Received Signal Strength Variations Research Articles

Reduction of the received signal strength variation with distance using averaging over multiple heights and frequencies

As a simple and inexpensive channel characteristic, received signal strength (RSS) finds extensive usage in localization applications. However, the quick changes in signal strength impact the localization precision. By averaging over access points (APs) with multiple frequencies and/or heights, this article suggests a novel approach to lowering RSS fluctuation. Initially focused on the plane-earth loss model, the study was later extended to include a multipath indoor propagation scenario that was simulated. We used ray-tracing software to model the indoor propagation situation. This research takes into account the results of three distinct methods for averaging RSS: height averaging, frequency averaging, and hybrid frequency and height (FH) averaging, which combines the two. We discovered that the Height-only strategy considerably decreased the RSS variation with distance for both settings we looked at. Using the frequency-only method even further reduced the variation. Using the Hybrid FH technique greatly enhances the results. Root mean square error values of 4.427 dB, 3.70 dB, and 3.5 dB, respectively, are provided for the averaging approaches and the ideal scenario in which no variance occurs. Another finding is that averaging with APs that have double the height or frequency will not improve the RSS distance variation.

DeepWiPos: A Deep Learning-Based Wireless Positioning Framework to Address Fingerprint Instability

Wireless fingerprint localization technologies have drawn significant attention because of their ubiquitous infrastructure and low energy consumption. However, they often suffer from the Received Signal Strength (RSS) instability (i.e., RSS variations across heterogeneous devices and RSS fluctuations over time) and fingerprint spatial ambiguity issues. Although various deep learning-based methods have been proposed recently, most of them only pursue remarkable accuracy in an ideal environment and escape the above-mentioned problems. The co-existence of these factors would make most existing methods suboptimal or invalid when applied in real scenarios. In this paper, to address the RSS instability, we define the fingerprint spatial gradient (FSG), which can convert the vulnerable and unstable fingerprints to stable relative values but lose absolute information for positioning. Next, to relieve the fingerprint instability and model the temporal sequential dependencies in a trajectory, we propose a Long Short-Term Memory network (LSTM)-based framework to fuse FSG and RSS, where an attention module is used to guide the fusion according to their contributions to the result. Finally, the beam search strategy is used to search for the optimal solution from outputs of the LSTM, eliminating the spatial ambiguity of fingerprints. The experiments show that DeepWiPos reduces average positioning error by 23.44% and 22.18% on Bluetooth and Wi-Fi, respectively when compared with the state-of-the-art.

SELE: RSS-Based Siamese Embedding Location Estimator for a Dynamic IoT Environment

Wireless Fidelity (Wi-Fi)-received signal strength (RSS) fingerprints are extensively used for localization in an indoor Internet of Things (IoT) environment. However, RSS fingerprinting-based localization methods face two major challenges. First, the labor-intensive and time-consuming task of constructing a fingerprinting database with a large number of RSS samples for offline training. Second, the RSS values vary due to device heterogeneity and temporal variance caused by the change in access points (APs), device orientation, and environmental factors. These dynamic RSS variations in the online phase decrease localization accuracy. This article proposes a Siamese embedding-based localization method for a dynamic IoT environment. The proposed method provides high localization accuracy with limited RSS samples by utilizing our proposed distance-based sampling method. Also, the Siamese embeddings capture the spatial relations between locations and remain persistent even with the RSS variations in the online phase. This is possible with the help of a lightweight, offline fine-tuning method that requires minimal RSS samples. The proposed method is validated on three real IoT testbeds with RSS variations. It outperforms the existing state-of-the-art methods on two testbeds along with the comparable results on the third.

AI-Based Channel Prediction in D2D Links: An Empirical Validation

Device-to-Device (D2D) communication propelled by artificial intelligence (AI) will be an allied technology that will improve system performance and support new services in advanced wireless networks (5G, 6G and beyond). In this paper, AI-based deep learning techniques are applied to D2D links operating at 5.8 GHz with the aim at providing potential answers to the following questions concerning the prediction of the received signal strength variations: i) how effective is the prediction as a function of the coherence time of the channel? and ii) what is the minimum number of input samples required for a target prediction performance? To this end, a variety of measurement environments and scenarios are considered, including an indoor open-office area, an outdoor open-space, line of sight (LOS), non-LOS (NLOS), and mobile scenarios. Four deep learning models are explored, namely long short-term memory networks (LSTMs), gated recurrent units (GRUs), convolutional neural networks (CNNs), and dense or feedforward networks (FFNs). Linear regression is used as a baseline model. It is observed that GRUs and LSTMs present equivalent performance, and both are superior when compared to CNNs, FFNs and linear regression. This indicates that GRUs and LSTMs are able to better account for temporal dependencies in the D2D data sets. We also provide recommendations on the minimum input lengths that yield the required performance given the channel coherence time. For instance, to predict 17 and 23 ms into the future, in indoor and outdoor LOS environments, respectively, an input length of 25 ms is recommended. This indicates that the bulk of the learning is done within the coherence time of the channel, and that large input lengths may not always be beneficial.

