Received Signal Strength Based Gait Authentication

Received Signal Strength Indicator (RSSI) is used in indoor positioning for measuring object distance to the base station. However, acquiring accurate RSSI values is challenging because wireless interference factors, such as multipath decline interference, make RSSI values of the same object fluctuate over time. Therefore, instead of a single RSSI, RSSI acquisition will collect a set of RSSI values from which the most moderate RSSI is derived. For this purpose, we propose an Enhanced Gaussian Mixture Model (EGMM) to derive a more precise RSSI for improving indoor positioning accuracy. EGMM enhances Gaussian Mixture Model (GMM) by applying Akaike information criterion (AIC) to determine the best K value for GMM to divide RSSI values into K sets representing signals from different paths. Then, EGMM identifies the most appropriate set of RSSI values to derive a more precise RSSI and thus improves the accuracy of indoor positioning. Our EGMM solution performs well in an open indoor space. The experiment is conducted with iBeacon devices, and the average error distance of EGMM is about 64% of those generated by existing Gaussian filtering. The average positioning error of EGMM is about 0.48 meter, which is adequate to indoor positioning accuracy.

We proposed a localization algorithm named LSARSSI for mobile node based on RSSI (received signal strength indicator) between locating sensor node with inertia module built-in and the single anchor. Instead of directly mapping RSSI values into physical distance, contrasting RSSI values received from anchor in different visited locations, LSARSSI utilizes the geometric relationship of perpendicular intersection to compute node positions. Given that the values of RSSI among two visited locations are equal, we regard that their distances to anchor node are equal. After obtaining several sets of such visited locations, the relative location of mobile node and anchor node can be calculated. Because of the limitations of LSARSSI, we put forward an improved algorithm named ILSARSSI. Our scheme uses only one location-known anchor which is useful in low density environment without using additional hardware. The simulations show that LSARSSI achieves high accuracy and ILSARSSI performs high stability and feasibility.

Towards a better indoor positioning system: A location estimation process using artificial neural networks based on a semi-interpolated database

Localisation methods based on the received signal strength indicator (RSSI) are widely used in wireless sensor networks (WSNs). They offer an energy reduced and cost-effective solutions for wireless localisation. Due to radio irregularity, RSSI localisation is vulnerable to multipath, shadowing, interference and environmental effects, which reduces the accuracy of localisation results. In this paper, a new distance measurement correction algorithm is presented, which is based on feedback filter and hardware characteristic. It implements the feedback filter with consideration of historical values of RSSI. This enables to predict possible values of RSSI and thus nodes positions. Hardware characteristics, such as multiplication and summation capabilities are explored to improve the localisation accuracy. Real experiments were carried out on panStamp sensor nodes at two different temperatures. The new proposed RSSI technique shows a reduced localisation error compared to the simple RSSI method.

The investigation of the received signal strength indicator (RSSI) and throughput performance in indoor environments for a 2.4 GHz transparent antenna is presented in this paper. The monopole transparent antenna with bi-directional radiation pattern is used in the measurement as the receiver. The RSSI results are taken from a laptop that equipped with AirMagnet wireless PC Card. The antenna throughput is measured with different RSSI levels of the highest, medium and lowest values. Moreover, the measured performance of direct electrical and solar power sources in an indoor environment is compared. The aim is to test the reliability of WiFi systems with solar panel. The measurement results show that throughput performance is proportional to the RSSI values. The RSSI values are between -60 dBm to -90 dBm with average throughput values between 8 and 7 Mbps. Ultimately, it is discovered that the same performance was obtained by using either one of the two power sources. Therefore solar source can be applied to power up the WiFi transparent antenna in real environment that eventually moving towards the green energy and sustainable technology.

