Received Signal Strength Indicator Variation Research Articles

Development of an indoor corridor localization system based on a WCL method with corridor area selection and distance compensation

SummaryIn this paper, a received signal strength indicator (RSSI)‐based weighted centroid localization (WCL) method for indoor corridor scenarios is developed and tested. The contribution of the proposed system is that, to scope the area of an unknown target position, the possible corridor area where the target node should be located is automatically selected, and only reference nodes deployed in such an area are applied for position estimation. Additionally, to improve the estimation precision, distance values converted from measured RSSIs and used for the WCL are also compensated to alleviate the RSSI variation problem caused by physical environments. Therefore, our methods can save a number of reference nodes to be used, while localization accuracy can also be achieved. Experiments in the corridor environment using a 2.4 GHz IEEE 802.15.4 wireless sensor network with four reference nodes deployed in the test field dimension of 22 m × 9.3 m have been performed. Experimental results demonstrate that the proposed method, which requires only two reference nodes, outperforms the original WCL method with four reference nodes by 53.053%, as indicated by average localization errors. The results also reveal that the proposed method provides error distances lower than the WCL method for all test target positions, while all estimated positions fall within the corridor areas by 100%.

Open-Source Hardware-Based Three-Dimensional Positioning Device for Indoor Measurement and Positioning

Indoor private (local) wireless networks are considered key enablers for the industrial Internet of Things (IIoT). To ensure that a private network satisfies its own requirements and guarantees coverage, it is important to accurately measure the radio performance at the accurate position to further optimize the network. Additionally, as carrier frequencies increase, cellular network-based positioning becomes increasingly important, as it enables more accurate positioning. For example, centimeter-level accuracy is expected in beyond 5G systems owing to the sub-THz band carrier frequency. To accurately compare and assess various cellular network-based positioning methods, positioning devices, which can move along the same trajectory more accurately than at the centimeter-level, are required. To this end, we implemented a three-dimensional (3D) positioning device developed from open-source hardware, which is generally used to implement computer numerical control (CNC) machines and 3D printers. The implemented 3D pointing device is cost-effective and portable and can be freely configurable and resized in one, two, and three dimensions owing to the use of open-source hardware. The received signal strength indicator (RSSI) employing the LoRa module can be obtained using the implemented device. Using a very low carrier frequency system, it is possible to accurately measure the RSSI variations due to multipath propagation. After demonstrating the reproducibility of the data, the effectiveness of the implemented device was confirmed by showing that the RSSI variation and fading correlation coefficient are close to the Rayleigh fading variation.

User Equipment Tracking for a Millimeter Wave System Using Vision and RSSI

In a mobile millimeter wave (mmWave) communication system, blockages cause disconnections or serious degradation of communications. Several techniques have been proposed to control radio links between multiple base stations based on blockage prediction using camera images to avoid these problems. However, blockage prediction requires continuously determining the position of user equipment (UE) with decimeter precision, which is difficult when sensors and resources on the UE side are not available, and there are many moving objects around the UE. To resolve this problem, we propose a UE tracking method that uses a received signal strength indicator (RSSI) and RGB-D camera images from the base station. The proposed method consists of a combination of visual tracking and reidentification of the UE using radio information and camera images. We use the synchronization of the RSSI variation with the occlusion on the image by movements of the UE and objects for reidentification. We evaluated the proposed method experimentally in an outdoor environment by simulating a communication area formed by mmWave band base stations. The proposed method achieved an 11.5% improvement in tracking accuracy compared with conventional visual tracking.

QA-kNN: Indoor Localization Based on Quartile Analysis and the kNN Classifier for Wireless Networks.

Considering the variation of the received signal strength indicator (RSSI) in wireless networks, the objective of this study is to investigate and propose a method of indoor localization in order to improve the accuracy of localization that is compromised by RSSI variation. For this, quartile analysis is used for data pre-processing and the k-nearest neighbors (kNN) classifier is used for localization. In addition to the tests in a real environment, simulations were performed, varying many parameters related to the proposed method and the environment. In the real environment with reference points of 1.284 density per unit area (RPs/m2), the method presents zero-mean error in the localization in test points (TPs) coinciding with the RPs. In the simulated environment with a density of 0.327 RPs/m2, a mean error of 0.490 m for the localization of random TPs was achieved. These results are important contributions and allow us to conclude that the method is promising for locating objects in indoor environments.