Novel Differential $r$-Vectors for Localization in IoT Networks

Wireless fidelity received signal strength (RSS) fingerprints are widely used for localization. However, device heterogeneity and temporal variation in the RSS values are the bottlenecks for accurate localization in the Internet of Things (IoT) networks. This letter addresses these challenges by proposing a novel differential r-vectors which are the device-invariant signature of a particular location and are invariant, even with temporal RSS variations. The proposed network addresses the vanishing gradient problem which occurs in existing deep learning based localization methods, and hence, the localization accuracy improves. We evaluate the proposed method on two real-world datasets comprising device heterogeneity and temporal RSS variations. Additionally, the proposed method outperforms state-of-the-art fingerprinting-based methods that address these issues.

Matrix Factorization-Based RSS Interpolation for Radio Environment Prediction

This letter proposes matrix factorization (MF) based interpolation of received signal strength (RSS) from a transmitter mounted on moving robot in factory environment. For realizing the reliable wireless communication, machine learning based channel prediction methods have been intensively studied in the past decade. However, some traffic models will make the observation of RSS sequence be intermittent, and the missing values must be interpolated before input to the predictor. Classical interpolation such as linear interpolation cannot appropriately estimate the missing values because the result of the interpolation depends on the observation time. In this letter, we propose to apply an MF-based interpolation technique to RSS interpolation in order to restore the true RSS variation pattern. Moreover, the basic MF-based interpolation is improved by introducing a smoothing term in an objective function to represent the smooth variation of the RSS sequence. The simulation results show that the MF-based interpolation can improve the prediction accuracy of the machine learning based channel prediction method.

Identity-Based Attack Detection and Classification Utilizing Reciprocal RSS Variations in Mobile Wireless Networks

Identity-based attacks (IBAs) are one of the most serious threats to wireless networks. Recently, there is an increasing interest in using the received signal strength (RSS) to detect IBAs in wireless networks. However, current schemes tend to generate excessive false alarms in the mobile scenario. In this paper, we propose a stronger Reciprocal Channel Variation-based Identification and classification (RCVIC) scheme for the mobile wireless networks, which exploits the reciprocity of the wireless fading channel and RSS variations naturally incurred by mobility to improve the detection performance. Different from current schemes only detect IBAs, RCVIC scheme conducts a multi-stage detection processes. If the IBAs are detected, RCVIC scheme partitions the received frames into two classes. The frames in the same class should be sent from the same senders, which could benefit the further analysis, such as network forensics, attacker localizing and trajectory analysis, etc. The feasibility of RCVIC are numerically evaluated through theoretical analysis and simulations. It is further validated through experiments using off-the-shelf 802.11 devices under different attacking patterns in real indoor and outdoor mobile scenarios.

A Model-Based RF Hand Motion Detection System for Shadowing Scenarios

With the explosive growth of mobile computing, new modes of human-computer interaction (HCI) are emerging and becoming feasible. Compared to vision-based systems that require lighting, radio frequency (RF)-based hand motion detection systems are becoming more popular in HCI applications. In real RF hand motion detection scenarios, the line-of-sight between the transmitter (Tx) and receiver (Rx) is usually blocked. Hence, shadowing significantly affects the detection accuracy. To design better RF hand motion detection systems, we propose a simple diffraction and interference model (DIM) to interpret the received signal strength (RSS) variation caused by hand motions in the shadowing scenario. Based on theories of knife-edge diffraction and mutual radio interference, DIM provides a simple theoretical foundation for analyzing the RSS variation with hand size, signal frequency, and Tx-Rx distance. Furthermore, a model-based RF hand motion detection system benefiting from DIM is presented. Unlike existing systems that require a large number of motion features to train a motion classifier, the model-based system achieves training-free motion classification, which has potential for hand motion detection on a real-time basis. Empirical data collected from a vector network analyzer validate our system as well as demonstrate a simple diffraction model can help hand motion detection processing for commonly growing HCI applications.

