RSSI Based Localization of Bluetooth Devices for Visually Impaired

Received Signal Strength Indicator (RSSI) is simplest among various indoor localization methods. The main drawback is requiring a training phase to build fingerprints database, which is time consuming and sensitive to changes of test environment. Time based approaches such as Time of Arrival (ToA) and Time Difference of Arrival (TDoA) achieve better performance with some hardware expenditure, while location accuracy is significantly affected by multipath interference. By employing Super-resolution techniques (SRTs), multipath components can be estimated to improve localization precision, but limited to OFDM-based Multiple Input Multiple output (MIMO) wireless communication systems. Considering for general radio signals with strong correlation, we propose an enhanced TDoA approach based on RSS-assisted cross-correlation method. Simulation and experimental results demonstrate that our method is effective to mitigate multipath interference, verified by IEEE 802.11b WLAN. Compared with SRTs, our method achieves comparable localization precision, and has superiority in computational complexity and anti-noise performance.

An indoor localization system in wireless sensor networks has become a hot development area. Received signal strength indicator (RSSI)-based localization is a promising technique since it requires a relatively low configuration, battery power and easy control. However, the received signal strength is influenced by channel interference and propagation environments. This characteristic affects channel stability and location accuracy in RSSI-based localization. As a result, we propose a novel indoor localization system consisting of a preprocessing method and a post-processing method. To improve channel stability, the pre-processing method selects an optimal channel in terms of the smallest distance error. The optimal channel is less affected to IEEE 802.11. To develop location accuracy, the post-processing method performs maximum likelihood estimation-based location tracking scheme considering the reliability of the RSSI value measurement. We apply three methods to the existing MLE to improve the reliability of the RSSI value. By using this indoor localization system with preprocessing and post-processing, the location error can be reduced. We also proved the performance of the indoor localization system by conducting experiments in a real indoor environment.

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.

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.

A survey of active and passive indoor localisation systems

A low power adaptive controlled current-reusing received signal strength indicator (RSSI) is proposed for the human body communication (HBC). The proposed RSSI has three low power features. First, the power on controller (PoC) scheme is proposed to achieve the duty control of the RSSI. It significantly reduces the average power consumption of RSSI over 90%. Second, the current stacking scheme is adopted to share both eight rectifiers and eight amplifiers, composing the RSSI. By the current reusing technique, the power consumption of the RSSI is reduced to 45%. In addition, the reconfigurable LNA is used in the front-end of HBC TRX. The RSSI adaptively controls the gain and noise figure of the LNA to optimize the power consumption. The proposed RSSI occupies 0.85mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in 0.18-μm CMOS technology.

This paper proposes a system that estimates crowdedness, such as the numbers of people and wireless communication devices, from the received signal strength indication (RSSI), which is measured using sensor nodes already-deployed for a different purpose. For example, the system estimates crowdedness using sensor nodes of structural health monitoring. Our system consists of two parts: an RSSI synchronous measurement and a crowdedness estimation algorithm. The RSSI synchronous measurement enables us to measure and collect two kinds of RSSI by synchronized RSSI sampling: the inter-node RSSI and surrounding RSSI. The crowdedness estimation algorithm estimates the number of people from the collected inter-node RSSI and the number of wireless communication devices from the collected surrounding RSSI in a location. We evaluated the proposed system in the laboratory at Osaka University. The evaluation results demonstrate that the system estimates the presence or absence of people in the laboratory with approximately 92% accuracy and the number of people with approximately 79% accuracy, with errors of up to two people.

Position location estimation in sensor networks is a valuable supplement since it supports the deployment of location-based services. Sensor networks have changing conditions in the environment due to propagation issues, noise and placement of sensors, which represent challenges that position location algorithms must deal with. Accuracy of the location estimation technique is relevant since it allows minimizing positioning error. In indoor environments, propagation issues such as multipath signals, affect adversely the precision of the positioning algorithm. Also, the use of parameters such as time of arrival has a trade-off between the small distances that the signals traverse and the precision of the hardware used to capture such measurements. In this paper, we use received signal strength indicator (RSSI) to estimate the coordinates of individual sensors in an area of study. The RSSI parameter is measured and processed by a set of reference nodes installed in the area. We show that performance of the location estimation algorithm needs additional techniques to obtain improved accuracy rate. We develop additional techniques based on the use of polynomial interpolation and spline functions to balance propagation issues. These techniques help us to implement correcting factors that are used in the propagation model to compensate the RSSI measurements. We use these techniques to show how the positioning error is reduced in the area of study with simulations and measurements using sensors.

