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LocAuth: A Fine-grained Indoor Location-based Authentication System using Wireless Networks Characteristics
Abstract
Location-based information has become an attractive attribute for use in many services including localization, tracking, positioning, and authentication. An additional layer of security can be obtained
by verifying the identity of users who wish to access confidential resources only within restricted,
small, indoor trusted zones. The objective of this paper is to construct highly secure indoor areas
primarily by detecting only legitimate users within their work cubicles. In this paper, we present a
fine-grained location-based authentication system (LocAuth) which ensures the physical presence of
the user within his/her small trusted zone (2 meters area). To do this, LocAuth exploits the ambient
wireless network characteristics (e.g., BSSID, SSID, and RSSI) of nearby Wi-Fi and Bluetooth devices observed from each trusted zone. We propose a novel technique called Top-Ranked Network
Nodes (TRNNs) to accurately overcome the fluctuations in wireless signals and enhance the ability
to distinguish targeted trusted zone from neighboring areas. In addition, we developed an application to implement LocAuth on Android-based smartphones and tested it in a real indoor environment.
The tested area is composed of seven adjacent and closely spaced work cubicles located in our lab.
We evaluated LocAuth in two ways: through RSSI-based nearest neighbors (RSSI-based NN) and
through supervised machine learning algorithm (Support Vector Machines). The results of the experiment show the effectiveness of LocAuth by achieving a high classification accuracy (above 98%).
This demonstrates its feasibility in terms of both accuracy as well as fine-grained classification.
... Meanwhile, high information intensity provides benefits for improvement in business skills. For example, a location-based service provides users with skills and capacities in real-time positioning, and links them to useful information, such as traffic conditions, routes, and weather, that involve their businesses [53,54]. Coleman et al. [55] indicated that local activity, restaurants, and transportation create the most requested information. ...
This paper examines the antecedents of cashless payment systems among businesses in Malaysia. The adoption of cashless payment systems by businesses has the potential to reduce the costs related to handling huge amounts of cash in the market and enhance transaction speed. Unfortunately, its current adoption in Malaysia is still small and very little is known about the factors. A seven-factor model based on the TOE framework was developed and tested. The partial least square (PLS) statistical approach was employed to analyze data collected from 200 business entities in Malaysia. The results reveal that compatibility and technology competence have higher significant relationships with the adoption of cashless payment systems. Management support, firm critical mass, competitive pressure, and information intensity are significantly related to the adoption of cashless payment systems, while firm size is not associated with it. The findings of this study provide significant practical implications for Malaysian stakeholders and technology vendors to recognize factors that affect a firm’s adoption of cashless payment systems to support business transactions. By investigating the phenomenon through the TOE framework, this study presents an integrated model of cashless payment systems by businesses. Our findings also offer guidance for future application of the PLS method in cashless payment and related research. The paper provides a more holistic understanding of the factors influencing cashless payment systems among businesses.
... Agadakos et al. [37] proposed the idea of determining the user's location using the Internet of Things (IoT) -by collecting reports of location information and proximity between user's phone and paired IoT devices owned by the user to act as an additional factor of authentication. Alawami et al. [38] investigated the possibility of classifying users' seats inside the same room (research lab) for the authentication purpose on their work cubicles using radio signals fingerprints. Fridman et al. [39] demonstrated that the physical location information could be unique to identify users for improving active and continuous authentication on mobile devices -using GPS (when outdoors) and Wi-Fi (when indoors). ...
A user’s location information can be used to identify the user. For example, in Android, we can keep our smartphone unlocked when it is located near a place that was previously registered as a trusted place. However, existing location-based user authentication solutions failed to support fine-grained indoor location registration. In this paper, we present a highly accurate identification scheme named LocID (Location-based IDentification) using the smartphone’s physical fingerprints that are sensed at the user’s secure area. Our key idea is to use Wi-Fi signals and the light intensity of smartphones’ fingerprints to accurately identify their locations within adjacent indoor areas. To generate stable and reliable location fingerprints, we develop a novel mathematical location-wise signal signature (LSS) algorithm by minimizing the bias highly affected by various environmental factors. To show the feasibility of LocID, we evaluated the performance using machine learning classifiers with a real-world dataset containing Wi-Fi and light fingerprints from 68 locations in a building for five weeks. The experimental results show LocID achieved an F1-score of 98.3%, false-negative rate (FNR) of 2.9%, and false-positive rate (FPR) of 1.7%, respectively, when LocID was retrained daily. Moreover, LocID achieved an F1-score of 94.4%, FNR of 2.7%, and FPR of 3.7%, respectively, when LocID was trained using the dataset during the first week and tested with the remaining four weeks dataset. Also, we tested LocID using only one-scan of location fingerprints and achieved 90.6% and 90.8% AUC scores for SVM and KNN classifiers, respectively.