Denoising-Contractive Autoencoder for Robust Device-Free Occupancy Detection

Device-free occupancy detection is very important for certain Internet of Things applications that do not require the user to carry a receiver. This paper achieves the device-free occupancy detection with RF fingerprinting, which labels each zone with a $2M$ -dimensional fingerprint vector. Specifically, the fingerprint vector consists of received signal strength (RSS) values measured from $M$ Bluetooth low energy (BLE) beacons and also their corresponding temporal RSS variations. However, the unreliable RSS values caused two common issues with the fingerprint vector: 1) noise and 2) sparsity. To this end, we propose denoising-contractive autoencoder (DCAE) to jointly deal with these two issues, by learning a robust fingerprint prior to device-free occupancy detection. We validate the performance of our proposed DCAE with large-scale real-world datasets. The experimental results indicate the substantial performance gain of our proposed DCAE in comparison with state-of-the-art autoencoders. In particular, the classifier trained using the fingerprints learned by our proposed DCAE is able to maintain at least 90% accuracy when the noise factor or sparsity ratio increases to 0.6 and 0.5, respectively.

Exploring the Laplace Prior in Radio Tomographic Imaging with Sparse Bayesian Learning towards the Robustness to Multipath Fading.

Radio tomographic imaging (RTI) is a technology for target localization by using radio frequency (RF) sensors in a wireless network. The change of the attenuation field caused by the target is represented by a shadowing image, which is then used to estimate the target’s position. The shadowing image can be reconstructed from the variation of the received signal strength (RSS) in the wireless network. However, due to the interference from multi-path fading, not all the RSS variations are reliable. If the unreliable RSS variations are used for image reconstruction, some artifacts will appear in the shadowing image, which may cause the target’s position being wrongly estimated. Due to the sparse property of the shadowing image, sparse Bayesian learning (SBL) can be employed for signal reconstruction. Aiming at enhancing the robustness to multipath fading, this paper explores the Laplace prior to characterize the shadowing image under the framework of SBL. Bayesian modeling, Bayesian inference and the fast algorithm are presented to achieve the maximum-a-posterior (MAP) solution. Finally, imaging, localization and tracking experiments from three different scenarios are conducted to validate the robustness to multipath fading. Meanwhile, the improved computational efficiency of using Laplace prior is validated in the localization-time experiment as well.

Enhancing the Accuracy and Robustness of a Compressive Sensing Based Device-Free Localization by Exploiting Channel Diversity.

As an emerging and promising technique, device-free localization (DFL) estimates target positions by analyzing their shadowing effects. Most existing compressive sensing (CS)-based DFL methods use the changes of received signal strength (RSS) to approximate the shadowing effects. However, in changing environments, RSS readings are vulnerable to environmental dynamics. The deviation between runtime RSS variations and the data in a fixed dictionary can significantly deteriorate the performance of DFL. In this paper, we introduce ComDec, a novel CS-based DFL method using channel state information (CSI) to enhance localization accuracy and robustness. To exploit the channel diversity of CSI measurements, the DFL problem is formulated as a joint sparse recovery problem that recovers multiple sparse vectors with common support. To solve this problem, we develop a joint sparse recovery algorithm under the variational Bayesian inference framework. In this algorithm, dictionaries are parameterized based on the saddle surface model. To adapt to the environmental changes and different channel characteristics, dictionary parameters are modelled as tunable parameters. Simulation results verified the superior performance of ComDec as compared with other state-of-the-art CS-based DFL methods.

An Enhanced Indoor Positioning Method Based on Wi-Fi RSS Fingerprinting

In WiFi-based indoor positioning, the received signal strength (RSS) measurements are commonly used to estimate the mobile user location. However, these measurements significantly fluctuate over time and are susceptible to human movement, multipath and Non-Line-of-Sight (NLOS) propagation, which reduce the location accuracy. In this paper, an enhancement positioning method based on the nearest neighbor algorithm is proposed. The distribution of the RSS samples recorded from several Access Points (APs) are used rather than their average, for reducing the location errors introduced by the RSS variations and the multipath problem. The proposed algorithm, named the Nearest K th Nearest Neighbor (NK-NN) is experimentally evaluated and compared to other powerful methods. The results show that the proposed method outperforms these methods.

