Ratio-based zero-profiling indoor localization

The success of wireless local area networks (WLANs) has led to an intense interest among wireless engineers in understanding and predicting radio propagation characteristics within buildings. This paper present radio signal propagation measurement and modeling at 2.4GHz, within a building in the Faculty of Engineering, University of Benin. A base station built around SENAO access point, and a PA24 flat panel directional antenna of 19dBi gain and 17.50 beam-widths was setup for the experimentation. Signals were monitored from the fixed BS using LINKSYS, and NET STUMBLER version 4.0 software run on a Laptop. In this report, the effect of multi-path and delay spread was not considered. The result of the investigation revealed a significant attenuation of the radio signal within the small distance. A path loss exponent of 7.8 to 8.9 on the ground floor, and 2.9 to 5.2 for the 2nd floor were obtained for the period of investigation.

With the proliferation of the Internet of Things (IoT), employing Received Signal Strength (RSS) as a metric to determine the location of a target (e.g., person or mobile device) is of great interest in terms of cost and ease of implementation. Indeed, RSS measurements can be easily obtained for most off-the-shelf devices, such as WiFi- or ZigBee compatible devices or sensors. This paper deals with the indoor localization problem in wireless sensor networks (WSNs) and proposes a new approach for radio signal propagation modelling and localization estimation, that accounts for the imperfection of RSS measurements and the reliability of RSS sources by using the Dempster-Shafer Theory (DST). In the signal propagation modelling, key information regarding the geometry of indoor environment that is divided into zones separated by walls (zoning), are considered. Based on the number of walls, the RSS irregularities are estimated using different distance intervals, which are weighted by a probability density determined experimentally. To estimate the location of a target node, the PCR6 rule is used to combine the belief masses of the positions obtained from the probability density. In order to evaluate the performance of the proposed approach, an experimental WSN has been deployed in a living apartment. The obtained results demonstrate that the proposed approach improves the localization accuracy compared to the case without zoning. Moreover, the obtained localization mean error proves the feasibility of a precise localization of humans in indoor environments in the case of Ambient Assisted Living and Social Robotics applications.

Ultra-wideband (UWB) is a popular technology for indoor positioning systems (IPSs) due to its robust signaling in harsh environments, through-wall propagation, and high-resolution ranging. However, the propagation of wireless signals in indoor environments is affected by nonline-of-sight (NLOS) conditions, which can lead to positively biased distance estimates. Several research works were carried out in this area, but the NLOS effect caused by the human body shadowing on the ranging performance is not properly addressed in the literature. In this paper, a commercial UWB transceiver is used to assess the impact of the human body shadowing on the ranging accuracy. A set of real-time features is combined with different machine learning algorithms for NLOS identification and mitigation. With a subset of four features, a 0.97 F1-score was obtained for NLOS identification. For NLOS mitigation, with a subset of three features, the residual error follows a Gaussian distribution, has a mean error close to zero, and an STD of 0.67 m.