We explore a method to fingerprint a location in terms of its measurable environment to create an authentication factor that is nonintrusive in the sense that a user is not required to engage in the authentication process actively. Exemplary, we describe the measurable environment by beacon frames from the WiFi access points in the user’s proximity. To use the measurable environment for authentication, measurements must be sufficiently discriminating between locations and similar at the same location. An authentication factor built from the measurable environment allows us to describe a user’s location in terms of measurable signals. Describing a location in terms of its measurable signals implies that we do not require an actual geographical mapping of the user’s location; comparing the measured signals is sufficient to create a location-based authentication factor. Only recognizing an earlier observed environment distinguishes our approach from other location-based authentication factors. We elaborate on using signals in the user’s environment in the background without user involvement to create a privacy-preserving but nonintrusive authentication factor suitable for integration into existing multi-factor authentication schemes.
This paper proposes a new privacy-enhancing, context-aware user authentication system, ConSec, which uses a transformation of general location-sensitive data, such as GPS location, barometric altitude and noise levels, collected from the user's device, into a representation based on locality-sensitive hashing (LSH). The resulting hashes provide a dimensionality reduction of the underlying data, which we leverage to model users' behaviour for authentication using machine learning. We present how ConSec supports learning from categorical and numerical data, while addressing a number of on-device and network-based threats. ConSec is implemented subsequently for the Android platform and evaluated using data collected from 35 users, which is followed by a security and privacy analysis. We demonstrate that LSH presents a useful approach for context authentication from location-sensitive data without directly utilising plain measurements.
In this paper, we propose a novel indoor passive localization approach called eigenspace-based DOA with direct-path recognition (ES-DPR), based on a DOA estimation algorithm with multiple omnidirectional antennas deployed in a uniform linear array (ULA). To address the multipath propagation interference problem in the indoor environments, we utilize the azimuth and RSS estimation results, which are calculated by using the eigenspace-based DOA (ES-DOA) algorithm, in a novel style. A direct-path bearing recognition algorithm is introduced to identify the real DOA of the signal source in different indoor environments, by combining the azimuth and RSS estimation with ensemble learning methods. Numerical simulations are conducted to verify the validity and superiority of the proposed method. The results show that the proposed ES-DPR method can achieve high resolution and has strong anti-noise capability in dealing with the multipath signals, and the direct-path recognition algorithm is reliable and robust in different indoor environments, even in undetectable direct-path conditions.
With the growing development of smartphones equipped with Wi-Fi technology and the need of inexpensive indoor location systems, many researchers are focusing their efforts on the development of Wi-Fi-based indoor localization methods. However, due to the difficulties in characterizing the Wi-Fi radio signal propagation in such environments, the development of universal indoor localization mechanisms is still an open issue. In this paper, we focus on the calibration of Wi-Fi-based indoor tracking systems to be used by smartphones. The primary goal is to build an accurate and robust Wi-Fi signal propagation representation in indoor scenarios.We analyze the suitability of our approach in a smartphone-based indoor tracking system by introducing a novel in-motion calibration methodology using three different signal propagation characterizations supplemented with a particle filter. We compare the results obtained with each one of the three characterization in-motion calibration methodologies and those obtained using a static calibration approach, in a real-world scenario. Based on our experimental results, we show that the use of an in-motion calibration mechanism considerably improves the tracking accuracy.
This paper presents the development and implementation of a location‐based, lightweight peer‐assisted authentication scheme for use in wireless networks. The notion of peer‐assisted authentication is based upon some target User Equipment (UE) seeking authentication and access to a network based upon its physical location. The target UE seeks authentication through the UE of peers in the same network. Compared with previous work, the approach in this paper does not rely on any cryptographic proofs from a central authentication infrastructure, thus avoiding complex infrastructure management. However, the peer‐assisted authentication consumes network channel resources which will impact on network performance. In this paper, we also present an access control algorithm for balancing the location authentication, network Quality of Service (QoS), network capacity and time delay. The results demonstrate that peer‐ assisted authentication considering location authentication and system QoS through dynamic access control strategies can be effectively and efficiently implemented in a number of use cases.
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Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on Wireless Local Area Networks (WLAN), highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in Wireless Sensor Networks (WSN) and are nowadays gaining attention for several envisioned Internet of Things (IoT) applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor Simultaneous Localization and Mapping (SLAM) approaches. The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.
