Received Signal Strength Samples Research Articles

Bayesian Active Learning for Received Signal Strength-Based Visible Light Positioning

Visible Light Positioning (VLP) is a promising indoor localization technology for providing highly accurate positioning. In this work, a VLP implementation is employed to estimate the position of a vehicle in a room using the Received Signal Strength (RSS) and fixed LED-based light transmitters. Classical VLP approaches use lateration or angulation based on a wireless propagation model to obtain location estimations. However, previous work has shown that machine learning models such as Gaussian processes (GP) achieve better performance and are more robust in general, particularly in presence of non-ideal environmental conditions. As a downside, Machine Learning (ML) models require a large collection of RSS samples, which can be time-consuming to acquire. In this work, a sampling scheme based on active learning (AL) is proposed to automate the vehicle motion and to accelerate the data collection. The scheme is tested on experimental data from a RSS-based VLP setup and compared with different settings to a simple random sampling.

A real-time fingerprint-based indoor positioning using deep learning and preceding states

In fingerprint-based positioning methods, the received signal strength (RSS) vectors from access points are measured at reference points and saved in a database. Then, this dataset is used for the training phase of a pattern recognition algorithm. Several noise types impact the signals in radio channels, and RSS values are corrupted correspondingly. These noises can be mitigated by averaging the RSS samples. In real-time applications, the users cannot wait to collect uncorrelated RSS samples to calculate their average in the online phase of the positioning process. In this paper, we propose a solution for this problem by leveraging the distribution of RSS samples in the offline phase and the preceding state of the user in the online phase.In the first step, we propose a fast and accurate positioning algorithm using a deep neural network (DNN) to learn the distribution of available RSS samples instead of averaging them at the offline phase. Then, the similarity of an online RSS sample to the RPs’ fingerprints is obtained to estimate the user’s location. Next, the proposed DNN model is combined with a novel state-based positioning method to more accurately estimate the user’s location. Extensive experiments on both benchmark and our collected datasets in two different scenarios (single RSS sample and many RSS samples for each user in the online phase) verify the superiority of the proposed algorithm compared with traditional regression algorithms such as deep neural network regression, Gaussian process regression, random forest, and weighted KNN.

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.

On Quantization for Secret Key Generation From Wireless Channel Samples

Physical layer security (PLS) techniques hold promise for augmenting secure communications in the 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> generation of mobile wireless networks. Secret key generation (SKG) is a PLS technique which exploits the wireless propagation channel's randomness to generate symmetric key bits for information encryption and decryption. This work proposes symmetric key generation based on non-uniform quantization of the received signal strength (RSS) samples of a Nakagami-$m$ fading channel. The proposed strategy for non-uniform quantization for SKG aims to set quantization thresholds for maximal key randomness and high values of key generation rate (KGR) and key agreement probability (KAP). Finally, a framework is proposed to use single node RSS measurements, readily available in the literature, to generate RSS samples at the other link end. This framework facilitates the testing of new SKG algorithms that require simultaneous RSS measurements by the legitimate nodes, which are not readily available in the open literature. The effectiveness of the proposed SKG scheme is validated through Monte Carlo methods and the National Institute of Standards and Technology (NIST) test suite for assessing the randomness of the generated key sequence.

Indoor Radio Map Construction and Localization With Deep Gaussian Processes

With the increasing demand for location-based service, WiFi-based localization has become one of the most popular methods due to the wide deployment of WiFi and its low cost. To improve this technology, we propose DeepMap, a deep Gaussian process for indoor radio map construction and location estimation. Received signal strength (RSS) samples are used in DeepMap to generate accurate and fine-grained radio maps. A two-layer deep Gaussian process model is designed to determine the relationship between the location and RSS samples, while the model parameters are optimized with an offline Bayesian training method. To identify the location of a mobile device, a Bayesian fusion method is proposed, which leverages RSS samples from multiple access points (APs) to achieve high location estimation accuracy. We conduct comprehensive experiments to verify the performance of DeepMap in two indoor settings. DeepMap’s robustness is validated using limited training data.