In this paper, we propose a surface correlation based fingerprinting method in sparse Wi-Fi environment. The proposed method can estimate location even with very few Wi-Fi APs by using accumulated spatial RSS pattern. We verify performance of the proposed method through several tests in a building with complex paths. Many studies have developed localization technologies that use measurements such as RSS (Received Signal Strength) or RTT (Round Trip Time) for Wi-Fi signal. All RF have signal noise and distortion problems as well as multipath problem in indoor environments. As the distance from the RF signal source increases, the discrimination of the RSS decreases due to RF signal noise and distortion. Therefore, ambiguity of RSS increases as the distance increases, the performance deteriorates. The conventional RF based localization method have tried to maintain dense RF environment to guarantee stable and accurate performance. However, in order to maintain the dense RF environment, it is becoming a barrier to commercialization of localization technology due to high cost of constructing RF infrastructure and maintenance difficulty. In this paper, we propose a Surface Correlation (SC) based fingerprinting technique to maintain high positioning performance even with few Wi-Fi APs. The surface is the accumulated RSS pattern data during the movement of a pedestrian, which means the spatial RSS pattern. Conventional fingerprinting methods using kNN (k-Nearest Neighbors) and WkNN (Weighted kNN) are very vulnerable to signal noise and distortion because they utilize instantaneous RSS data. The performance of Particle Filter (PF) based localization technology is also degraded in areas where Wi-Fi AP is sparsely distributed. However, the proposed method is very robust against signal noise and distortion because it compares the spatial RSS pattern with the reference RSS pattern on the fingerprinting database. This increases localization accuracy and availability for Wi-Fi coverage. For localization, the proposed method conduct correlation process for the spatial RSS pattern of a pedestrian with reference RSS pattern of database. So, the reference RSS pattern should be extracted from a reference trajectory that matches a pedestrian`s walking path. The proposed method utilize a database of ‘link-node’ structure to dynamically generate a reference trajectory in indoor environment with complicated path. It generate candidate reference trajectories using only MAC address and turn event of Pedestrian Dead-Reckoning (PDR). The proposed method fix a reference trajectory between these candidate trajectories through correlation process of measured RSS pattern with each reference RSS pattern. A reference point on the fixed reference having most similar RSS pattern to a pedestrian`s is considered as a current location. In order to improve performance at a fork where RSS ambiguity increases, the proposed method estimates preferentially for a reference trajectory matching with PDR turn event. The turn event can be wrong due to various motion of a pedestrian. The proposed method selects a correct reference trajectory by continuously performing correlation process on candidate reference trajectories. In order to verify the performance of the proposed surface correlation based fingerprinting method, we conducted several tests in a three-story building. Before the tests, we conducted a survey of Wi-Fi environment to construct fingerprinting database in the building. As a result of the survey, 170 APs are distributed in the building. We reduced the number of APs by 20% from 170 to 17, and analyzed performance of the proposed and conventional localization methods. The test results show that the performance of the conventional localization methods degrade as the number of applied APs decreases. The proposed fingerprinting method shows a 3 meter localization error even with very few Wi-Fi APs, and unlike the conventional fingerprinting method, the localization error is bounded to be within 5m due to the use of spatial RSS pattern. In this paper, we presented the fingerprinting method using the surface correlation in sparse Wi-Fi environment. The proposed fingerprinting method accumulates Wi-Fi RSS during movement of a pedestrian. The proposed method compares the spatial RSS pattern, which is accumulated RSS data, with reference RSS patterns of fingerprinting database. By comparing the RSS patterns, the proposed method greatly improves accuracy as well as stability. In addition, it can be effectively applied in a sparse Wi-Fi environment by improving the availability of ambiguous areas of Wi-Fi coverage. Through these results, we expect to be able to provide accurate and reliable location information even in indoor weak RF environment.

In this paper, we present new received signal strength (RSS)-based indoor localization system. In indoor environment, it is not desirable to use a theoretical signal propagation path-loss model. The signal propagation is severally affected by surrounded object, especially metallic object. In order to cope with this problem, we introduce a new path-loss exponent estimation (PLE) node. This node generates a path-loss model appropriate for a given space. We then determine a mobile node's location based on the new path-loss model. Since PLE node operates in real time manner, it can robustly adapt the path-loss model for the circumstance. We evaluate our adaptive path-loss model-based localization system through actual experimental tests.

A multipath model of UWB pulse propagation in the indoor environment is addressed in this paper. The simulation utilizes the principles of geometrical optics (GO) for direct and reflected paths' tracing and time domain technique for describing the transient electromagnetic field reflected from wall, floor, ceiling, and objects. The closed-form time domain analytic solution for UWB impulse signal reflection, which takes into account the frequency range from 3. 1 to 10.6 GHz, is proposed, and it is suitable for dry concrete, wood, glass, etc. , which are envisioned as common building construction materials for indoor environment. A specific house with two cube-like objects, which is thought to be a more realistic environment, is investigated. The simulation results provide the three-dimension (3D) space-time distribution of received pulsed field, which give more intrinsically interpretations for the mechanics of UWB impulse signals propagation in indoor environment.

Time reversal has been shown as a promising technology for the indoor positioning. Instead of mitigating the multipath channel, the time reversal based indoor positioning system (TRIPS) exploits the rich multipath propagation in indoor environments as a specific signature for each location. In order to do this, a database should be first constructed by channel probing. The devices used in this process are assumed to have no hardware impairments or be well calibrated. However, the low cost terminal to be located can exhibit impairments, such as I/Q imbalance at the front end transmitter known to create an interference image of the signal that can particularly impact the performance of time reversal. In this paper, we investigate the impact of the I/Q imbalance on the TRIPS. We analytically show that the I/Q imbalance modifies the metric used for the localization and hence reduces the spatial focusing gain of TRIPS. Numerical simulations are carried out to evaluate this observation. The results further show that the I/Q imbalance creates errors in the positioning estimation. More specifically, at the gain and phase imbalances of respectively 1.1 and 10°, the focusing gain reduction is about 0.5 dB while the estimation error rate is increased by about 5% in the specified scenario.

