QID: Robust Mobile Device Recognition via a Multi-Coil Qi-Wireless Charging System

Recent years have witnessed the increasing penetration of wireless charging base stations in the workplace and public areas, such as airports and cafeteria. Such an emerging wireless charging infrastructure has presented opportunities for new indoor localization and identification services for mobile users. In this paper, we present QID, the first system that can identify a Qi-compliant mobile device during wireless charging in real-time. QID extracts features from the clock oscillator and control scheme of the power receiver and employs light-weight algorithms to classify the device. QID adopts 2-dimensional motion unit to emulate a variety of multi-coil designs of Qi, which allows for fine-grained device fingerprinting. Our results show that QID achieves high recognition accuracy. With the prevalence of public wireless charging stations, our results also have important implications for mobile user privacy.

According to the increasing of modern people\'s life time in indoor places, the importance of indoor LBS has been continuously gotten higher attention. This phenomenon is one of the inevitable IT trends which caused by the evolved computing paradigm, mobile and wireless communication technologies, and Internet of Things. In addition, there are other hot topics in these trends, big data and cloud. The trends are like a blackhole which strongly pulls the whole technology. That is, it has same ripple effects in the indoor location service and technology. Although there are still several problems which related to the accuracy and precision in the indoor location service, it will be certainly overcome in the near future. However, the practical uses of indoor LBS are applied in some limited fields such as marketing, finance, and so on until now. This is one of the reasons why we propose the advanced indoor location measuring architecture, which is very suitable for emergency situations. Moreover, the proposed architecture can improve the accuracy and precision, when it measures an indoor location, because we need high accuracy and precision in emergency situations than common situations. In the proposed architecture, it utilizes various information and big data to measure an exact indoor position, and it operates with various IoT devices. Based on these schemes, the proposed architecture can provide high accuracy and precision than the existing methods. In this paper, we verified the superiority of architecture than others in various aspects. Keywords: Big Data, Cloud, Emergency, IoT, Indoor Location

Objective of this paper is to introduce an evolutionary-based wireless charging system. Wireless charging is a technology which is inspired by Sir Nikola Tesla's basic principles of wireless power transfer. Because of which we are able to transmit an electrical power through the air gap. Many people face low battery issues in their daily life. And it becomes worst in public places where electrical sockets are limited and not available. Moreover, they also need to take care of their traditional charging chords in their hectic day to day life. In order to abate all these difficulties of people, we developed a system which provides charging wirelessly in public area as like free public WIFI provided to the user on public area. Our system is based on Electromagnetic Induction coupling as a core functionality to possess wireless charging. In this article, we also introduced a mobile application which helps to automate charging with respect to threshold charging percentage and guard our users against harmful accidental charging disaster. Also in this article, we provide a smart automation system which focuses on electricity conservation via the Internet of Things (IoT).

Over the last years, many different methods have been proposed for indoor localization and navigation services based on Radio frequency (RF) technology and Radio Signal Strength Indicator (RSSI). The accuracy achieved with such systems is typically low, mainly due to the variability of RSSI values, unsuitable for classic localization methods (e.g. triangulation). In this paper, we propose a novel approach based on multiple neural networks. We demonstrate with experimental results that by training and then activating different neural networks, tailored on the user orientation, high definition accuracy is achievable, allowing indoor navigation with a cost effective Bluetooth (BT) architecture.

Introduction: The energy supply challenge in wireless charging applications is currently a significant research problem. To address this issue, this study introduces a novel small-scale long-distance radio frequency (RF) energy harvesting system that utilizes a hybrid model incorporating CNN, LSTM, and reinforcement learning. This research aims to improve RF energy harvesting and wireless charging efficiency.Method: The methodology of this study involves data collection, data processing, model training and evaluation, and integration of reinforcement learning algorithms. Firstly, RF signal data at different distances are collected and rigorously processed to create training and testing datasets. Next, the CNN-LSTM model is trained using the prepared data, and model performance is enhanced by adjusting hyperparameters. During the evaluation phase, specialized test data is used to assess the accuracy of the model in predicting RF energy harvesting and wireless charging efficiency. Finally, reinforcement learning algorithms are integrated, and a reward function is defined to incentivize efficient wireless charging and maximize energy harvesting, allowing the system to dynamically adjust its strategy in real time.Results: Experimental validation demonstrates that the optimized CNN-LSTM model exhibits high accuracy in predicting RF energy harvesting and wireless charging efficiency. Through the integration of reinforcement learning algorithms, the system can dynamically adjust its strategy in real time, maximizing energy harvesting efficiency and charging effectiveness. These results indicate significant progress in long-distance RF energy harvesting and wireless charging with this system.Discussion: The results of this study validate the outstanding performance of the small-scale long-distance RF energy harvesting system. This system is not only applicable to current wireless charging applications but also demonstrates potential in other wireless charging domains. Particularly, it holds significant prospects in providing energy support for wearable devices, Internet of Things (IoT), and mobile devices.

