Fluctuation Of Received Signal Strength 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.

A new device-free localization method for RSS data with considering correlations

Device-free localization (DFL) is an emerging intelligent sensing technique with challenging issues due to the highly correlated data. Localization performance has been demonstrated from algorithmic as well as modelling perspectives, for example, by including various sparsity constraints. The general sparse regularization method simultaneously estimates parameters and selects features, but its performance relies heavily on the correlations among features, which leads to generalization errors. Our goal in this study is to formulate a DFL method based on independently interpretable sparse coding to improve the accuracy and efficiency of localization. The scheme uses a novel correlation constraint of sensing matrix columns to handle the high correlation of the received signal strength (RSS) data, overcoming not only the fluctuation of RSS due to environmental changes but also avoiding overfitting through identification of informative variables that provide interpretable results. Meanwhile, the minimax concave penalty (MCP) is proposed as a sparse regularizer to obtain strong sparsity and an exact estimate. We propose using the difference of convex functions algorithm (DCA) and non-convex proximal splitting to address the non-convexity problem. Extensive experiments were performed in a cluttered indoor laboratory and open outdoor environment to verify the efficacy of the proposed method.

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.

Enhanced Radio Map Interpolation Methods Based on Dimensionality Reduction and Clustering

The received signal strength (RSS) based Wi-Fi fingerprinting method is one of the most potential and easily deployed approaches for a reliable indoor positioning system. However, due to the labor intensive and time-consuming radio map construction process, interpolation is often incorporated. To ensure the interpolated radio map is robust against environmental noise and RSS fluctuations, we propose two novel interpolation methods, termed as DimRed and DimRedClust, for an improved radio map construction. The former performs dimensionality reduction prior to the interpolation while the latter employs both the dimensionality reduction and clustering before interpolating the radio map. For dimensionality reduction, principal component analysis (PCA) or truncated singular value decomposition (TSVD) is adopted to profoundly extract essential features from the RSS data while the K-means algorithm is used to partition the reference points (RPs) into several clusters. Subsequently, the RSS for all virtual points are interpolated via inverse distance weighting (IDW). Numerical results based on the real-world multi-floor multi-building dataset confirm the supremacy of the proposed schemes over the baseline IDW interpolation. Compared to the baseline IDW, the proposed PCA-K-means-IDW, TSVD-K-means-IDW, PCA-IDW, and TSVD-IDW could attain a performance gain in terms of average positioning error of up to 30.17%, 30.93%, 19.33%, and 21.61%, respectively.

Smartphone User Tracking by Incorporating User Orientation Using a Double-Layer HMM

We consider the problem of localizing a smartphone user using received signal strength (RSS) measured by a set of known network nodes in a harsh indoor environment. While the RSS of a wireless signal can be conveniently accessed, using it to estimate location is non-trivial in the presence of multipath propagation, shadowing and radio interference. Auxiliary information, such as the indoor building map and user's orientation information, potentially can help to improve localization performance. As the indoor layout is usually known as a priori, a user's moving direction or orientation in a given indoor map may contain valuable information to assist for reducing location ambiguities at estimation, typically when the radio signal channel is corrupted with noise. In this paper, we propose a double-layer hidden Markov model (DHMM) within a Bayesian learning framework for combining user orientation information and processing RSS data in the localization process to deal with RSS fluctuations induced by human body shadowing and multipath interference. Simulation and experimental results show that incorporating user orientation can potentially provide promising indoor positioning results.

An RSS Transform-Based WKNN for Indoor Positioning.

An RSS transform–based weighted k-nearest neighbor (WKNN) indoor positioning algorithm, Q-WKNN, is proposed to improve the positioning accuracy and real-time performance of Wi-Fi fingerprint–based indoor positioning. To smooth the RSS fluctuation difference caused by acquisition equipment, time, and environment changes, base Q is introduced in Q-WKNN to transform RSS to Q-based RSS, based on the relationship between the received signal strength (RSS) and physical distance. Analysis of the effective range of base Q indicates that Q-WKNN is more suitable for regions with noticeable environmental changes and fixed access points (APs). To reduce the positioning time, APs are selected to form a Q-WKNN similarity matrix. Adaptive K is applied to estimate the test point (TP) position. Commonly used indoor positioning algorithms are compared to Q-WKNN on Zenodo and underground parking databases. Results show that Q-WKNN has better positioning accuracy and real-time performance than WKNN, modified-WKNN (M-WKNN), Gaussian kernel (GK), and least squares-support vector machine (LS-SVM) algorithms.