In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor en-vironments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Never-theless, such BLE- and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices' information (i.e., estimated positions and BLE- RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the stand-alone lateration method in terms of positioning accuracy.

Indoor localization systems have already wide applications for providing localized information and directions. They can also be very useful in medical applications, as an individual’s indoor movements can be indicative of his/her clinical status. Research for indoor localization systems is ongoing and there are numerous solutions with different hardware or algorithmic approaches, for different use cases. Ease of setup and cost are significant factors when using indoor localization systems for medical applications. This paper proposes an indoor localization system with room-level accuracy, focusing on an easy installation procedure that can be easily followed from non-technical staff, as well as on low cost, so that it can be widely adopted. The proposed system has been mainly designed for behavioural monitoring of adults in an indoor environment. The system operates by tracking a carried device based on the processing of RSS (Received Signal Strength) measurements from Bluetooth Low Energy beacons placed in the area. The proposed system has been evaluated in realistic settings and and achieves an accuracy of 95.31% in room estimation.

Wi-Fi-based indoor positioning systems are becoming increasingly prevalent in digital transitions; therefore, ensuring accurate and robust positioning is essential to supporting the growth in demand for smartphones’ location-based services. The indoor positioning system on a smartphone, which is generally based on Wi-Fi received signal strength (RSS) measurements or the fingerprinting comparison technique, uses the K-NN algorithm to estimate the position due to its high accuracy. The fingerprinting algorithm is popular due to its ease of implementation and its ability to produce the desired accuracy. However, in a practical environment, the Wi-Fi signal strength-based positioning system is highly influenced by external factors such as changes in the environment, human interventions, obstacles in the signal path, signal inconsistency, signal loss due to the barriers, the non-line of sight (NLOS) during signal propagation, and the high level of fluctuations in the RSS, which affects location accuracy. In this paper, we propose a method that combines pedestrian dead reckoning (PDR) and Wi-Fi fingerprinting to select a k-node to participate in the K-NN algorithm for fingerprinting-based IPSs. The selected K-node is used for the K-NN algorithm to improve the robustness and overall accuracy. The proposed hybrid method can overcome practical environmental issues and reduces the KNN algorithm’s complexity by selecting the nearest neighbors’ search space for comparison using the PDR position estimate as the reference position. Our approach provides a sustainable solution for indoor positioning systems, reducing energy consumption and improving the overall environmental impact. The proposed method has potential applications in various domains, such as smart buildings, healthcare, and retail. The proposed method outperforms the traditional KNN algorithm in our experimental condition since its average position error is less than 1.2 m, and provides better accuracy.

The computational capabilities of off-the-shelf wireless sensors networks presents a limitation when more complex forms of localization algorithms are employed for location estimation purposes, particularly in an indoor environment. Range-free algorithms rely on Received Signal Strength (RSS) from sensors that are location aware (anchor nodes) as the major means of distance estimation. This paper presents a non-site specific algorithm for better estimating RSS relationship with distance. By employing a unique form of rationalization of raw RSS with respect to distance using the proposed algorithm, it is possible to enhance the reliability of RSS when employed in indoor Localization Algorithms. Consequently, this paper presents an innovative RSS-Distance rationalization algorithm for localization of objects in an indoor environment. The paper compared the proposed algorithm with Simple Moving Average (SMA) algorithm due to the wide applicability and ease of manipulation of SMA. The analysis of the proposed algorithm and SMA shows that the proposed algorithm better modifies RSS for more accurate position estimation in an indoor environment.

Time reversal-based indoor positioning system (TRIPS) is a promising technology for the centimeter-accuracy indoor positioning, since it exploits the rich multipath propagation in indoor environments as a specific signature for each location. In TRIPS, a database is first constructed via channel probing. Well-calibrated devices are usually assumed in this process, i.e., no hardware impairments. However, a low cost terminal to be located, whose typical impairment is the I/Q imbalance (IQI) at the front-end transmitter, can significantly influence the TRIPS performance. More specifically, IQI creates an interference image of the signal that reduces the metric value used in TRIPS and hence decreases the localization accuracy. In this paper, we analytically investigate the impact of the IQI on the metric of TRIPS. A closed-form approximation of the localization metric inherent to the IQI is derived. In order to improve the TRIPS performance, an effective IQI mitigation method is proposed. Numerical simulations are carried out to validate the derived analytical expression under the IQI impact and the proposed compensation method.