Trilateration Approaches for Indoor Wi-Fi Positioning

In smartphones several sensors and receivers are embedded which enable positioning in Location-based Services and other navigation applications. They include GNSS receivers and Wireless Fidelity (Wi-Fi) cards as well as inertial sensors, such as accelerometers, gyroscope and magnetometer. In this paper, indoor Wi-Fi positioning is studied based on trilateration. Three methods are investigated which are a resection, a calculation of the center of gravity point and a differential approach. The first approach is a commonly employed resection using the ranges to the Wi-Fi Access Points (APs) as radii and intersect the circles around the APs. In the second method, the center of gravity in a triangle of APs is calculated with weighting of the received signal strength (RSS) of the Wi-Fi signals. The third approach is developed by analogy to Differential GNSS (DGNSS) and therefore termed Differential Wi-Fi (DWi-Fi). Its advantage is that a real-time modeling of the temporal RSS variations and fluctuations is possible. For that purpose, reference stations realized by low-cost Raspberry Pi units are deployed which serve at the same time as APs. The experiments conducted in a laboratory and entrance of an office building showed that position deviations from the ground truth of around 2 m are achievable with the second and third method. Thereby the positioning accuracies depend mainly on the geometrical point location in the triangle of APs and reference stations and the RSS scan duration.

Online Learning-Based WIFI Radio Map Updating Considering High-Dynamic Environmental Factors

Indoor localization has been recognized as a promising research around the world, and fingerprint-based localization method which leverages WIFI Received Signal Strength (RSS) has been extensively studied since widespread deployment of Access Points (APs) makes WIFI signals omnipresent and easily be obtained. A primary weakness of WIFI-based fingerprinting localization approach lies in its vulnerability under environmental changes and alteration of AP deployment. Despite some studies focus on dealing with effects of AP alterations and low-dynamic environmental factors, such as humidity, temperature, etc., influences of high-dynamic factors, such as changes of crowds' density and position, on WIFI radio map have not been sufficiently studied. In this work, we propose OWUH, an Online Learning-based WIFI Radio Map Updating service considering influences of high-dynamic factors. OWUH utilizes sensors in smart phones as the source of RSS datasets, and it combines historical and newly collected RSS data and purposeful probe data as dataset to incrementally update radio map, which means, compared with traditional methods, the OWUH approach requires a smaller number of RSS data for frequent updating of radio map. Moreover, in order to further enlarge our dataset, we take static data and low-dynamic data into account. An improved online learning method is proposed to recognize periodic pattern and update current radio map. Extensive experiments with 15 volunteers across 10 days indicate that OWUH effectively accommodates RSS variations over time and derives accurate prediction of fresh radio map with mean errors of less than 5dB, outperforming existing approaches.

Research on RSS Data Optimization and DFL Localization for Non-Empty Environments.

Device-free localization (DFL) is a new technique which can estimate the target location through analyzing the shadowing effect on surrounding radio frequency (RF) links. In a relatively complex environment, the influences of random disturbance and the multipath effect are more serious. There are kinds of noises and disturbances in the received signal strength (RSS) data of RF links and the data itself can even be distorted, which will seriously affect the DFL accuracy. Most of the common filtering methods adopted in DFL field are not targeted and the filtering effects are unstable. This paper researches the characteristics of RSS data with random disturbances and proposes two-dimensional double correlation (TDDC) distributed wavelet filtering. It can filter out the random disturbances and noise while preserving the RSS fluctuations which are helpful for the DFL, thus improving the quality of RSS data and localization accuracy. Furthermore, RSS variation rules for the links are different in complex environments and hence, it is difficult for the collected training samples to cover all possible patterns. Therefore, a single machine learning model with poor generalization ability finds it difficult to achieve ideal localization results. In this paper, the Adaboost.M2 ensemble learning model based on the Gini decision tree (GDTE) is proposed to improve the generalization ability for unknown patterns. Extensive experiments performed in two different drawing rooms demonstrate that the TDDC distributed wavelet filtering and the GDTE localization model have obvious advantages compared with other methods. The localization accuracy rates of 87% and 95% can be achieved respectively in the two environments.

Trilateration approaches for seamless out-/indoor GNSS and Wi-Fi smartphone positioning

Abstract More and more sensors and receivers are found nowadays in smartphones which can enable and improve positioning for Location-based Services and other navigation applications. Apart from inertial sensors, such as accelerometers, gyroscope and magnetometer, receivers for Wireless Fidelity (Wi-Fi) and GNSS signals can be employed for positioning of a mobile user. In this study, three trilateration methods for Wi-Fi positioning are investigated whereby the influence of the derivation of the relationship between the received signal strength (RSS) and the range to an Access Points (AP) are analyzed. The first approach is a straightforward resection for point determination and the second is based on the calculation of the center of gravity in a triangle of APs while weighting the received RSS. In the third method a differential approach is employed where as in Differential GNSS (DGNSS) corrections are derived and applied to the raw RSS measurements. In this Differential Wi-Fi (DWi-Fi) method, reference stations realized by low-cost Raspberry Pi units are used to model temporal RSS variations. In the experiments in this study two different indoor environments are used, one in a laboratory and the second in the entrance of an office building. The results of the second and third approach show position deviations from the ground truth of around 2 m in dependence of the geometrical point location. Furthermore, the transition between GNSS positioning outdoors and Wi-Fi localization indoors in the entrance area of the building is studied.