An Indoor Positioning Algorithm Based on Fingerprint and Mobility Prediction in RSS Fluctuation-Prone WLANs

The creation of context-aware services in pervasive computing environments has driven the wide development of wireless local area network (WLAN)-based indoor positioning systems. One of the main challenges in WLAN-based indoor positioning is the severe fluctuation of received signal strength (RSS), which may cause the RSS patterns to be mismatched and the positioning to be inaccurate. In this paper, an indoor positioning algorithm that combines the fingerprint scheme with mobility prediction is proposed. Since the mobility prediction is performed according to the moving speed and direction of the mobile client, the resulting location estimation is more stable compared to the use of RSS alone. Experimental results show that the proposed positioning algorithm can mitigate the impact of the RSS fluctuation and has better positioning accuracy and stability than previous fingerprint-based approaches.

Extreme RSS Based Indoor Localization for LoRaWAN With Boundary Autocorrelation

The received signal strength (RSS) finger-print-based approaches are widely used for indoor location-based services (LBSs). The emerging long range wide area network (LoRaWAN) is a cost-effective solution for indoor latency-tolerant LBSs attributed to its long-range property. In general, there are serious RSS fluctuations due to fadings along the communication path, thus significantly jeopardizing the localization accuracy. To overcome the challenge, in this article we propose the extreme RSS (ERSS) to stabilize the fingerprint database and formulate boundary autocorrelation to downsize tremendously the searching complexity and thus proliferating localization accuracy. In essence, the RSS fluctuations are modeled as a Bernoulli random process so that the RSS stability can be estimated by a newly defined fluctuation analytic function. To mitigate the impact of the perturbative fluctuation, the ERSS is further defined to cultivate a highly stable and robust fingerprint database which withstands environmental dynamics. In addition, boundary autocorrelation is developed to measure and compare the similarity between the measured RSS values versus the prestored fingerprint database. RSS values with low autocorrelation coefficients are eradicated from the typically lengthy searching. The downsized complexity significantly improves the localization accuracy. Experiments were carried out and the results revealed that the proposed method achieved sub-10-m localization accuracy in indoor environments. Such accuracy is encouraging and superior in contemporary LoRaWAN measurements.

SmartLoc: Smart Wireless Indoor Localization Empowered by Machine Learning

Recently, machine learning (ML) has been widely adopted for fingerprint-based indoor localization because of its potency in delineating relationships between received signal strength (RSS) information and labels accurately. Existing ML-based indoor localization systems are less robust because they only adopt the output with the highest probability. This affects the final location estimate, hence compromising accuracy due to the severity of RSS fluctuations. Since different ML algorithms (MLAs) yield different performances, it is therefore intuitive to fuse predictions from multiple MLAs to improve the positioning performance in the presence of signal fluctuation. In this article, we propose SmartLoc, a smart wireless indoor localization framework to enhance indoor localization. In the offline phase, multiple MLAs are trained by utilizing an offline database. We further apply probability alignment to guarantee the predicted probabilities of each MLA at the same confidence level. In the online phase, given a testing RSS sample of a user at an unknown location, we extract the labels with probabilities greater than a certain threshold from each MLA to construct the space of candidate labels (SCL). The size of SCL can be adaptively determined by using our proposed dynamic size determination algorithm. Based on the SCL, we propose a probabilistic model to intelligently estimate the user's location by evaluating the label credibility simultaneously. A high label credibility indicates that the frequently occurred label is more likely to be true. Experimental results in a real changing environment verify the superiority of SmartLoc, outperforming the best among comparative methods by 10.8% in 75th percentile accuracy.

A Compressive Sensing Approach to Detect the Proximity Between Smartphones and BLE Beacons

Bluetooth low energy (BLE) beacons have been widely deployed to deliver proximity-based services (PBSs) to user’s smartphones when users are in the proximity of a beacon. Conventional proximity detection simply uses the received signal strength (RSS) to infer the proximity, and then retrieves the PBS by mapping the beacon ID with the corresponding service in the cloud database. Such an approach suffers two major issues: 1) the severe RSS fluctuation might confuse the smartphone during the detection and 2) a malicious PBS can be delivered by manipulating the same beacon ID. This paper proposes RF fingerprinting to label a beacon with an $N$ -dimensional fingerprint vector, which consists of $N$ RSS values from $N$ deployed beacons. The contribution of our proposed method is twofold: 1) we infer the proximity based on the fingerprint vector instead of relying solely on the single RSS value and 2) we retrieve the PBS by mapping the fingerprint vector instead of the hard-coded beacon ID. The challenge with our proposed approach is the incomplete fingerprint observation during real-time detection, resulting in an underdetermined proximity detection problem. To this end, we exploit the compressive sensing (CS) approach based on the differential evolutional algorithm to address such an underdetermined problem. Extensive simulations with real-world datasets show that our proposed approach outperforms the legacy machine learning techniques with substantial performance gains.