Conventional wired charging for EVs has some major drawbacks, particularly in the areas of convenience and safety. To solve these problems, the wireless charging technology has been extensively publicized. The accurate positioning of the transmitting and receiving coils is essential for achieving high charging efficiency. In this paper, an automatic coil-positioning simulation framework based on Back Propagation (BP) neural network is proposed. Based on simulation data and artificial intelligence, we make use of data-driven BP model with current, voltage and inductance as input and coil coordinates as output to describe the nonlinear relationship between inductance and coil positioning. Then the accurate localization is achieved by dynamic coupling parameters. Simulation results show that the framework enhances the accuracy and robustness of wireless coil positioning under various operating conditions. The obtained results offer a key technology for accurate automatic alignment and efficient energy transfer in wireless electric vehicle charging. The BP network structure contains several hidden layers with dropout regularization to avoid overfitting and generalize to various coil geometries. The training used supervised dataset extracted from electromagnetic simulations with different misalignment, separation and load conditions varying operating regimes. Subsequently, cross validation verifies the high prediction accuracy and less sensitivity of measurement noise and parameter fluctuation. At the same time, the framework exhibits a certain tolerance to external disturbances (such as parasitic capacitance and coupling variations). These results validate that automatic coil positioning technology can provide a feasible solution for maintaining high-efficiency energy transfer in dynamic onboard automotive environments.

The indoor location market is growing rapidly. However, fine-grained and up-to-date indoor maps are rarely available, and the existing indoor localization and navigation services mostly require extra infrastructures to provide accurate locations. In this demonstration we show iMoon, an indoor navigation system that provides indoor mapping, localization and navigation services using photos and sensor data collected from widely available mobile devices. The demonstration leverages a cohesive suite of computer vision, mobile sensing, and wireless networking techniques.

In recent years, service robots have been widely used in people’s daily life, and with the development of more and more intelligence, people put forward higher requirements for autonomous positioning and navigation functions of robots. Like outdoor navigation, indoor navigation also needs the support of navigation data. Although the indoor positioning and navigation scheme based on cameras, lidars and other sensors is gradually developing, due to the complexity of the indoor structure, manual production of indoor navigation data is time-consuming and laborious, and the efficiency is relatively low. In order to solve the problem of low productivity and improve the accuracy of robot automatic navigation, we added a new type of intelligent camera, called OpenCV AI kit or OAK-D, and proposed a method to automatically build data files that can be used for indoor navigation and location services using indoor 3D point cloud data. This intelligent camera performs neural reasoning on chips that do not use GPUs. It can also use stereo drills for depth estimation, and use 4K color camera images as input to run the neural network model. Python API can be called to realize real-time detection of indoor doors, windows and other static objects. The target detection technology uses an artificial intelligence camera, and the robot can well identify and accurately mark on the indoor map. In this paper, a high-performance indoor robot navigation system is developed, and multisensor fusion technology is designed. Environmental information is collected through artificial intelligent camera (OAK-D), laser lidar, and data fusion is carried out. In the experiment part of this paper,The static fusion map module is created based on the laser sensor information and the sensor information of the depth camera, the hierarchical dynamic cost map module is created in the real-time navigation, and the global positioning of the robot is realized by combining the word bag model and the laser point cloud matching. Then a software system is realized by integrating each module. The experiment proves that the system is practical and effective, and has practical value.