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.

Expectation Maximization Indoor Localization Utilizing Supporting Set for Internet of Things

With the growth of WLAN infrastructure, received signal strength (RSS) fingerprint-based WiFi indoor positioning systems have received considerable attention recently. Some existing RSS fingerprint-based localization methods estimate locations by directly matching an online testing sample with an offline database, and thus show low accuracy because RSS is known to be vulnerable to variations caused by changing environment and heterogeneous hardware. To overcome the above drawbacks, we propose an expectation maximization indoor localization approach by leveraging supporting set (EMSS). In the offline phase, we first divide a positioning area into ${G}$ grid points and index each grid by a label. All the indices of grid points form a label set $ {\Psi =\{1,2,\ldots, G\}}$ . Then, we collect the RSS fingerprints to construct an offline database for all labeled grid points. In the online phase, given an online RSS testing sample, we first construct a supporting set (SS), which is a subset of $ {\Psi }$ , selected by the similarity between the online RSS sample and offline database. So, SS is a latent space that likely includes the true label (location) of the user. Based on the SS, we then derive an expectation maximization (EM) algorithm by incorporating the fingerprint quality into the estimation of the true label. EM can intelligently estimate the location of the user by evaluating the fingerprint quality of SS. Furthermore, we propose an optimal size selection algorithm based on Bayesian information criterion to adaptively determine the size of SS. Our method can effectively mitigate the impacts of changing environment and heterogeneous hardware without fingerprint and hardware calibrations, and can thus be practically applied. Experimental results verify that EMSS performs significantly better than some existing fingerprint-based methods.

RMapTAFA: Radio Map Construction Based on Trajectory Adjustment and Fingerprint Amendment

With the increasing demands of location-based services, indoor positioning systems based on the received signal strength (RSS) fingerprinting and pedestrian dead reckoning (PDR) have been attracting lots of research interests in both academia and industry. However, the RSS fingerprinting suffers from the burdensome site survey for radio map construction, while the PDR tracking suffers from the accumulative step localization error. In this paper, we propose the RMapTAFA scheme to construct a radio map from pedestrian trajectories to jointly address the two challenges. We first propose a novel sample-fingerprint structure containing two new coefficients for fingerprint composition: the credibility coefficient measures the confidence level of a sample, while the reliability coefficient defines the importance of each element in a fingerprint. Each step RSS sample of a pedestrian trajectory, if being determined eligible, is included into the proposed structure, together with its credibility computed from our proposed fingerprint amendment algorithm. Furthermore, we propose a trajectory adjustment algorithm via selective particle filtering by enjoying the RSS-fingerprinting result obtained from the constructed radio map. Field measurements and experiments validate the RMapTAFA scheme in terms of the improved localization performance of both pedestrian trajectory tracking and stationary point positioning.

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.

Voronoi Diagram and Crowdsourcing-Based Radio Map Interpolation for GRNN Fingerprinting Localization Using WLAN.

In the last decade, fingerprinting localization using wireless local area network (WLAN) has been paid lots of attention. However, this method needs to establish a database called radio map in the off-line stage, which is a labor-intensive and time-consuming process. To save the radio map establishment cost and improve localization performance, in this paper, we first propose a Voronoi diagram and crowdsourcing-based radio map interpolation method. The interpolation method optimizes propagation model parameters for each Voronoi cell using the received signal strength (RSS) and location coordinates of crowdsourcing points and estimates the RSS samples of interpolation points with the optimized propagation model parameters to establish a new radio map. Then a general regression neural network (GRNN) is employed to fuse the new and original radio maps established through interpolation and manual operation, respectively, and also used as a fingerprinting localization algorithm to compute localization coordinates. The experimental results demonstrate that our proposed GRNN fingerprinting localization system with the fused radio map is able to considerably improve the localization performance.