Traveling distance estimation to mitigate unnecessary handoff in mobile wireless networks

Amidst the factors that degrade the performance of a mobile wireless network (MWN), unnecessary handoff is considered to be one of the premier culprits since it aimlessly consumes the network resources. Most of the existing handoff algorithms proposed for MWN suffer from frequent unnecessary handoff as the primary decision of switching network cells is solely based on a single contributing factor, namely the received signal strength (RSS) that approximates the distance between a mobile node and access points. In practice, the RSS may not provide accurate approximation of the true distance as it fluctuates with wireless propagation impairments (such as shadowing, scattering, and reflection), leading to potential incorrect handoff decision within neighboring cells. In this paper, an improved estimation model for instantaneous distance between the mobile node and access point is formulated. Building upon this model, a new handoff technique is proposed by introducing two RSS thresholds and estimated distance verification, anticipating RSS fluctuations that may lead to handoff decision errors. The performance of the proposed handoff mechanism is thoroughly examined using numerical simulations and the acquired results demonstrate promising advantages of this proposed technique.

Accurate WiFi Localization by Unsupervised Fusion of Extended Candidate Location Set

Fusing the predictions of multiple received signal strength (RSS)-based classifiers is an efficient strategy to mitigate the impact of the fluctuation of the RSS. However, most of the existing fusion methods exhibit two remarkable shortcomings: 1) they need to train and store offline weights by the supervised learning and 2) they directly fuse the so-called candidate location set (CLS), which is collected from the most likely location estimate of each classifier (location with the largest probability of being the true location predicted by the classifier), and thus do not fully leverage the knowledge of classifiers. In general, the fluctuation of RSS does not guarantee the location predicted by each classifier with the highest probability to be the true location, thus leading to severe performance degeneration of the existing fusion methods. To overcome the above shortcomings, we propose an accurate WiFi localization framework by unsupervised fusion of an extended CLS (ECLS). First, we train multiple classifiers by only using RSS fingerprints in the offline phase. In the online phase, instead of collecting the CLS from the trained classifiers, we construct an ECLS by augmenting CLS with other location estimates (locations with predication probability greater than a certain threshold) from each classifier. As compared with the CLS, ECLS provides a bigger fusion space that likely includes the true location of the user. Furthermore, an unsupervised fusion localization algorithm based on the ECLS is derived from the joint optimization of weights and the location of the user. Furthermore, a point of inflection searching algorithm is also proposed to intelligently construct the ECLS. Real experimental results show that our proposed algorithm is more robust to changing environments and model errors, and can significantly improve the localization accuracy without any fingerprint and hardware calibrations.

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.

Transferred Knowledge Aided Positioning via Global and Local Structural Consistency Constraints

The fluctuation of received signal strength (RSS) induced by changing environment is the main hindrance from practical applications of the fingerprint-based indoor positioning methods. Transfer learning can mitigate the fluctuation of RSS by transferring knowledge from a source domain (off-line RSS data) to a target domain (online RSS data). However, the existing transfer learning approaches do not fully take into account the full constraints in Global and LOcal Structural conSistency (GLOSS), thus resulting in insufficient knowledge transfer. To overcome the above drawback, we propose a Transferred knowlEdge-Aided POsiTioning (TEAPOT) approach via GLOSS constraints in this paper. TEAPOT imposes the global structural consistency by minimizing the differences between the marginal and conditional distributions of the source and target domains and maximizing the samples variance in a latent subspace. Simultaneously, it also imposes the local structural consistency by minimizing within class variance and maximizing between class variance to retain the source discriminative information and preserving the local neighborhood relationship by using manifold regularization. Furthermore, a nonlinear TEAPOT is derived to improve the ability of TEAPOT to alleviate the limitation of linear projection. Compared with the existing methods, two of our proposed TEAPOT approaches via GLOSS constraints show higher accuracy and better ability in handling out-of-sample generalization. The experimental results verify that the proposed method significantly outperforms the existing methods.

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.

Extended Kalman Filter for Real Time Indoor Localization by Fusing WiFi and Smartphone Inertial Sensors

Indoor localization systems using WiFi received signal strength (RSS) or pedestrian dead reckoning (PDR) both have their limitations, such as the RSS fluctuation and the accumulative error of PDR. To exploit their complementary strengths, most existing approaches fuse both systems by a particle filter. However, the particle filter is unsuitable for real time localization on resource-limited smartphones, since it is rather time-consuming and computationally expensive. On the other hand, the light computation fusion approaches including Kalman filter and its variants are inapplicable, since an explicit RSS-location measurement equation and the related noise statistics are unavailable. This paper proposes a novel data fusion framework by using an extended Kalman filter (EKF) to integrate WiFi localization with PDR. To make EKF applicable, we develop a measurement model based on kernel density estimation, which enables accurate WiFi localization and adaptive measurement noise statistics estimation. For the PDR system, we design another EKF based on quaternions for heading estimation by fusing gyroscopes and accelerometers. Experimental results show that the proposed EKF based data fusion approach achieves significant localization accuracy improvement over using WiFi localization or PDR systems alone. Compared with a particle filter, the proposed approach achieves comparable localization accuracy, while it incurs much less computational complexity.