LoRaWAN technology’s reliability is challenged by weather parameters, which can influence the communication channel design, especially when dealing with outdoor devices. We propose to analyze this effect by evaluating the relationship between the received signal strength indicator (RSSI) and different weather parameters, as well as its temporal changes. A rigorous statistical analysis of the RSSI sequences is conducted to assess if they could be represented by a specific statistical model. For this purpose, several models are investigated. The Artificial Intelligence (AI) algorithms cover machine learning (ML) and deep learning methods are appealing when dealing with time series forecasting. Nevertheless, the classical autoregressive integrated moving average (ARIMA) model can be an attractive alternative due to its simplicity. Therefore, this work proposes a comparative study of ARIMA, AI, and hybrid approaches to forecast the RSSI using weather parameters as regressors. The considered AI algorithms are the artificial neural network (ANN), support vector machine (SVM), random forest (RF), and Long Short-Term Memory (LSTM). Also, hybrid models are constructed, coupling the ARIMA with them. The models are evaluated in time series of RSSI, measured by eight different LoRaWAN transmitter nodes and considering the temperature, pressure, relative humidity, and rain as weather parameters. Our analysis reveals that temperature is the dominant factor among weather parameters, and negatively affects RSSI. The ARIMA model that uses only the temperature as a regressor provides consistently better fits than the ARIMA without regressors. Moreover, coupling the ARIMA with the temperature as a regressor and the ANN (ARIMA-ANN) is the best option among the pure AI and hybrid approaches. However, it provided accuracy measures very close to those obtained from the ARIMA model fitted in the first stage, with similar performance. Therefore, the ARIMA model considering the temperature is the most competitive alternative when analyzing RSSI measurements, with the advantage of being the most straightforward method. These results suggest that the RSSI from the analyzed LoRaWAN receiver nodes may not present nonlinear patterns and, considering several weather parameters, they are affected mainly by the outdoor temperature.

Wireless sensor networks have presented multiple opportunities, but also several challenges to researchers. Tools are needed to overcome these challenges. Received signal strength indication (RSSI) is one possible tool since it could theoretically be used to determine the distance between two communicating nodes. However in reality, in addition to distance, RSSI is affected by quite a few other factors, making distance estimates extremely unreliable. We carried out three different studies with MicaZ smart dust motes to examine in realistic operating conditions the effects of distance, transmission strength, antenna characteristics, environmental and radio interferences on the RSSI value. The studies show that while many of the effects are easily observable, it is difficult to recognize the causes solely based on the RSSI value. The high level of indeterminism in the environment causes RSSI to have little practical value in realistic deployments. Ill. 6, bibl. 6 (in English; summaries in English, Russian and Lithuanian).

This study aims to determine the results of the analysis of Quality of Service (QoS) parameter testing using LoRa SX1262 telemetry with a certain distance on Smart PDAM and determine the effectiveness of using LoRa SX1262 telemetry on data exchange on multipoint communication networks. The data collection technique uses observation and documentation techniques to obtain valid test results data. This study uses an quantitative descriptive analytical approach with a standard assessment of the TIPHON QoS parameters on the throughput, delay, packet loss, and RSSI (Received Signal Strength Indicator) values. Based on the results of the analysis of this study, it was found that the data can be received well when the RSSI value is above -90 dBm, the average throughput and packet loss values resulting from data transmission on a digital water meter system is inversely proportional to the amount of bandwidth, the average delay of sending LoRa SX1262 telemetry data using a 115200 baud rate of 513 milliseconds. The conclusion of this study is that multipont communication on digital meter data transmission is effectively used at a maximum distance of 300 meters with a bandwidth of 500 kHz.

Indoor positioning opens up opportunities for a wide range of applications, including active marketing, accessibility and security. Although GPS (Global Positioning System) is widely used for outdoor location, it is inaccurate and in some cases unavailable indoors. One of the solutions is to use Bluetooth Beacons to determine the distance between the device and the beacon indoors using the Received Signal Strength Indicator (RSSI). The location of the object in the environment can be determined using at least three beacons and methods such as trilateration. This work aims to evaluate the use of artificial neural networks (ANN) to determine the distance and location of the laptop in an indoor environment. A first experiment compares the Log Distance Path Loss (LDPL) model and the ANN to determine the distance between the beacon and a laptop. A second experiment compares which method is best to determine the position of the laptop in a room. The following methods were evaluated: a) trilateration with distance calculation using the LDPL method; b) trilateration with distance calculation using an ANN; and c) position determination using an ANN. The results show that RSSI values can vary due to obstacles and the position of the antenna between the beacon and the laptop.

