Wireless Positioning System Research Articles

Edge Computing-Based Localization Technique to Detecting Behavior of Dementia

Wireless localization systems have significant impact in the field of human-driven edge computing (HEC). It became very attractive among the researchers and used in applications of numerous areas such as medical, industrial, public safety, logistics, and so on. Ultra-wideband (UWB) technology used in localization systems owing to achieving high accuracy in real-time. In this paper, we exhibit a UWB based localization system based on the edge computing (EC) paradigm to analyze the wandering behavior of the patients who are suffering from dementia disease in the large-scale form. Physical changes in the brain are responsible for dementia disease. The appearance of wandering behavior is a common manner of the patients, which also a threat and interference for caregivers. We used the UWB standard appliance to symbolize various sorts of wandering patterns, including pacing, lapping, and two random movements in the large 2D map. The flow of all the movements illustrated in the X and Y-axis. Support vector machine (SVM) and k-nearest neighbor (k-NN) algorithms used to classify all the patterns and accuracy result is above 99%. The result shows that the proposed system can achieve high accuracy in classification and satisfactory for applications in the medical area.

Automatic Positioning and Recognition of Anesthesia Points Based on Ultrasound Image Guidance Technology

Local anesthesia has certain harm to the patient, and the wrong position of the anesthesia point may result in poor anesthesia effect. Based on this, this study built an automatic positioning recognition model based on ultrasound image guidance technology. At the same time, this paper combined the actual requirements of anesthesia to establish a wireless positioning system based on ultrasonic infrared, and the system combined ultrasonic propagation characteristics and sensing receiving characteristics. In addition, in the study, this article combined with the state of the anesthesia point to take ultrasound-guided anesthesia. Finally, this paper designed a comparative experiment to study the performance of automatic positioning of anesthesia points based on ultrasound image guidance technology and collected actual data to analyze. Studies have shown that the method proposed in this study has certain clinical effects and can provide theoretical references for subsequent related research.

NLOS Identification via AdaBoost for Wireless Network Localization

To identify NLOS propagations for a high-resolution wireless localization system, a strong NLOS classifier is proposed via a machine learning algorithm named AdaBoost. Signal features are extracted to characterize the distinctions between LOS and NLOS, based on which a series of weak learners are primarily given. By training and re-sampling data iteratively, weak learners with the minimal classification error are selected and the weighted sum is boosted into a strong classifier. Simulations demonstrate that the proposed AdaBoost-based NLOS classifier can improve NLOS identification performance, leading to reduced positioning errors.

Epoxy Radiotechnical Hot Pressed Glass Fiber Plastics for Ship Aerial Fairings and Protection of Antennas in Radio Connection and Radio Location Systems

The present work is aimed at solving an urgent scientific problem of creation of high-strength and water-resistant dielectric glass-reinforced hot pressed glasses using bi- and polyfunctional epoxy amine binders and fiberglasses from alkalineless, quartz, and silica glasses, as well as their use in shipbuilding.

Preliminary Analysis of Organic Wireless Positioning System in an Indoor Environment

The massive deployment of wireless infrastructure especially in the trending 4th Industrial Revolution (IR4.0) has opened the opportunity for wireless-based positioning and localization. The works on wireless positioning system revolves on two techniques, the geometrical technique employing either triangulation or trilateration, and mapping technique employing signal fingerprint method. The later is more favoured since the accuracy was superior to geometrical technique. However, the signal fingerprint method required enormous efforts in order to record and stored the signal fingerprint. This paper investigates the new approach in constructing the signal fingerprint using organic approach. The signal is recorded using the developed apps, and then the user sends the signal data into the cloud. The organic fingerprinting data were compared to the conventional fingerprinting. The preliminary results show promising accuracy in the experimented indoor environment.

IVR

Smartphone localization is essential to a wide range of applications in shopping malls, museums, office buildings, and other public places. Existing solutions relying on radio fingerprints and/or inertial sensors suffer from large location errors and considerable deployment efforts. We observe an opportunity in the recent trend of increasing numbers of security surveillance cameras installed in indoor spaces to overcome these limitations and revisit the problem of smartphone localization with a fresh perspective. However, fusing vision-based and radio-based systems is non-trivial due to the absence of absolute location, incorrespondence of identification and looseness of sensor fusion. This study proposes iVR, an integrated vision and radio localization system that achieves sub-meter accuracy with indoor semantic maps automatically generated from only two surveillance cameras, superior to precedent systems that require manual map construction or plentiful captured images. iVR employs a particle filter to fuse raw estimates from multiple systems, including vision, radio, and inertial sensor systems. By doing so, iVR outputs enhanced accuracy with zero start-up costs, while overcoming the respective drawbacks of each individual sub-system. We implement iVR on commodity smartphones and validate its performance in five different scenarios. The results show that iVR achieves a remarkable localization accuracy of 0.7m, outperforming the state-of-the-art systems by >70%.