Personal mobile devices currently have access to a significant portion of their user’s private sensitive data and are increasingly used for processing mobile payments. Consequently, securing access to these mobile devices is a requirement for securing access to the sensitive data and potentially costly services. Face authentication is one of the promising biometrics-based user authentication mechanisms that has been widely available in this era of mobile computing. With a built-in camera capability on smartphones, tablets, and laptops, face authentication provides an attractive alternative of legacy passwords for its memory-less authentication process, which is so sophisticated that it can unlock the device faster than a fingerprint. Nevertheless, face authentication in the context of smartphones has proven to be vulnerable to attacks. In most current implementations, a sufficiently high-resolution face image displayed on another mobile device will be enough to circumvent security measures and bypass the authentication process. In order to prevent such bypass attacks, gesture recognition together with location is proposed to be additionally modeled. Gestures provide a faster and more convenient method of authentication compared to a complex password. The focus of this paper is to build a secure authentication system with face, location and gesture recognition as components. User gestures and location data are a sequence of time series; therefore, in this paper we propose to use unsupervised learning in the long short-term memory recurrent neural network to actively learn to recognize, group and discriminate user gestures and location. Moreover, a clustering-based technique is also implemented for recognizing gestures and location.
Visible Light Communication (VLC) is a blooming research area which uses visible light spectrum as the communication channel. Exponential growth of Light Emitting Diodes (LEDs) deployment as a light source is encouraged to use for communication purposes as well. With the introduction of VLC, LED can be used for communication while providing the primary function of illumination. Since location-based network access protocols have not been implemented for Wi-Fi devices, this research has been conducted with the intention of using VLC to perform location-based authentication. In our location authentication protocol, VLC technology is used for authentication purpose and Wi-Fi is used for data transfer once the device has been authenticated. The proposed protocol provides location-based connectivity to Wi-Fi devices with minimum changes to Remote Authentication Dial-In User Service (RADIUS) and Wi-Fi Protected Access 2 (WPA2) protocol by using VLC.
Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques such as Angle of Arrival (AoA), Time of Flight (ToF), Return Time of Flight (RTOF), Received Signal Strength (RSS); based on technologies such as WiFi, Radio Frequency Identification Device (RFID), Ultra Wideband (UWB), Bluetooth and systems that have been proposed in the literature. The paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.
Wireless optical communication is one of the most upcoming areas in field of communication. It is classified into two types-outdoor and indoor wireless communication. Indoor wireless communication is called as visible light communication and tries to combine illumination with communication. The paper provides a detailed survey about the evolution of visible light communication technology and open research challenges faced while implementing visible light communication.
Knowledge of deployed transmitters’ (Tx) locations in a wireless network improves many aspects of network management. Operators and building administrators are interested in locating unknown Txs for optimizing new Tx placement, detecting and removing unauthorized Txs, selecting the nearest Tx to offload traffic onto it, and constructing radio maps for indoor and outdoor navigation. This survey provides a comprehensive review of existing algorithms that estimate the location of a wireless Tx given a set of observations with the received signal strength indication. Algorithms that require the observations to be location-tagged are suitable for outdoor mapping or small-scale indoor mapping, while algorithms that allow most observations to be unlocated trade off some accuracy to enable large-scale crowdsourcing. This article presents empirical evaluation of the algorithms using numerical simulations and real-world Bluetooth Low Energy data.
Current WiFi positioning systems (WPS) require databases, such as locations of WiFi access points and propagation parameters, or a radio map - to assist with positioning. Typically, procedures for building such databases are time-consuming and labour-intensive. In this paper, two autonomous crowdsourcing systems are proposed to build the databases on handheld devices by using our designed algorithms and an inertial navigation solution from a Trusted Portable Navigator (T-PN). The proposed systems, running on smartphones, build and update the database autonomously and adaptively to account for the dynamic environment. To evaluate the performance of an automatically generated database, two improved WiFi positioning schemes (fingerprinting and trilateration) corresponding to these two database building systems, are also discussed. The main contribution of the paper is the proposal of two crowdsourcing-based WPSs that eliminate the various limitations of current crowdsourcing-based systems which (a) require a floor plan or GPS, (b) are suitable only for specific indoor environments, and (c) implement a simple MEMS-based sensors solution. In addition, these two WPSs are evaluated and compared through field tests. Results in different test scenarios show that average positioning errors of both proposed systems are all less than 5.75 m.
This paper presents an improvement for GPS-based indoor-outdoor detection with moving direction information. First, we considered several features based on raw GPS satellite informationas the baseline. From the evaluation with GPS data from 19 persons, the SVM-based classifier using a combination of elevation and S/N ratio achieved over 96% accuracy for the simple 'clear' situation. Then we introduced direction information from a compass sensor to increase the detection robustness in the canyon of buildings. The second experimentation result showed the proposed method considering direction information kept almost the same accuracy of indoor-outdoor detectionin a 'canyon' situation, whereas the baseline classifier worsened accuracy by 10%.
The article presents an easy to implement approach for indoor localization and navigation that combines Bayesian filtering with support vector machine classifiers to associate high-dimensionality cellular telephone network received signal strength fingerprints to distinct spatial regions. The technique employs a “space sampling” and a “time sampling” scheme in the training procedure, and the Bayesian filter allows introducing a priori information on room layout and target trajectories, resulting in robust room-level indoor localization.