There are no large-scale deployments to navigate the people indoor environments as the GPS for outdoor environments. For this reason, the development of high-performance systems of indoor localization has been considered the potential research areas by both academia and industry in last decade. These indoor localization research studies are divided into two groups in the literature which are readerbased and tag-based systems. Most of these studies are reader-based. These readers and tags can determine the received signal strength levels of the signal using the radio frequency technology. We present a tagbased and cost-effective indoor localization system using NRF24L series radio modules. Different from the available tag-based systems, we propose a smart transmit power scheme run in the tags called SmartTag. The SmartTag provides communication with the nearest reader using different power levels. However, the existing tag-based methods require the received signal strength indicator (RSSI) information to achieve high localization accuracy. Our proposed system eliminates RSSI information with the SmartTag. We have developed a simulation of the prototype system, and we used Monte Carlo simulations in many different scenarios. We have also implemented the system prototype and conducted in an actual indoor environment to demonstrate the performance of the SmartTag by comparing with the Monte Carlo simulations. From the indoor experimental results, we confirm that the detections of room and objects are accurate and positioning accuracy is improved thanks to the SmartTag localization. If we briefly summarize our work, we have determined the position in indoor environments by communicating with the nearest reader in the medium thanks to the scheme on the SmartTag.

In today’s era finding the location and navigation of an object is essential for many reasons. The primary reason is locating the object, and navigation helps systems to make decisions faster. Secondary reason is we may detect fault and posses faster processing and operation. Another side of development and research is for cost-effective techniques involving indoor and outdoor systems. Many researchers are working on indoor positioning and developing lightweighted solutions using radio frequencies. Managing and monitoring radio frequencies in an indoor system have physical barriers; they also have some technological limitations where we can provide solutions. The solutions for indoor localization and tracking are GPS signal, Bluetooth, infrared, RFID, and wireless LAN. All these radio frequencies deal with a crucial issue of gaining RSS (Received Signal Strength). In this work, we are focusing on RFID-based indoor environment where RSS is our main attention, we also target on gaining efficiency and accuracy, stability, robustness. The proposed algorithm of this work is mainly targeting with directional antenna by which we can overcome the physical and other barriers of radio frequency. These kinds of indoor environments such as health care, loyalty management system, automatic parking allotment system, tracking services for older people, or customers inside living communities, mobile robot, logistics system, etc., can be facilitated using directional antenna and RFID tagging for navigation indoor positioning.

Objectives: The growing demand of Wireless connectivity supported in the 802.11 standard has caused a high demand of wireless networks by the users, due to the benefits of mobility and low-cost implementation. Such networks have been used in common places such as houses, offices, schools among others. The objective of this article is to propose an empiric propagation model, supported in the Log-Normal-Shadowing Path Loss model for 2.4 GHz and 5 GHz bands in Indoor environments, compatible with University campus building. Methods/Statistical Analysis: A scenario of a six-storey building on a university campus was proposed, with dimensions of 60 m long, 34 m wide and 24 m high. Additionally, such building has 24 Access Points (AP) distributed in such a way that allow network to serve connectivity to its students. In order to establish the expressions that describe the Log-Normal-Shadowing Path Loss propagation model, an experimental design of factorial mixed type was considered involving three factors: frequency band (2.4 GHz or 5 GHz), condition of the media (Free space or with obstacles) and the distance between the receiver and the AP. The experiment aims to assess how each factor influences on the Received Signal Strength Indicator RSSI. Topic Significance: The proposed model will be used to run analysis of radio propagation in indoor environments using institutional educational buildings and holding a capacity of carrying out prediction processes of RSSI, in the 2.4 GHz and 5 GHz bands. Findings: Based on obtained results, it was possible to evidence that the established models allow predicting the behavior the reception power and signal damping, with a level of confidence of 95%. Additionally, it was possible to verify, in the 5 GHZ band, the damping levels are bigger that those in 2.4 GHz band. Such aspect holds great importance when it comes to perform design processes of Wireless networks. Applications/Improvements: In future research papers, it would be significantly important to establish the expression for outdoor environments, supported in the Shadowing Path model. Keywords: Channels Allocation, Efficiency, Optimization, Performance, SINR, WLAN