A Comparative Study of RSSI-Based Localization Methods: RSSI Variation Caused by Human Presence and Movement

In a received signal strength indicator (RSSI) based localization system, the presence or movement of humans is one of the major effects causing RSSI variation. Using RSSI data during such a situation to estimate the target position can give large errors. Regarding this problem, in this paper, a comparison of several RSSI-based localization methods with and without human presence and movement were investigated experimentally. The major contribution of this work is that the well-known and widely used RSSI-based localization methods presented in the literature, including the min–max, the trilateration, the weighted centroid localization (WCL), and the relative span exponential weighted localization (REWL) methods, were tested. Thus, how human presence or absence influences the accuracy of these methods, and which methods show the best estimates while tolerating human movement effects can be investigated. The experiments were carried out in a laboratory and in a parking building. The results demonstrate that, without human movement effects, all methods perform very similarly. In contrast, human movements significantly increased estimation errors.Here, the maximum distance errors of the min–max, the trilateration, the WCL, and the REWL are 1.34 m, 4.09 m, 1.25 m, and 1.24 m, respectively. Obviously, the min–max, the WCL (with an optimal parameter), and the REWL (with the optimal parameter) can well tolerate the RSSI variations caused by human movements and provide significantly better accuracy than the trilateration method. Based on these findings, all the mentioned localization methods should be further improved to deal with the human movement problem.

Human movement effects on the performance of the RSSI-based trilateration method: adaptive filters for distance compensation

In this paper, an experimental study of human movement effects in indoor wireless networks on the performance of the received signal strength indicator (RSSI)-based trilateration method is presented. The contribution of this work is that how the RSSI fluctuation caused by human movements nearby a target node influences the estimation accuracy of the trilateration method is studied. Additionally, efficient adaptive filters which can observe RSSI variation levels and select appropriate inputs (i.e., distance values converted from RSSI values) are proposed for handling the RSSI variation problem and compensating the position estimation error. Experiments using low-cost 2.4-GHz wireless nodes have been set and tested in a laboratory room. Results demonstrate that, during the human blocking the communication link between the transmitter and the receiver, RSSI data are fluctuated and the trilateration method shows a large estimation error. Here, the localization accuracy significantly depends on the RSSI variation level. The experimental results also indicate that, by applying our proposed solutions, they can automatically provide appropriate inputs for the trilateration method. The estimation error decreases by 43.76% and 46.28% for our test scenarios.

Adaptive Filtering Methods for RSSI Signals in a Device-Free Human Detection and Tracking System

In a device-free human detection and tracking system using a received signal strength indicator (RSSI), the change in the RSSI pattern is monitored and analyzed to detect and track the human movement. The variation in measured RSSI signals is one of the major effects leading to significant detection and tracking error. To handle such a research problem, in this paper, we propose adaptive RSSI filtering methods designed by considering both the detection and tracking accuracy and the computational complexity. The novelty of our proposed filtering methods is that, to reduce the computational complexity, the measured RSSI input values are automatically filtered only when they have high variation levels; an appropriate threshold is set and used for the decision. Additionally, to increase the detection and tracking accuracy, the measured RSSI input values with different variation levels are filtered with different filtering levels adaptively. The proposed filtering methods are verified by the experiments, which have been carried out in an indoor environment. Various human movement patterns with different directions and speeds are tested. The experimental results show that, with our test scenarios, the proposed filtering methods can appropriately reduce the RSSI variation. They provide good detection and tracking accuracy, which is measured by the number of times the system can detect and track the human with the correct zone. The computational complexity measured by the number of mathematical operations, used by the proposed methods, is lower than comparative filtering methods.

Implementation and test of an RSSI-based indoor target localization system: Human movement effects on the accuracy

The movement of humans in wireless networks is one of major effects leading to significant received signal strength indicator (RSSI) variation. Using fluctuated RSSI on estimating the target position in the RSSI-based indoor localization system can give large error and poor decision of the system. In this paper, how the human movement affects the accuracy of an implemented indoor target localization system is explored by experiments, and a proposed simple RSSI filtering solution as the guideline solution to directly handle such a research problem is also presented. For our purpose, firstly, the RSSI-based indoor target localization system, which consists of design communication operations for measuring the RSSI in the wireless network and selected well-known localization methods (i.e. the min-max and the trilateration methods) for estimating the target position, is implemented and tested. Secondly, selected well-known filtering methods (i.e. the moving average and the exponentially weighted moving average filters) and the span thresholding filter (i.e. the proposed solution) are applied for reducing the RSSI variation and the estimated position error caused by the human movement. Our experiments have been carried out in an indoor environment. An LPC2103F microcontroller interfacing with a 2.4 GHz CC2500 RF module is developed and employed as the wireless node. Experimental results reveal that the estimated position error determined by the min-max and the trilateration methods significantly increases during the human movement, and converts according to human movement patterns and numbers of movement people. Also, the results demonstrate that by applying the moving average filter with a high window size and the exponentially weighted moving average filter with an optimal weighting factor to raw RSSI data, the estimated position error is not much improved. In contrast, the span thresholding filter gives better results and can directly cope with the human movement problem. In average, the localization error and the standard deviation decrease 11.921% and 42.086% in the case of the min-max method, and they decrease 44.535% and 87.154% in the case of the trilateration method.