With the fast growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted a lot of interest due to its high accuracy. In this paper, we present a novel deep learning based indoor fingerprinting system using Channel State Information (CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi system architecture includes an off-line training phase and an on-line localization phase. In the off-line training phase, deep learning is utilized to train all the weights as fingerprints. Moreover, a greedy learning algorithm is used to train all the weights layer-by-layer to reduce complexity. In the on-line localization phase, we use a probabilistic method based on the radial basis function to obtain the estimated location. Experimental results are presented to confirm that DeepFi can effectively reduce location error compared with three existing methods in two representative indoor environments.
Smartphones with an embedded GPS sensor are being increasingly used for location determination to enable Location based services (LBS) deliver location context pervasive computing services such as maps and navigation. Although a Smartphone GPS provides adequate accuracy, it has limitations such as high energy consumption and is unavailable in locations with an obscured view of GPS satellites. Use of alternate location sensors such as Wi-Fi and GSM can be used to augment GPS and to alleviate these GPS limitations, but they can increase the average localization error. The novelty of our contribution is twofold. First we present an accelerometer based architecture that reduces GPS energy-consumption without compromising on either the location accuracy or sampling rate. Evaluation of our system shows energy-savings of up to 27% in typical circumstances. Second, as a user's mobility state is complex we also propose a method to not only detect that a user is non-stationary but also classify a representative set of mobility states.
We present the design and implementation of the Horus WLAN location determination system. The design of the Horus system aims at satisfying two goals: high accuracy and low computational requirements. The Horus system identifies different causes for the wireless channel variations and addresses them to achieve its high accuracy. It uses location-clustering techniques to reduce the computational requirements of the algorithm. The lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.We discuss the different components of the Horus system and its implementation under two different operating systems and evaluate the performance of the Horus system on two testbeds. Our results show that the Horus system achieves its goal. It has an error of less than 0.6 meter on the average and its computational requirements are more than an order of magnitude better than other WLAN location determination systems. Moreover, the techniques developed in the context of the Horus system are general and can be applied to other WLAN location determination systems to enhance their accuracy. We also report lessons learned from experimenting with the Horus system and provide directions for future work.
Indoor localization has attracted more and more attention because of its importance in many applications. One of the most popular techniques for indoor localization is the received signal strength indicator (RSSI) based fingerprinting approach. Since RSSI values are very complicated and noisy, conventional machine learning algorithms often suffer from limited performance. Recently developed deep learning algorithms have been shown to be powerful for the analysis of complex data. In this paper, we propose a local feature-based deep long short-term memory (LF-DLSTM) approach for WiFi fingerprinting indoor localization. The local feature extractor attempts to reduce the noise effect and extract robust local features. The DLSTM network is able to encode temporal dependencies and learn high-level representations for the extracted sequential local features. Real experiments have been conducted in two different environments, i.e., a research lab and an office. We also compare the proposed approach with some state-of-the-art methods for indoor localization. The results show that the proposed approach achieves the best localization performance with mean localization errors of 1.48 and 1.75 m under the research lab and office environments, respectively. The improvements of our proposed approach over the state-of-the-art methods range from 18.98% to 53.46%.
The demand for a ubiquitous and accurate indoor localization service is continuously growing. Despite the pervasive nature of cellular-based solutions, their localization quality depends on the number of cell towers provided by the phone. According to the standard, any cell phone can receive signal strength information from up to seven cell towers. However, the majority of cell phones usually return only the associated cell tower information, significantly limiting the amount of information available to the location determination algorithm. In this paper, we present SoloCell: a novel deep learning-based indoor localization system that utilizes the signal strength history from only the associated cell tower to achieve a fine-grained localization. SoloCell incorporates different modules that lessen the data collection effort and improve the deep model's robustness against noise. Evaluation using different Android phones shows that SoloCell can track the user with a median localization error of 0.95m This accuracy demonstrates the superiority of SoloCell compared to the state-of-the-art systems by at least 210%.