Indoor localization strategy based on fault-tolerant area division for shipboard surveillance

Abstract Localization based on wireless sensor networks has been shown to be a promising application in ships. Although a considerable number of algorithms have been designed for low-overhead and high-accuracy localization, some problems have been ignored, such as interference in the shipboard environment and the method of using anchor-deploying. In this paper, we present a method for range-free localization called fault-tolerant area division (FAD) to deploy and divide the area in which precise indoor localization is required. Despite the limitations with respect to shipboard environmental interference, sensing irregularity, received signal strength variation, and other unavoidable factors, FAD has been shown to be reliable by improving the fault-tolerant mechanism. In addition, to address the scheme of anchor-node placement, which complicates the localization performance, this paper presents a new deployment strategy for the anchor nodes using optimization methods. This paper presents and analyzes an enhancement method using a series of simulations and real-world ship experiments. The result shows that a well-organized deployment and a fault-tolerant mechanism can make such localization method more reliable and compatible.

Detector Based Radio Tomographic Imaging

Received signal strength based radio tomographic imaging is a popular device-free indoor localization method which reconstructs the spatial loss field of the environment using measurements from a dense wireless network. Existing methods achieve high accuracy localization using a complex system with many sophisticated components. In this work, we propose an alternative and simpler imaging system based on link level occupancy detection. First, we introduce a single-bounce reflection based received signal strength model, which allows relating received signal strength variations to a large region around the link-lines. Then, based on the model, we present methods for all system components including a classifier, a detector, a back-projection based reconstruction algorithm, and a localization method. The introduced system has the following advantages over the other imaging based methods: i) a simple image reconstruction method that is straightforward to implement; ii) significantly lower computational complexity such that no floating point multiplication is required; iii) each link's measured data are compressed to a single bit, providing improved scalability; and iv) physically significant and repeatable parameters. The proposed method is validated using measurement data. Results show that the proposed method achieves the above advantages without loss of accuracy compared to the other available methods.

A Study on Particle Filter based on KLD-Resampling for Wireless Patient Tracking

In this paper, we consider a typical health care system via the help of Wireless Sensor Network (WSN) for wireless patient tracking. The wireless patient tracking module of this system performs localization out of samples of Received Signal Strength (RSS) variations and tracking through a Particle Filter (PF) for WSN assisted by multiple transmit-power information. We propose a modified PF, Kullback-Leibler Distance (KLD)-resampling PF, to ameliorate the effect of RSS variations by generating a sample set near the high-likelihood region for improving the wireless patient tracking. The key idea of this method is to approximate a discrete distribution with an upper bound error on the KLD for reducing both location error and the number of particles used. To determine this bound error, an optimal algorithm is proposed based on the maximum gap error between the proposal and Sampling Important Resampling (SIR) algorithms. By setting up these values, a number of simulations using the health care system’s data sets which contains the real RSSI measurements to evaluate the location error in term of various power levels and density nodes for all methods. Finally, we point out the effect of different power levels vs. different density nodes for the wireless patient tracking.

A Study on Particle Filter based on KLD-Resampling for Wireless Patient Tracking

In this paper, we consider a typical health care system via the help of Wireless Sensor Network (WSN) for wireless patient tracking. The wireless patient tracking module of this system performs localization out of samples of Received Signal Strength (RSS) variations and tracking through a Particle Filter (PF) for WSN assisted by multiple transmit-power information. We propose a modified PF, Kullback-Leibler Distance (KLD)-resampling PF, to ameliorate the effect of RSS variations by generating a sample set near the high-likelihood region for improving the wireless patient tracking. The key idea of this method is to approximate a discrete distribution with an upper bound error on the KLD for reducing both location error and the number of particles used. To determine this bound error, an optimal algorithm is proposed based on the maximum gap error between the proposal and Sampling Important Resampling (SIR) algorithms. By setting up these values, a number of simulations using the health care system's data sets which contains the real RSSI measurements to evaluate the location error in term of various power levels and density nodes for all methods. Finally, we point out the effect of different power levels vs. different density nodes for the wireless patient tracking.