Location information plays a remarkable role in wireless sensor networks in collecting information to support distributed spectrum sensing and directional wireless charging. To decide the optimal operating frequency band of wireless sensor network, the location information of the wireless sensor network nodes should be known to fulfill the geographical map of the spectrum using the technique of distributed spectrum sensing. In addition, wireless charging also needs the location information of the wireless sensor network node for the directional wireless charging is much more efficient than the omnidirectional wireless charging. The location of the wireless sensor network node is estimated by Wi-Fi signal because the Wi-Fi infrastructures are widely deployed not only for Internet wireless access but also for the communication between wireless sensor network nodes. The most of our daily life is spent in the indoor environment, and the channel state information is one of the most important parameters of fingerprint in indoor localization. The channel state information is not a constant parameter for various timing synchronization and frequency offset even in the same location; therefore, a new parameter—normalized amplitude of channel state information—is proposed to be a robust parameter, which is insensitive to timing synchronization and frequency offset. In this article, we first formulate the received signal in a multipath channel by taking timing synchronization and frequency offset into consideration, then we derive the closed-form expression of channel state information, and propose the new parameter—normalized amplitude of channel state information. Finally, the robustness of the new parameter is verified by numerical simulations.

Knowing self location matters a lot in people's daily life. While Global Positioning System (GPS) provides almost perfect solution for outdoor area, it would not work in indoor areas because of no line-of-sight to satellites. However, since human tend to spend more and more time in complexly constructed buildings, helping people localize themselves in indoor space become a critical problem. Raising localization accuracy and reducing deployment cost are two main objects in indoor localization problem. High localization accuracy ensures usability of service, while low deployment cost lessens the effort people must take to use localization service. Numerous technologies have been proposed to tackle the problem. However, practical indoor localization that can provide high localization accuracy with minimum cost is still a vacancy.
\nIn the first part of this thesis, we devote ourself to explore the possibility of fingerprint based localization. Although a large number of fingerprinting based indoor localization systems have been proposed, our field experience with Google Maps Indoor (GMI), the only system available for public testing, shows that it is far from mature for indoor navigation. Motivated by the obtained insights from field studies with GMI, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on Ambient Magnetic Field fingerprints, which is formed by ``twisted'' geomagnetic field by building structures, that are far more stable than WiFi fingerprints, and it exploits crowdsensing to construct floor maps rather than expecting individual venues to supply digitized maps. Based on our experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience.
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\nIn our experiments with ambient magnetic field fingerprint, we see that scalability of ambient magnetic field based approach is not satisfactory comparing to WiFi based approaches. By further exploration, we find that dual properties naturally existed in ambient magnetic field fingerprint and WiFi fingerprint. Therefore based on GROPING, we present MaWi - a dual-sensor enabled indoor localization system in the second part of this thesis. Central to MaWi is a novel framework combining two self-contained but complementary localization techniques: Wi-Fi and Ambient Magnetic Field. Combining the two techniques, MaWi not only achieves a high localization accuracy, but also effectively reduces human labor in building fingerprint databases: to avoid war-driving, MaWi is designed to work with low quality fingerprint databases that can be efficiently built by only one person. Our experiments demonstrate that MaWi, with a fingerprint database as scarce as one data sample at each spot, outperforms the state-of-the-art proposals working on a richer fingerprint database.
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\nAlthough MaWi is designed to use minimum human effort to collect fingerprints, the initial spot surveying is still an inevitable burden for all fingerprint-based localization systems. To ultimately reduce human effort in initial phase, we try to find a localization solution in a model-based methodology. In the last part of this thesis, we focus on exploiting ``multipath" phenomenon in wireless signal propagation and utilize it to fully or partially reconstruct the geometry of the indoor space, as well as locate signal source. Whereas a few physical layer techniques have been proposed to locate a signal source indoors, they all deem multipath a ``curse'' and hence take great efforts to cope with it. We, on the contrary, deem multipath a ``blessing'' and thus innovatively exploit the power of it. Essentially, with minor assumption (or knowledge) of the geometry of an indoor space, each signal path may potentially contribute a new piece of information to the location of its source. As a result, it is possible to locate the source with very few sensors (most probably just one hand-held device). At the same time, the extra information provided by multipath effect can help to fully or partially reconstruct the geometry of the indoor space, which enables a floor plan generation process missing in most of the indoor localization systems. To demonstrate these ideas, we instrument a USRP-based radio sensor prototype named iLocScan; it can simultaneously scan an indoor space (hence generate a plan for it) and position the signal source in it. Through iLocScan, we mainly aim to showcase the feasibility of harnessing multipath in assisting indoor localization, rather than to rival existing proposals in terms of localization accuracy. Nevertheless, our experiments show that iLocScan can offer satisfactory results on both source localization and space scanning.