This paper proposes a novel indoor localization scheme to jointly track a mobile device (MD) and update an inaccurate floor plan map using the time-of-arrival measured at multiple reference devices (RDs). By modeling the floor plan map as a collection of map features, the map and MD position can be jointly estimated via a multi-RD single-cluster probability hypothesis density (MSC-PHD) filter. Conventional MSC-PHD filters assume that each map feature generates at most one measurement for each RD. If single reflections of the detected signal are considered as measurements generated by map features, then higher-order reflections, which also carry information on the MD and map features, must be treated as clutter. The proposed scheme incorporates multiple reflections by treating them as virtual single reflections reflected from inaccurate map features and traces them to the corresponding virtual RDs (VRDs), referred to as a multi-reflection-incorporating MSC-PHD (MRMSC-PHD) filter. The complexity of using multiple reflection paths arises from the inaccuracy of the VRD location due to inaccuracy in the map features. Numerical results show that these multiple reflection paths can be modeled statistically as a Gaussian distribution. A computationally tractable implementation combining a new greedy partitioning scheme and a particle-Gaussian mixture filter is presented. A novel mapping error metric is then proposed to evaluate the estimated map?s accuracy for plane surfaces. Simulation and experimental results show that our proposed MRMSC-PHD filter outperforms existing MSC-PHD filters by up to 95% in terms of average localization and by up to 90% in terms of mapping accuracy.
In the world today, various ways of positioning alongside location awareness have been developed. They play an important role towards improving the performance of localization system based technologies for indoor and outdoor conditions, which enables the user to clarify a problem, to develop a system that supports various users' needs and assist in their requirements. The outdoor positioning provides an accurate estimation of a location through the use of the global positioning system. However, it cannot be used indoors due to the inherent weakness in the signal. Therefore, the indoor positioning systems require an alternative solution to improve its performance. The integration of indoor and outdoor positioning systems seamlessly is significant towards bridging different systems without obstacle, and helps to support the user with ease. This paper examines previous literature works with regards to different locations and positioning that have enabled integration of indoor and outdoor environments seamlessly. The paper will seek to analyze the historical works in terms of their respective research methods, results and proposals. The associated challenges for the system are included in this paper as well, as a means to enable continuous improvement of the system in a real environment. Through the use of sensor technologies, the system can be utilized to improve daily activities by improving the complexity of the positioning technology.
The emergent context-aware applications in ubiquitous computing demands for obtaining accurate location information of humans or objects in real-time. Indoor location-based services can be delivered through implementing different types of technology, among which is a recent approach that utilizes LED lighting as a medium for Visible Light Communication (VLC). The ongoing development of solid-state lighting (SSL) is resulting in the wide increase of using LED lights and thereby building the ground for a ubiquitous wireless communication network from lighting systems. Considering the recent advances in implementing Visible Light Positioning (VLP) systems, this article presents a review of VLP systems and focuses on the performance evaluation of experimental achievements on location sensing through LED lights. We have outlined the performance evaluation of different prototypes by introducing new performance metrics, their underlying principles, and their notable findings. Furthermore, the study synthesizes the fundamental characteristics of VLC-based positioning systems that need to be considered, presents several technology gaps based on the current state-of-the-art for future research endeavors, and summarizes our lessons learned towards the standardization of the performance evaluation.
A GPS is usually installed at the center of a vehicle because with this configuration its data become the position of the vehicle without complex calibration and transformation. In this paper, however, we verify that the off-center arrangement of the GPS improves the position and orientation accuracy theoretically and experimentally. The extended Kalman filter (EKF) formulation and its observability and uncertainty analysis present that the off-centered GPS is better observable and less uncertain. Moreover, our experiments with synthetic data and real sensor dataset confirm the improvement of localization accuracy. The synthetic data generated in various situations reveal the important properties of localization with the off-centered GPS. The public outdoor dataset with real GPS data also supports the advantage of the off-centered GPS.
Wireless Big Data has aroused extensive attention, as mass mobile devices have been developed and deployed for the upcoming 5G era. The context information of these devices is of importance for personalized services in a smart environment. Nevertheless, the constant change of scenes challenges to the network operator. In this paper, we propose an ensemble learning scheme for indoor-outdoor classification for a typical urban area, based on the cellular data captured in a commercial LTE network. The variables are extracted by network key performance indicators (KPIs) and radio propagation knowledge. Based on these main variables, the decision trees grow and split by the Gini index of sampled features. Then, all decision trees are assembled as weak learners to built the ensemble scheme, thus improving the discrimination ability. The self-validation results show the ensemble model achieves extreme accurate (with an out-of-bag error lower than 1%) classification for indoor and outdoor environments. Moreover, the prominent variables are selected based on the variable importance of in the initial training. The reconfigured model based on fewer variables and less weak learners also gains the highest accuracy and relative short compute time, compared with other classical machine learning methods.
Millimeter wave (mmWave) location systems not only provide accurate positioning for location-based services, but can also help optimize network operations, for example through location-driven beam steering and access point association. In this paper, we design and evaluate localization schemes that exploit the characteristics of mmWave communication systems. We propose two range-free algorithms belonging to the broad classes of triangulation and angle difference-of-arrival. The schemes work both with multiple anchors and with as few as a single anchor, under the only assumption that the floor plan and the positions of the mmWave access points are known. Moreover, they are designed to be lightweight, so that even computationally-constrained devices can run them. We evaluate our proposed algorithms against two benchmark approaches based on fingerprinting and angles of arrival, respectively. Our results, obtained both by means of simulations and through measurements involving commercial 60-GHz mmWave devices, show that sub-meter accuracy is achieved in most of the cases, even in the presence of only a single access point. The availability of multiple access points substantially improves the localization accuracy, especially for large indoor spaces.