Location accuracy is one of the key indicators in measuring the performance of wireless sensor localization systems. Apparently, the localization performance is largely determined by ranging precision in the distance-based localization system. In the RSSI-based distance estimation techniques, the accuracy of their estimation is vulnerable to the change of RSSI measured values. As is known, the Received Signal Strength Indicator (RSSI) is environment dependent in certain extent. In addition, the received signal strength mainly depends on the emitting power of the sender. In this paper, the RSSI measured values are investigated when the sending power gets changed due to low supply voltage of the sender, and then a calibrated method is proposed. Experiments show that the proposed method can significantly mitigate distance estimation errors caused by low supply voltage, thus it can be widely used in battery powered wireless sensor localization systems.

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, $k$ -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.

Measuring the volume of the product is an important aspect of the production line for protecting against quality problems. Normally, the product volume can be measured by weighting the object through weighing machine. However, the large objects may not be comfortable for moving to the weighing machine. In general, the received signal strength indicator (RSSI) can be used to measure the existence of object according to the absorbed received signal strength. Moreover, machine learning is the popular technology which is used to classify the object and can be used to estimate the value of the targets. In this paper, we present a new technique for product quantification using RSSI-based machine learning techniques. Our method can support various sizes or types of products and can measure the volume of goods in a variety of placement patterns. Additionally, we consider four machine learning methods including linear regression, multinomial logistic regression, Naïve Bayes, and K-Nearest Neighbors to achieve the highest accuracy. As a result, the K-Nearest Neighbors is the most suitable technique to be used to estimate the volume from the RSSI.

The location method based on Received Signal Strength Indication (RSSI) ranging has the advantages of low development cost and simple implementation mechanism, and is the mainstream indoor location method nowadays. However, at present, there is a lack of systematic research on the characteristics of Access Point (AP) received signal strength, which cannot meet the signal strength characteristics required by specific indoor positioning environment. Detailedly, in this study, the indoor distribution characteristics of 2.4 GHz RSSI were analyzed experimentally from three influencing factors including antenna orientation of the receiver, type of wireless network card of the receiver and height difference of the transmitting and receiving antenna. The experimental results show that: (1) The RSSI value measured is the strongest When the antenna of the receiver is vertically oriented to the antenna of the transmitter, while the antenna is vertically backward to the transmitter, it is the opposite. The difference between the strongest signal and the weakest signal is 20%–25% at the same test point. (2) The network card with a large measurement range should be selected for data collection on the premise that the quantization step size is 1 and the smoothness is small. Avoiding using the network card for positioning with the maximum limit of measurement range is proposed also. (3) The path loss index of the ranging model is affected by the height difference of the antenna, resulting in a deviation of 0.1–0.2.

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.

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.

Nowadays, location awareness becomes the key to numerous Internet of Things (IoT) applications. Among the various methods for indoor localisation, received signal strength indicator (RSSI)-based fingerprinting attracts massive attention. However, the RSSI fingerprinting method is susceptible to lower accuracies because of the disturbance triggered by various factors from the indoors that influence the link quality of radio signals. Localisation using body-mounted wearable devices introduces an additional source of error when calculating the RSSI, leading to the deterioration of localisation performance. The broad aim of this study is to mitigate the user’s body shadowing effect on RSSI to improve localisation accuracy. Firstly, this study examines the effect of the user’s body on RSSI. Then, an angle estimation method is proposed by leveraging the concept of landmark. For precise identification of landmarks, an inertial measurement unit (IMU)-aided decision tree-based motion mode classifier is implemented. After that, a compensation model is proposed to correct the RSSI. Finally, the unknown location is estimated using the nearest neighbour method. Results demonstrated that the proposed system can significantly improve the localisation accuracy, where a median localisation accuracy of 1.46 m is achieved after compensating the body effect, which is 2.68 m before the compensation using the classical K-nearest neighbour method. Moreover, the proposed system noticeably outperformed others when comparing its performance with two other related works. The median accuracy is further improved to 0.74 m by applying a proposed weighted K-nearest neighbour algorithm.