The growth in using various smart wireless devices in the last few decades has given rise to indoor localization service (ILS). Indoor localization is defined as the process of locating a user location in an indoor environment. Indoor device localization has been widely studied due to its popular applications in public settlement planning, health care zones, disaster management, the implementation of location-based services (LBS) and the Internet of Things (IoT). The ILS problem can be formulated as a learning problem utilizing Wi-Fi technology. The measured Wi-Fi signal strength can be used as an indication of the distribution of users in a various indoor location. Developing a classification model with high accuracy can be achieved using a machine learning approach. Artificial Neural Network is one of the most successful trends in machine learning. In this article, we provide our initial idea of using Cascaded Layered Recurrent Neural Network (L-RNN) for the classification of user localization in an indoor environment. Several neural network models were trained, with the best performance attainment is reported. The experimental results marked that the presented L-RNN model is highly accurate for indoor localization and can be utilized for many applications.

Indoor positioning service (IPS) allows users to navigate in large and unfamiliar indoor venues with no or unreliable GPS signals. Received Signal Strength (RSS)-based IPS has received much attentions during the past decade because it does not require costly infrastructure update but makes use of WiFi infrastructure readily available and ubiquitous smartphones. While IPS can greatly facilitate human indoor activities, it also raises serious privacy concerns as periodical location queries submitted by users allow the IPS server to continuously track users’ whereabout. In addition, a curious user may infer the fingerprint database stored at the IPS server. This paper introduces PriHorus, a privacy-preserving indoor positioning scheme built upon Horus, a representative RSS-based indoor positioning system. PriHorus can protect both users’ location privacy and IPS server’s data privacy while achieving the same level of location accuracy as in the original Horus system.

Assistance Systems employing for example interactive control of building environments and indoor localization services are topics of growing relevance for ambient systems. Today there exist already a variety of indoor localization approaches and systems as well as universal remote control systems. In recent works we reported on our research on lightweight middleware offering a position related smart building services gateway. Such a system allows clients to receive visualand contextual information about the environment and allows performing control of systems within this environment. The gateway middleware eases abstraction and integration of underlying systems and information, for example indoor localization technology, the original 3D building model data format, or existing control applications into client applications. In the current work we investigate the integration of dynamic position information using the iLoc indoor localization system and a mobile position visualization application. Indoor Localization; Position Information Gateway; Building Automation

With more and more requirements of navigation for complex indoor environments, the indoor location service has become the hotspot in the field of mobile computing. However, with only one single type of wireless signal, it is difficult to achieve ideal accuracy of positioning in the indoor environments full of indoor noises. In order to improve the performance of indoor location service, we propose a novel indoor localization mechanism, which realizes an effective data fusion of Wi-Fi and RFID signals via on-demand deployment of Wi-Fi access points and RFID tags. This mechanism can eliminate the blind areas of location so as to realize the low-cost and high-accurate indoor localization. In order to further improve the location performance, we put forward the Kalman filter algorithm based on singular value judgment (KFASVJ) and KFASVJ-based indoor localization algorithm (KILA). The KILA is adopted to judge the maximum singular value of Wi-Fi signal wave, so as to optimize the wireless signal wave. KILA can reduce the indoor noise interference with Wi-Fi signals, so to realize a high accuracy of positioning and real-time positioning stability in complex indoor environments. The experimental results and the performance analysis show that KILA achieves a better accuracy of positioning than typical Kalman-filter-based localization algorithm (KFLA), about 13% to 28% accuracy of positioning improvement in the indoor environments with the [35dB, 65dB] indoor noise. KILA has the lower time complexity, the higher location speed and the better stability, and it can maintain a good localization performance even in the indoor environment with the indoor noises changing dynamicly.

The recent years has seen a lot of research in indoor Iocalization and location services. Wireless location finding is one of the key technologies for wireless sensor networks. The GPS technology used for outdoor Iocalization, but when to deal with indoor Iocalization GPS solution is inadequate. Indoor locations include buildings like supermarkets, big malls, parking, universities and locations under the same roof. Though indoor location services based on received signal strength indication (RSSI), fingerprinting, angle of arrival (AoA) and time of flight (ToF) based techniques attracted more attention from Researcher, there is very less work has been done in achieving accuracy, removing NLOS errors and providing indoor navigation system. The proposed system which works on improving positioning accuracy of Iocalization and increasing speed of positioning with NLOS Error and TOF Ranging Error Mitigation using Wi-Fi RSSI based Fingerprinting and use of inertial sensors of smartphone like Accelerometer, magnetometer and Gyroscope. Furthermore an advanced particle filter is used to mitigate ranging error and NLOS Error.Keywords-component; formatting; style; styling; insert (key words)