FineLoc: A Fine-Grained Self-Calibrating Wireless Indoor Localization System

Self-calibrating wireless indoor localization systems construct the radio map even the indoor floor plan automatically, which avoids the labor-intensive site survey process; however, existing systems utilizing the feature of Wi-Fi signals can only provide coarse-grained indoor maps, which hinders improvement of localization accuracy. In this paper, we present FineLoc, a fine-grained self-calibrating localization system based on the freely-deployed Bluetooth low energy (BLE) nodes and crowdsourced data, which can profile more detailed layout information of the indoor space. We first reveal that existing systems can only generate inaccurate floor plans owning to the coarse-grained Wi-Fi reference information. Then, we utilize the increasingly popular BLE beacon nodes as the source of reference information, with which a series of dead-reckoning optimization and new schemes particularly for finer-grained indoor map construction are presented. We implement a prototype FineLoc system, which is deployed in around $11,000\;\mathrm{m}^2$11,000m2 areas. Our experimental results with the prototype show that FineLoc can achieve 80 percent localization errors within $1.6\;\mathrm{m}$1.6m, $1.4\;\mathrm{m}$1.4m, and $1.1\;\mathrm{m}$1.1m in the library, classroom building, and office building, respectively, with an average density of deployed BLE nodes less than $2.6/100\;\mathrm{m}^2$2.6/100m2.

Optimal Power Allocation for Anchor Nodes in Jammed Wireless Localization Systems

In this letter, optimal power allocation strategies are proposed to improve the localization performance in jammed wireless localization systems. For a wireless localization system, the power allocation is a key issue because it relates to network lifetime and localization accuracy. However, when jammer nodes are considered, the power allocation problem for anchor nodes becomes a new task due to the effect of jammer nodes. So in this letter, two power allocation schemes are introduced in jammed wireless localization systems. In the first scheme, the average Cramer-Rao lower bound (CRLB) of agent nodes is the objective and in the second scheme, the objective is to minimize the maximum CRLB of agent nodes. In particular, both power allocation problems can be transformed into semi-definite programs (SDP) with total and peak power constraints. Numerical results indicate that the proposed schemes can reach better performance in localization accuracy compared to uniform power allocation strategies.

Jamming Strategies in Wireless Source Localization Systems

We consider optimal jamming strategies in wireless source localization systems, where anchor nodes estimate positions of target nodes in the presence of jammers that emit zero-mean Gaussian noise. The Cramer–Rao lower bound (CRLB) for target location estimation is derived and the problem of optimal power allocation for jammer nodes is formulated to maximize the average CRLB for target nodes under total and peak power constraints. Exploiting the special problem structure and successive convex approximation techniques, we develop an iterative algorithm that transforms the original non-convex problem into a sequence of convex geometric programs. In addition, we present a closed-form solution that is asymptotically optimal. Numerical results demonstrate the improved jamming performance of the proposed solutions over the uniform power allocation strategy.

Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision.

Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localization performance and node lifetime. To analyze the impact, we used the established trilateration based localization model with two methods i.e., Centroid Approximation (CA) and Minimum Mean Square Error (MMSE). We observed that trilateration based localization with MMSE method outperforms the CA method. We further investigated the use of two filters i.e., Low Pass Filter (LPF) and Kalman Filter (KF) and evaluated their effects in terms of mitigating the random variations from BLE signal. In comparison to non-filter based approach, we observed a great improvement in localization accuracy and localization precision with a filter-based approach. Furthermore, in comparison to LPF based trilateration localization with CA, the performance of a KF based trilateration localization with MMSE is far better. An average of 1 m improvement in localization accuracy and approximately 50% improvement in localization precision is observed by using KF in trilateration based localization model with the MMSE method. In conclusion, with KF in trilateration based localization model with MMSE method effectively eliminates random variations in BLE RSS with multiple transmission power levels and thus results in a BLE based WILS with high accuracy and high precision.