Wireless Sensor Networks (WSN) are widely used in different monitoring systems. Given the distributed nature of WSN, a constantly increasing number of research studies are concentrated on some important aspects: maximizing network autonomy, node localization, and data access security. The node localization and distance estimation algorithms have, as their starting points, different information provided by the nodes. The level of signal strength is often such a starting point. A system for Received Signal Strength Indicator (RSSI) acquisition has been designed, implemented, and tested. In this paper, experiments in different operating environments have been conducted to show the variation of Received Signal Strength Indicator (RSSI) metric related to distance and geometrical orientation of the nodes and environment, both indoor and outdoor. Energy aware data transmission algorithms adjust the power consumed by the nodes according to the relative distance between the nodes. Experiments have been conducted to measure the current consumed by the node depending on the adjusted transmission power. In order to use the RSSI values as input for distance or location detection algorithms, the RSSI values can’t be used without intermediate processing steps to mitigate with the non-linearity of the measured values. The results of the measurements confirmed that the RSSI level varies with distance, geometrical orientation of the sensors, and environment characteristics.

Results of a study of the influence of manufacturing environment factors on wireless personal networks are presented in the paper. A classification of such factors is proposed, and the noise immunity of wireless personal networks is determined using a real production as an example. A method for evaluating noise immunity based on a received signal strength indicator (RSSI) is proposed. RSSI values can be obtained natively from almost any receiver and transmitter, which makes this method affordable compared to using network analyzers and other specialized equipment. In the conducted experiment the receiver and transmitter were located at a distance ranging from 0.5 to 25 m from each other. The act of signal transfer was carried out alternately under the influence of each production environment factor. Then measured RSSI values were analyzed and converted into the maximum permissible distance between the receiver and the transmitter in accordance with the proposed method. As a result, data on the influence of a production environment on the noise immunity of wireless personal networks was obtained. Nevertheless, it is concluded that the influence is not significant enough to decide against the use of wireless personal networks, since the influence of many factors can be offset by the use of mesh topology and a dense arrangement of receivers and transmitters. The results are particularly interesting in the context of manufacturing digitalization, where the wireless method of data transmission from field level sensors becomes preferable to the wired due to the requirement for flexibility and mobility of a production process.

Tracking a moving user in indoor environments using Bluetooth low energy beacons.

In wireless sensor network (WSN), position awareness (localization) of the sensor nodes is necessary to exploit the communication and to provide the meaningful information about their surroundings. Here Received Signal Strength Indicator (RSSI) technique is used as it requires no extra hardware and can be used for both indoor and outdoor environment. RSSI is having limitations such as high randomness due to fading and shadowing. This makes difficulties in establishing exact relationship between RSSI value and distance. In this paper we propose the method of creating database by using Split Range Technique (SRT) to estimate the approximate distance between two nodes. Using this database, position estimation has been done. For this Adaptive n-Triangle algorithm is implemented. Present results of RSSI shows the approximate positioning of the sensor node with the error estimation of about 5–10%. The experiment can be extended to show the predicted path of a moving object which is being identified by sensors.

Localization for visually impaired people in dynamically changing environments with unexpected hazards and obstacles is a current need. Many techniques have been discussed in the literature with respect to location-based services and techniques used for the positioning of devices. Time difference of arrival (TDOA), time of arrival (TOA) and received signal strength (RSS) have been widely used for the positioning but narrow band signals such as Bluetooth cannot efficiently utilize TDOA or TOA. Received signal strength indicator (RSSI) to measure RSS, has been found to be more reliable. RSSI measurement estimations depend heavily on the environmental interference. RSSI measurement estimations of Bluetooth systems can be improved either by improving the existing methodologies used to implement them or by using fusion techniques that employ Kalman filters to combine more than one RSSI method to improve the results significantly. This paper focuses on improving the existing methodology of measuring RSSI by proposing a new method using trilateration for localization of Bluetooth devices for visually impaired people. To validate the new method, class 2 Bluetooth devices (Blue Giga WT-12) were used with an evaluation board. The software required was developed in National Instruments LabView. The PCB was designed and manufactured as well. Experiments were then conducted, and surface plots of Bluetooth modules were obtained to show the signal interference and other environmental effects. Lastly, the results were discussed, and relevant conclusions were drawn.

Localization is one of the most challenging and important issues in wireless sensor networks (WSNs), especially if cost-effective approaches are demanded. In this paper, we present intensively discuss and analyze approaches relying on the received signal strength indicator (RSSI). The advantage of employing the RSSI values is that no extra hardware (e.g. ultrasonic or infra-red) is needed for network-centric localization. We studied different factors that affect the measured RSSI values. Finally, we evaluate two methods to estimate the distance; the first approach is based on statistical methods. For the second one, we use an artificial neural network to estimate the distance.