model based on a Convolutional Long Short-Term Memory (ConvLSTM) network to classify human activities within the indoor localization scenario using smartphone inertial sensor data. Results show that the proposed human activity recognition (HAR) model accurately identifies nine types of activities: not moving, walking, running, going up in an elevator, going down in an elevator, walking upstairs, walking downstairs, or going up and down a ramp. Moreover, predicted human activities were integrated within an existing indoor positioning system and evaluated in a multi-story building across several testing routes, with an average positioning error of 2.4 m. The results show that the inclusion of human activity information can reduce the overall localization error of the system and actively contribute to the better identification of floor transitions within a building. The conducted experiments demonstrated promising results and verified the effectiveness of using human activity-related information for indoor localization. [ABSTRACT FROM AUTHOR]

Firefighter's interventions under dense smoke and flames are hazardous and ideally need an efficient in-advance geo-located actuation plan. The existing communication and sensing technologies should be customized, optimized, and integrated to better know the conditions (flame locations, air condition) before and during the rescue team's interventions. In this paper, we propose a firefighter intervention architecture, which consists of several sensing devices (flame detectors, carbon dioxide air content) a navigation platform (an autonomous ground wheeled robot), and a communication/localization network (BLE IoT network) that can be used before and during an intervention in rescue or fire extinguishing missions even for indoor or confined spaces. The paper's key novelty presents our integrated solution, giving some key implementation details and an intensive experimentation campaign in two real firefighter scenarios with real controlled fires. Results carried out in these real indoor scenarios are presented to demonstrate the feasibility of the system. A fire detection system is proposed to improve fire focus in real time and moving in confined spaces with no visibility and physical references. The results obtained in the experimentation show the proposal's effectiveness in locating the fire focus's position and orientation reducing time and risk exposure. This kind of location-aware fire integrated systems would significantly impact the speed and security of first responder interventions. [ABSTRACT FROM AUTHOR]

This work makes multiple scientific contributions to the field of Indoor Localization for Ambient Assisted Living in Smart Homes. First, it presents a Big-Data driven methodology that studies the multimodal components of user interactions and analyzes the data from Bluetooth Low Energy (BLE) beacons and BLE scanners to detect a user's indoor location in a specific 'activity-based zone' during Activities of Daily Living. Second, it introduces a context independent approach that can interpret the accelerometer and gyroscope data from diverse behavioral patterns to detect the 'zone-based' indoor location of a user in any Internet of Things (IoT)-based environment. These two approaches achieved performance accuracies of 81.36% and 81.13%, respectively, when tested on a dataset. Third, it presents a methodology to detect the spatial coordinates of a user's indoor position that outperforms all similar works in this field, as per the associated root mean squared error—one of the performance evaluation metrics in ISO/IEC18305:2016—an international standard for testing Localization and Tracking Systems. Finally, it presents a comprehensive comparative study that includes Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression, to address the challenge of identifying the optimal machine learning approach for Indoor Localization. [ABSTRACT FROM AUTHOR]

A wide variety of business areas organize their work based on the location of their employees because only by taking these locations into account, they can schedule activities properly. However, in a large number of cases, the requirement of immediacy, such as the need to help an injured person in a hospital or to dry up water in a busy hallway to prevent people from slipping, is a major constraint. This work is based on a proof of concept in which we used Bluetooth Low Energy devices to track the location of each employee in an indoor environment. Among other factors, the location of each individual is assigned a large percentage of the weight to assign a task. This proposal is intended to cover some scenarios of great interest, guaranteeing the correctness of measurement and the privacy of staff tracking. [ABSTRACT FROM AUTHOR]

The IEEE 802.11mc WiFi standard provides a protocol for a cellphone to measure its distance from WiFi access points (APs). The position of the cellphone can then be estimated from the reported distances using known positions of the APs. There are several "multilateration" methods that work in relatively open environments. The problem is harder in a typical residence where signals pass through walls and floors. There, Bayesian cell update has shown particular promise. The Bayesian grid update method requires an "observation model" which gives the conditional probability of observing a reported distance given a known actual distance. The parameters of an observation model may be fitted using scattergrams of reported distances versus actual distance. We show here that the problem of fitting an observation model can be reduced from two dimensions to one. We further show that, perhaps surprisingly, a "double exponential" observation model fits real data well. Generating the test data involves knowing not only the positions of the APs but also that of the cellphone. Manual determination of positions can limit the scale of test data collection. We show here that "boot strapping," using results of a Bayesian grid update method as a proxy for the actual position, can provide an accurate observation model, and a good observation model can nearly double the accuracy of indoor positioning. Finally, indoors, reported distance measurements are biased to be mostly longer than the actual distances. An attempt is made here to detect this bias and compensate for it. [ABSTRACT FROM AUTHOR]

Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (i.e., wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user's location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set. [ABSTRACT FROM AUTHOR]

Determination of indoor position based on fine time measurement (FTM) of the round trip time (RTT) of a signal between an initiator (smartphone) and a responder (Wi-Fi access point) enables a number of applications. However, the accuracy currently attainable—standard deviations of 1–2 m in distance measurement under favorable circumstances—limits the range of possible applications. An emergency worker, for example, may not be able to unequivocally determine on which floor someone in need of help is in a multi-story building. The error in position depends on several factors, including the bandwidth of the RF signal, delay of the signal due to the high relative permittivity of construction materials, and the geometry-dependent "noise gain" of position determination. Errors in distance measurements have unusal properties that are exposed here. Improvements in accuracy depend on understanding all of these error sources. This paper introduces "frequency diversity," a method for doubling the accuracy of indoor position determination using weighted averages of measurements with uncorrelated errors obtained in different channels. The properties of this method are verified experimentally with a range of responders. Finally, different ways of using the distance measurements to determine indoor position are discussed and the Bayesian grid update method shown to be more useful than others, given the non-Gaussian nature of the measurement errors. [ABSTRACT FROM AUTHOR]

With the extensive development and utilization of urban underground space, coal mines, and other indoor areas, the indoor positioning technology of these areas has become a hot research topic. This paper proposes a robust localization method for indoor mobile platforms. Firstly, a series of coding graphics were designed for localizing the platform, and the spatial coordinates of these coding graphics were calculated by using a new method proposed in this paper. Secondly, two spatial resection models were constructed based on unit weight and Tukey weight to localize the platform in indoor environments. Lastly, the experimental results show that both models can calculate the position of the platform with good accuracy. The space resection model based on Tukey weight correctly identified the residuals of the observations for calculating the weights to obtain robust positioning results and has a high positioning accuracy. The navigation and positioning method proposed in this study has a high localization accuracy and can be potentially used in localizing practical indoor space mobile platforms. [ABSTRACT FROM AUTHOR]

In the indoor location field, the quality of received-signal-strength-indicator (RSSI) fingerprints plays a key role in the performance of indoor location services. However, changes in an indoor environment may lead to the decline of location accuracy. This paper presents a localization method employing a Hybrid Wireless fingerprint (HW-fingerprint) based on a convolutional neural network (CNN). In the proposed scheme, the Ratio fingerprint was constructed by calculating the ratio of different RSSIs from important contribution access points (APs). The HW-fingerprint combined the Ratio fingerprint and the RSSI to enhance the expression of indoor environment characteristics. Moreover, a CNN architecture was constructed to learn important features from the complex HW-fingerprint for indoor locations. In the experiment, the HW-fingerprint was tested in an actual indoor scene for 15 days. Results showed that the average daily location accuracy of the K-Nearest Neighbor (KNN), Support Vector Machines (SVMs), and CNN was improved by 3.39%, 8.03% and 9.03%, respectively, when using the HW-fingerprint. In addition, the deep-learning method was 4.19% and 16.37% higher than SVM and KNN in average daily location accuracy, respectively. [ABSTRACT FROM AUTHOR]

Due to its payload, size and computational limits, localizing a micro air vehicle (MAV) using only its onboard sensors in an indoor environment is a challenging problem in practice. This paper introduces an indoor localization approach that relies on only the inertial measurement unit (IMU) and four ultrasonic sensors. Specifically, a novel multi-ray ultrasonic sensor model is proposed to provide a rapid and accurate approximation of the complex beam pattern of the ultrasonic sensors. A fast algorithm for calculating the Jacobian matrix of the measurement function is presented, and then an extended Kalman filter (EKF) is used to fuse the information from the ultrasonic sensors and the IMU. A test based on a MaxSonar MB1222 sensor demonstrates the accuracy of the model, and a simulation and experiment based on the T h a l e s I I MAV platform are conducted. The results indicate good localization performance and robustness against measurement noises. [ABSTRACT FROM AUTHOR]