Robust Power Allocation for Cooperative Localization in Jammed Wireless Sensor Networks

In this paper, we propose robust power allocation strategies to improve the localization performance in cooperative wireless sensor localization systems when suffering interference of jammer nodes. In wireless sensor localization systems, transmitting power strategies will affect the localization accuracy and determine the lifetime of wireless sensor networks. At the same time, the power allocation problem will be evolution to a new challenge when there are jammed nodes. So in this paper, we first present the optimization framework in jammed cooperative localization systems. Moreover, the imperfect parameter estimations of agent and jammer nodes are considered to develop robust power allocation strategies. In particular, this problem can be transformed into second-order cone programs (SOCPs) to obtain the end solution. Numerical results show the proposed power allocation strategies can achieve better performance than uniform power allocation and the robust schemes can ensure lower localization error than nonrobust power control when systems are subject to uncertainty.

Issue of Assessment of the Strong Electromagnetic Pulses Impact on the Functioning of Selected Electronic Devices

Abstract By analyzing the exploitation process of selected electronic devices (including those used in communication and radiolocation systems), it can be concluded that they operate in heterogeneous, often extreme conditions. Their operate in a full ability state depends not only on the reliability of the subsystems that create them, but also on the effective management of their exploitation, including vulnerability and resistance to the impact of strong electromagnetic pulses. The article proposes specific criteria containing indicators that enable the assessment of the strong electromagnetic pulses impact on the functioning of selected electronic devices.

Changing the Delay of the Analog-Digital Conversion in Reduce the Leakage Signal Effect

The research of reduction the leakage signal effect in short range radiolocation system is considered. LFM signal is used as a probe signal. The structure of the differential frequency signal and the structure of operating harmonic are shown for the spectral method of processing the useful signal. To reduce the leakage signal effect, it is proposed to use subsampling of the useful signal with a variable delay of the clock signal of the analog-to-digital converter. The dependence between the level of the parasitic component of the operating harmonic signal, due to the leakage signal effect, and the delay of the clock signal of the analog-digital converter, is given. The clock signal of the analog-digital converter is generated by a microcontroller timer. The timer is initialized in pulse width modulation mode. The required delay of the clock signal of the analog-digital converter is selected by changing the threshold of the timer. For testing the algorithm, a microwave module K-LC5 and a prototype board with a microcontroller STM32F407 are used.

Fuzzy Logic Type-2 Based Wireless Indoor Localization System for Navigation of Visually Impaired People in Buildings.

The ability to precisely locate and navigate a partially impaired or a blind person within a building is increasingly important for a wide variety of public safety and localization services. In this paper, we explore indoor localization algorithms using Bluetooth Low Energy (BLE) beacons. We propose using the BLE beacon’s received signal strength indication (RSSI) and the geometric distance from the current beacon to the fingerprint point in the framework of fuzzy logic for calculating the Euclidean distance for the subsequent determination of location. According to our results, the fingerprinting algorithm with fuzzy logic type-2 (hesitant fuzzy sets) is fit for use as an indoor localization method with BLE beacons. The average error of localization is only 0.43 m, and the algorithm obtains a navigation precision of 98.2 ± 1%. This precision confirms that the algorithms provide great aid to a visually impaired person in unknown spaces, especially those designed without physical tactile guides, as confirmed by low Fréchet and Hausdorff distance values and high navigation efficiency index (NEI) scores.

Research and Experiment on UWB Path Loss Characteristics in Outdoor Near-Ground Environments

Ultra wideband (UWB) technology has been adopted in many wireless communication and localization systems in indoor and outdoor areas. In this paper, based on the application in outdoor near-ground environments, the main path loss models are discussed according to the analysis of the propagation environment and received UWB signal. The path loss calculation model by combining the multi-waves with the threshold is proposed. With the outdoor experimental measurements and data processing, the path loss exponents are determined and the path loss characteristics are discussed.

Dynamic Wireless Indoor Localization Incorporating With an Autonomous Mobile Robot Based on an Adaptive Signal Model Fingerprinting Approach

Indoor localization based on received signal strength (RSS) will result in a decreased precision after the environment changes. In this paper, we develop an adaptive wireless indoor localization system (ILS) for dynamic environments. The system consists of the following two components: an automated database updating process and a new fingerprinting algorithm called adaptive signal model fingerprinting (ASMF). In the ILS, a self-locating mobile robot is set up to continuously collect RSS measurement data within the localization space for autonomously updating the fingerprint database. ASMF is designed to reduce the time consumption and the amount of RSS data needed for updating the database. The fingerprint of the signal in ASMF is constructed by the position of the beacons and three signal models, which can be duly corrected based on the regression and optimization algorithm. Finally, we propose experiments for positioning targets in the static and dynamic environments and compare the results of the ASMF algorithm with traditional trilateration and k-nearest-neighbor fingerprinting algorithms. The experimental results demonstrate that the ASMF-based ILS provides much better performance in both static and dynamic environments; furthermore, the positioning accuracy can be actually maintained by the autonomous updated ASMF database.