Human identification and monitoring are critical in many applications, such as surveillance, evacuation planning. Human identification and monitoring are not an easy task in the case of a large and densely populated crowd. However, none of the existing solutions consider seamless localization, identification, and tracking of the crowd for surveillance in both indoor and outdoor environments with significant accuracy. In this paper, we propose a novel and real-time surveillance system (named, SmartISS) which identifies, tracks and monitors individuals' wireless equipment(s) using their MAC ids. Our trackers/sensing units (PSUs) are the portable entities comprising of Smartphone/Jetson-TK1/PC which are enough to capture users' devices probe requests and locations without users' active cooperation. PSUs upload collected traces on the cloud server periodically where cloud server keeps finding the suspicious person(s). To retrieve the updated information, we propose an algorithm (named, LLTR) to select the optimal number of PSUs for finding the latest location(s) of the suspicious person(s). To validate and to show the usability of SmartISS, we develop a real prototype testbed and evaluate it extensively on a real-world dataset of 117,121 traces collected during the technical festival held at IIT Roorkee, India. SmartISS selects PSUs with an average selection accuracy of 95.3%.
Smartphones have become an indispensable tool in the work and life of most people. Given the many kinds of sensors built into smartphones, solving indoor navigation problems using these phones has become feasible. This paper proposes such a solution, using a particle-filter-based indoor positioning method. This method improves the positioning accuracy and helps improve the user experience. The major features of the method are that it leverages and fuses the advantages of two different indoor positioning methods [pedestrian dead reckoning (PDR) and received-signal-strength-indicator-based indoor positioning], and considers the influence on positioning of the indoor spatial map. The smartphone built-in sensors are used as data sources and constitute a feasible method for PDR. The iBeacon devices are also used--because of the many advantages, they present in indoor environments, such as low energy consumption, convenient deployment, and popular applications--and their received signal strength indicator values are used as observations in the particle filter. The devices used in this paper are conventional, without any special equipment, and the experimental sites are common work environments. Therefore, we believe that the proposed method has a wide application potential for indoor navigation applications (e.g., museum guides), and is especially suitable for working with indoor navigation maps.
Expansion of wireless body area networks applications, such as health-care, m-banking, and others has lead to vulnerability of privacy and personal data. An effective and unobtrusive natural method of authentication is therefore a necessity in such applications. Accelerometer-based gait recognition has become an attractive solution, however, continuous sampling of accelerometer data reduces the battery life of wearables. This paper investigates the usage of received signal strength indicator (RSSI) as a source of gait recognition. Unlike the accelerometer-based method, the RSSI approach does not require additional sensors (hardware) or sampling of them, but uses the RSSI values already available in all radio devices. Three radio channel features namely, the time series, auto-correlation function, and level crossing rate were extracted from unique signature of the RSSI in relation to the corresponding subject. The extracted features were then used together with four different classification learners, namely decision tree, support vector machine,
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-nearest neighbors, and artificial neural network, to evaluate the method. The best performance was achieved utilizing artificial neural network with 95% accuracy when the features were extracted from one on-body radio channel (right wrist to waist), and 98% when the features were extracted from two on-body radio channels (right wrist to waist, and left wrist to waist). The developed RSSI-based gait authentication approach can complement high-level authentication methods for increased privacy and security, without additional hardware, or high energy consumption existing in accelerometer-based solutions.
Indoor/outdoor localization, tracking and positioning applications are developed using the GPS receivers, ultrasound, infrared and RF (Wi-Fi and cellular) signals. The key point of such upper layer applications is to detect precisely whether a user is indoor or outdoor. This detection is crucial to improve the performance drastically through making a clever decision whether it is suitable to turn ON/OFF the sensors. Due to this, an unrealistic assumption is posed by the applications that the testbed environment type (indoor or outdoor) must be pre-known. In this paper, we present a realistic and ubiquitous (SenseIO) system which provides not only binary indoor/outdoor, but also a fine-grained detection (i.e., Rural, Urban, Indoor and Complex places). Without any prior knowledge, SenseIO leverages the measurements of sensor-rich smartphones (e.g., cellular, Wi-Fi, accelerometer, proximity, light and time-clock) to infer automatically the ambient environment type. A novel SenseIO multi-model system consists of four modules: (1) single serving cell tower, (2)Wi-Fi based, (3) activity recognition, and (4) light intensity. In addition, to achieve realism and ubiquity goals, we develop a SenseIO framework which includes three scenarios (A, B, C). We implement SenseIO on android-based smartphones and test it through multi-path tracing in real I/O environments. Our experiments for each individual module and all framework scenarios show that the SenseIO provides promising detection accuracy (above 92%) and outperforms existing indoor-outdoor techniques in terms of both accuracy and fine-grained detection.