A System for Detection and Tracking of Human Movements Using RSSI Signals

A device-free human detection and tracking system using a received signal strength indicator (RSSI) for an indoor environment is presented in this paper. The proposed system has two major functions: a wireless communication system and a human detection and tracking system. The first function is developed for measuring and collecting RSSI signals affected by human presence and movement, while the second function is developed for detecting and tracking the human using a predefined threshold and a zone selection method. The novelty of our proposed system is that the communication protocol can avoid signal interference and packet loss in the network, and the detection and tracking method can specify an actual zone that the human is present by taking an optimal predefined threshold and a level of RSSI variation in each zone into consideration. The proposed system is verified by experiments, and various human movement patterns with different directions and speeds are tested. The experimental results show that the proposed communication protocol can significantly provide communication reliability, and the proposed method can properly detect and track human movements. The packet delivery ratio indicating communication reliability is almost 100%. Detection and tracking accuracy measured by the number of times the method can detect and track the human with the correct zone is almost 100% in all cases of one man movements.

An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

This paper proposes an efficient and effective WiFi fingerprinting-based indoor localization algorithm, which uses the Received Signal Strength Indicator (RSSI) of WiFi signals. In practical harsh indoor environments, RSSI variation and hardware variance can significantly degrade the performance of fingerprinting-based localization methods. To address the problem of hardware variance and signal fluctuation in WiFi fingerprinting-based localization, we propose a novel normalized rank based Support Vector Machine classifier (NR-SVM). Moving from RSSI value based analysis to the normalized rank transformation based analysis, the principal features are prioritized and the dimensionalities of signature vectors are taken into account. The proposed method has been tested using sixteen different devices in a shopping mall with 88 shops. The experimental results demonstrate its robustness with no less than 98.75% correct estimation in 93.75% of the tested cases and 100% correct rate in 56.25% of cases. In the experiments, the new method shows better performance over the KNN, Naïve Bayes, Random Forest, and Neural Network algorithms. Furthermore, we have compared the proposed approach with three popular calibration-free transformation based methods, including difference method (DIFF), Signal Strength Difference (SSD), and the Hyperbolic Location Fingerprinting (HLF) based SVM. The results show that the NR-SVM outperforms these popular methods.

Audit of a Real Wi-Fi Deployment to Provide Data, VoIP Communications and an IIoT Item Location Service

As the number of Access Points and stations sharing the unlicensed ISM (Industrial Scientific Medical) bands increases, interference diminishes the theoretical performance of 802.11 standard. This paper presents results of the performance of an IEEE 802.11b/g network in a public facility infrastructure which needed to deploy more than a thousand access points to provide an IIoT (Industrial Internet of the Things) item location service. As a first step to better analyze such network characteristics, this work studies the channel occupancy, the RSSI (Received Signal Strength Indicator) variation and the throughput of a static and mobile station. The study also analyzes the performance of Voice over IP over the wireless network. The results reveal the inefficient use of the wireless medium in a large 802.11 network due to multiple radio propagation conditions.

An Indoor Pedestrian Positioning Method Using HMM with a Fuzzy Pattern Recognition Algorithm in a WLAN Fingerprint System.

With the rapid development of smartphones and wireless networks, indoor location-based services have become more and more prevalent. Due to the sophisticated propagation of radio signals, the Received Signal Strength Indicator (RSSI) shows a significant variation during pedestrian walking, which introduces critical errors in deterministic indoor positioning. To solve this problem, we present a novel method to improve the indoor pedestrian positioning accuracy by embedding a fuzzy pattern recognition algorithm into a Hidden Markov Model. The fuzzy pattern recognition algorithm follows the rule that the RSSI fading has a positive correlation to the distance between the measuring point and the AP location even during a dynamic positioning measurement. Through this algorithm, we use the RSSI variation trend to replace the specific RSSI value to achieve a fuzzy positioning. The transition probability of the Hidden Markov Model is trained by the fuzzy pattern recognition algorithm with pedestrian trajectories. Using the Viterbi algorithm with the trained model, we can obtain a set of hidden location states. In our experiments, we demonstrate that, compared with the deterministic pattern matching algorithm, our method can greatly improve the positioning accuracy and shows robust environmental adaptability.

Experimental Evaluation of a Simple Outlier RSSI Data Rejection Algorithm for Location Estimation in Wireless Sensor Networks

The ability to estimate a target location is essential in many applications of wireless sensor networks. Received signal strength indicator (RSSI)-based maximum likelihood (ML) method in a wireless sensor network usually requires a pre-determined statistical model on the variation of RSSI in a sensing area and uses it as an ML function when estimating the location of a target in the sensing area. However, when estimating the location of a target, due to several reasons, we often measure the RSSIs which do not follow the statistical model, in other words, which are outlier on the statistical model. As the result, the effect of the outlier RSSI data worsens the estimation accuracy. If the wireless sensor network has a lot of sensor nodes, we can improve the estimation accuracy intentionally rejecting such outlier RSSIs. In this paper, we propose a simple outlier RSSI data rejection algorithm for an ML location estimation. The proposed algorithm iteratively eliminates the anchor nodes which measure outlier RSSIs. As compared with the location estimation methods with previously proposed outlier RSSI data rejection algorithms, our proposed method performs better with much less computational complexity.