Implicit Secondary Authentication for Sustainable SMS Authentication

Short message service (SMS) is the most widely adopted multi-factor authentication method for consumer-facing accounts. However, SMS authentication is susceptible to vulnerabilities such as man-in-the-middle attack, smishing, and device theft. This study proposes implicit authentication based on behavioral pattern of users when they check an SMS verification code and environmental information of user proximity to detect device theft. User behavioral pattern is collected by using the accelerometer and gyroscope of a smart device such as a smartphone and smart watch. User environmental information is collected using device fingerprint, wireless access point, Bluetooth, and global positioning system information. To evaluate the performance of the proposed scheme, we perform experiments using a total of 1320 behavioral and environmental data collected from 22 participants. The scheme achieves an average equal error rate of 6.27% when using both behavioral and environmental data collected from only a smartphone. Moreover, it achieves an average equal error rate of 0% when using both behavioral and environmental data collected from a smartphone and smart watch. Therefore, the proposed scheme can be employed for more secure SMS authentication.

Radiotechnical hot pressed glass fiber plastics for ship aerial fairings and antennas protection in radio connection and radio location systems

The article is devoted to the urgent scientific problem of creation and introduction in shipbuilding of high-strength, water-resistant dielectric glass-reinforced hot pressed plastics on the basis of bi- and polyfunctional epoxy-amine binders and glass fabrics from alkali, quartz and silica glass.

Analysis of Accuracy and Precision of WLAN Position Estimation System based on RSS

The coordinates of the position of a wireless access point are the main goal in the wireless localization technique. In common, outdoor wireless localization techniques use the trilateration method by installing several devices used as anchors, and this makes the cost for installation and maintenance higher. An outdoor wireless localization system that is more efficient, effective, and flexible but still accurate and precise becomes indispensable. This paper proposes a wireless position estimation system based on a received signal strength (RSS) value called WLAN Position Estimation System (WLAN PES) integrating WLAN Distance Estimation System (WLAN DES) with WLAN PES formula. To confirm the accuracy and precision of WLAN PES, this paper focuses on the analysis of WLAN PES testing conducted at ten points in which each measurement point had coordinates and angles that are different from one another. The test point was made to circle the target WLAN access point. The distance of each test point was 1000 meters against the targeted WLAN access point. In each of these test points, the WLAN finder reads RSSfnd. Then, the system calculated the estimated distance value using WLAN DES based on the RSSfnd value. After the system obtained a distance estimation value, the estimated position value was calculated with the WLAN PES formula. In addition to the distance estimation value, WLAN PES formula required some variables such as latitude and longitude coordinates from the WLAN finder position, and the bearing of the WLAN finder. WLAN PES was found to be capable of determining the estimated position coordinates of a WLAN access point with an accuracy value and precision value of 93.26% and 98.77%, respectively.

Monitoring of Drainage System in Urban Using Device Free Localization Neural Networks and Cloud computing

Wireless Sensor Network is a Wi-Fi community consisting of spatially propagated and self-sufficient devices using sensors to detect physical or environmental conditions. During heavy rainfall, the urban drainage system cannot drain the water. A wireless sensor with many interconnected wireless sensor nodes captures real-time data from the network environment and transmits this data to a base station for analysis and operation. With wireless sensor nodes, it is possible to capture and monitor the amount of water in drainages and the difference in water flow between the two points in the drainage system. Nevertheless, the majority localization techniques aims on device based localization, which can find target with festinated devices. It is not suitable for applications such as terrain, drainage flow and flooding. Here device free wireless localization system using artificial neural networks and a cluster based wireless sensor network system to monitor urban drainage is proposed. There are two stages in the system. During the off-line preparation stage, Acceptable Signal Strength (RSS) differential metrics are calculated between the RSS metrics together while the monitor area is empty and calculated by a specialized in the region. Some RSS dissimilarity values ​​are selected in the RSS Difference Matrix. The RSS dissimilarity standards ​​and associated matrix indices are taken as the inputs of the ANN representation in addition to the identified position coordinate are in its outputs. The real-time data collected from the wireless sensor network is used to detect overflow and provide alarms before disturbances arise.