The signals available for navigation depend on the environment. To operate reliably in a wide range of different environments, a navigation system is required to adopt different techniques based on the environmental contexts. In this paper, an environmental context detection framework is proposed, building the foundation of a context adaptive navigation system. Different land environments are categorized into indoor, urban, and open-sky environments based on how Global Navigation Satellite System (GNSS) positioning performs in these environments. Indoor and outdoor environments are first detected based on the availability and strength of GNSS signals using a hidden Markov model. Then the further classification of outdoor environments into urban and open-sky is investigated. Pseudorange residuals are extracted from raw GNSS measurements in a smartphone and used for classification in a fuzzy inference system alongside the signal strength data. Practical test results under different kinds of environments demonstrate an overall 88.2 percent detection accuracy. Copyright © 2018 Institute of Navigation
Indoor positioning based on Received Signal Strength Indicator (RSSI) of WLAN has received more and more attention because of low cost and easy implementation. However, traditional localization algorithms often fail to achieve better positioning results because of multi-path effect and shadow effect etc. In order to solve the problem of multi-collinearity and more noise in WLAN indoor location data, this paper presents a novel nonlinear Partial Least Square (PLS) method to address the problem of low precision in WLAN location. The proposed method integrates an inner Relevant Vector Machine (RVM) function with an external linear PLS framework. Firstly, the localization area is divided into a number of small areas by K-means algorithm. Then PLS is applied to extract the features of the fingerprint database to reduce the number of the variable dimensions and eliminate the correlations. The obtained score matrices are used as the input and output of RVM. Finally the coordinates of test points are regressed and predicted by the RVM-PLS algorithm. Simulation and experiments in real scenario prove the effectiveness of the proposed method. Compared with SVM-PLS, RBF-PLS, SVM-PCA, EBQPLS, PLS, SVM, RBF, RVM, and WKNN algorithm, the experimental results show that the proposed algorithm has higher positioning accuracy.
User location information provides additional layer of security to a system. The location information of a user is an important attribute that can be used in authentication systems. Legitimate user has to be physically resides within a restricted area to gain access. With the growth of wireless communication technologies that use radio waves, determination of user location for indoor environment is very challenging. Radio signal can penetrate walls; thus it is not easy to verify location information within a restricted area as small as a room. In this paper, we propose a location-based authentication system that is cost-effective and user friendly. The proposed system uses common infrastructure such as LED lightbulb and smartphone. To verify the location of the user, visible light communication technology via the LED lightbulb is used.
User location can act as an additional factor of authentication in scenarios where physical presence is required, such as when making in-person purchases or unlocking a vehicle. This paper proposes a novel approach for estimating user location and modeling user movement using the Internet of Things (IoT). Our goal is to utilize its scale and diversity to estimate location more robustly, than solutions based on smartphones alone, and stop adversaries from using compromised user credentials (e.g., stolen keys, passwords, etc.), when sufficient evidence physically locates them elsewhere. To locate users, we leverage the increasing number of IoT devices carried and used by them and the smart environments that observe these devices. We also exploit the ability of many IoT devices to "sense" the user. To demonstrate our approach, we build a system, called Icelus. Our experiments with it show that it exhibits a smaller false-rejection rate than smartphone-based location-based authentication (LBA) and it rejects attackers with few errors (i.e., false acceptances).
Wireless Local Area Network (WLAN) has become a promising choice for indoor positioning as the only existing and established infrastructure, to localize the mobile and stationary users indoors. However, since WLAN has been initially designed for wireless networking and not positioning, the localization task based on WLAN signals has several challenges. Amongst the WLAN positioning methods, WLAN fingerprinting localization has recently achieved great attention due to its promising results. WLAN fingerprinting faces several challenges and hence, in this paper, our goal is to overview these challenges and the state-of-the-art solutions. This paper consists of three main parts: 1) Conventional localization schemes; 2) State-of-the-art approaches; 3) Practical deployment challenges. Since all the proposed methods in WLAN literature have been conducted and tested in different settings, the reported results are not equally comparable. So, we compare some of the main localization schemes in a single real environment and assess their localization accuracy, positioning error statistics, and complexity. Our results depict illustrative evaluation of WLAN localization systems and guide to future improvement opportunities.
In recent years, numerous people have purchased smartphones and have become accustomed to carrying the devices with them at all times. Most advanced smartphones are now equipped with a variety of sensors that constantly measure and catch information such as geographical location, temperature, humidity, and Wi-Fi data. Among other things, such Wi-Fi data includes service set identifiers (SSID) and basic service set identifiers (BSSID), which are device information used by wireless local area network (LAN) access points. Thus, under normal circumstances, when a user's smartphone is within range of an access point, the device and the access point exchange information. The kinds of data exchanged relate to the user's geographical location histories, and can possibly be used to characterize the user's behavior. Accordingly, in this paper, we propose a personal authentication technique that uses such Wi-Fi information characteristics.
Today, there is widespread use of mobile applications that take advantage of a user's location. Popular usages of location information include geotagging on social media websites, driver assistance and navigation, and querying nearby locations of interest. However, the average user may not realize the high energy costs of using location services (namely the GPS) or may not make smart decisions regarding when to enable or disable location services-for example, when indoors. As a result, a mechanism that can make these decisions on the user's behalf can significantly improve a smartphone's battery life. In this paper, we present an energy consumption analysis of the localization methods available on modern Android smartphones and propose the addition of an indoor localization mechanism that can be triggered depending on whether a user is detected to be indoors or outdoors. Based on our energy analysis and implementation of our proposed system, we provide experimental results-monitoring battery life over time-and show that an indoor localization method triggered by indoor or outdoor context can improve smartphone battery life and, potentially, location accuracy.
Recently, several indoor localization solutions based on WiFi, Bluetooth, and UWB have been proposed. Due to the limitation and complexity of the indoor environment, the solution to achieve a low-cost and accurate positioning system remains open. This article presents a WiFibased positioning technique that can improve the localization performance from the bottleneck in ToA/AoA. Unlike the traditional approaches, our proposed mechanism relaxes the need for wide signal bandwidth and large numbers of antennas by utilizing the transmission of multiple predefined messages while maintaining high-accuracy performance. The overall system structure is demonstrated by showing localization performance with respect to different numbers of messages used in 20/40 MHz bandwidth WiFi APs. Simulation results show that our WiFi-based positioning approach can achieve 1 m accuracy without any hardware change in commercial WiFi products, which is much better than the conventional solutions from both academia and industry concerning the trade-off of cost and system complexity.
The US Federal Communications Commission (FCC) has mandated wireless network operators and mobile devices to provide accurate location information for E-911. Requirements for time of arrival (TOA) and time difference of arrival (TDOA) measurements have been specified in 3GPP LTE Rel. 9 to ensure accurate user equipment (UE) positioning even under bad conditions (e.g. with channel quickly varying and SNR being as low as −13 dB). To fulfil these requirements, it is vital to accurately estimate the first signal arriving path. In this work, we first derive - without any approximation - the Cramér–Rao lower bound (CRLB) of the LTE TOA and TDOA measurements based on the different pilots, which is shown to be as low as a few metres for SNR = −13 dB. The achievable performance of the LTE system is compared with the FCC and 3GPP requirements, and the impact of mobile multipath channels on the measurements is analysed. Then, we describe practical low-complexity methods for LTE TOA and TDOA measurements with enhanced first arriving path detection. The maximum likelihood based correlation profile is used as detection metric. After grossly determining the signal region by a moving window, three methods, namely, peak detection, SNR-based threshold and adaptive threshold based on noise floor and metric peak value are employed to estimate the first arriving path. Simulation results show that the proposed adaptive threshold-based method can meet all 3GPP requirements under various realistic mobile channels, and can in some cases achieve a performance close to the CRLB. Copyright © 2014 John Wiley & Sons, Ltd.
People spend the majority of their time indoors, and human indoor activities are strongly correlated with the rooms they are in. Room localization, which identifies the room a person or mobile phone is in, provides a powerful tool for characterizing human indoor activities and helping address challenges in public health, productivity, building management, etc. Existing room localization methods, however, require labor-intensive manual annotation of individual rooms.
We present ARIEL, a room localization system that automatically learns room fingerprints based on occupants' indoor movements. ARIEL consists of (1) a zone-based clustering algorithm that accurately identifies in-room occupancy "hotspot(s)" using Wi-Fi signatures; (2) a motion-based clustering algorithm to identify inter-zone correlation, thereby distinguishing different rooms; and (3) an energy-efficient motion detection algorithm to minimize the noise of Wi-Fi signatures. ARIEL has been implemented and deployed for real-world testing with 21 users over a 10-month period. Our studies show that it supports room localization with higher than 95% accuracy without requiring labor-intensive manual annotation.
Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. In this work, we analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers in orthogonal frequency division multiplexing systems and propose a novel approach called FILA, which leverages the channel state information (CSI) to build a propagation model and a fingerprinting system at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach.
One hour term authentication for wi-Fi information captured by smartphone sensors
- Kobayashi
Spotfi: decimeter level localization using Wi-Fi
- Kotaru
A real-time robust indoor tracking system in smartphones
- V.