Robust Neighborhood Graphing for Semi-Supervised Indoor Localization With Light-Loaded Location Fingerprinting

The indoor localization systems based on wireless local area network received signal strength (RSS) have been widely applied due to the simplicity of system deployment as well as easy implementation on various mobile devices like the smartphones. However, they are often suffered by the major drawback of the extensive effort for location fingerprinting which is significantly labor-intensive and time-consuming. In response to this compelling problem, we design an improved manifold alignment approach to construct a cost-efficient radio map which consists of the sparsely collected location fingerprints and crowdsourcing RSS data with the purpose of reducing the overall fingerprints calibration effort. A new graph construction scheme which is proved to be the optimal choice to model the smoothness assumption in semi-supervised learning is proposed to explore the informativeness conveyed by location fingerprints during the process of radio map construction. In addition, the concept of execution characteristic function is considered to minimize the RSS sample capacity at each reference point to reduce fingerprints calibration effort further. Finally, the extensive experimental results demonstrate the performance improvement by the proposed system with the probability of localization errors within 3 m, 79.60%, which is at most 26.30 percentages higher than the one by the existing systems using location fingerprints solely.

Indoor Localization Algorithm based on Fingerprint Using a Single Fifth Generation Wi-Fi Access Point

This paper proposes an indoor positioning system (IPS) based on WLAN using a single fifth-generation (5G) Wi-Fi access point. The proposed method uses fingerprint and classification models based on KNN (K-nearest neighbor) and Bayes rule. The fingerprint is formed by beam RSS (Received Signal Strength) samples, collected in some 2D locations of the indoor environment. Numerical simulations shown that using the best beam samples, it is possible to locate the stationary user's mobile device with average error less than 2.5 m.

WLAN Fingerprint Indoor Positioning Strategy Based on Implicit Crowdsourcing and Semi-Supervised Learning

Wireless local area network (WLAN) fingerprint positioning is an indoor localization technique with high accuracy and low hardware requirements. However, collecting received signal strength (RSS) samples for the fingerprint database is time-consuming and labor-intensive, hindering the use of this technique. The popular crowdsourcing sampling technique has been introduced to reduce the workload of sample collection, but has two challenges: one is the heterogeneity of devices, which can significantly affect the positioning accuracy; the other is the requirement of users’ intervention in traditional crowdsourcing, which reduces the practicality of the system. In response to these challenges, we have proposed a new WLAN indoor positioning strategy, which incorporates a new preprocessing method for RSS samples, the implicit crowdsourcing sampling technique, and a semi-supervised learning algorithm. First, implicit crowdsourcing does not require users’ intervention. The acquisition program silently collects unlabeled samples, the RSS samples, without information about the position. Secondly, to cope with the heterogeneity of devices, the preprocessing method maps all the RSS values of samples to a uniform range and discretizes them. Finally, by using a large number of unlabeled samples with some labeled samples, Co-Forest, the introduced semi-supervised learning algorithm, creates and repeatedly refines a random forest ensemble classifier that performs well for location estimation. The results of experiments conducted in a real indoor environment show that the proposed strategy reduces the demand for large quantities of labeled samples and achieves good positioning accuracy.

Joint mode selection and resource allocation in D2D communication based underlaying cellular networks

Device-to-Device (D2D) communication technology, under the standardization of third generation partnership project and a component of the evolving fifth generation architecture, is mainly aimed to increase system capacity and data rate via providing direct communications between end devices without the use of routing data through the network. Apart from the attracting features, due to the resource sharing between cellular user equipment (CUE) and D2D user equipment (DUE) in such communications, an efficient algorithm for resource and power allocation to DUE, especially for mobile users is necessary to maintain the performance. The current paper introduces a joint mode algorithm for mobile user to choose between cellular and D2D communications and thereby, analyzes resource allocation issues. We propose an efficient algorithm for mobility management of users based on their current connection modes. The locations of D2D pairs are estimated by Levenberg–Marquardt method based on the received signal strength (RSS) from different macrocells. Since the range of D2D communication is much shorter than that of cellular communication, in order to prevent ping-pong handoffs between cellular and D2D modes, we propose the estimation of the next RSS samples in cellular mode prior to switching to D2D mode. In both cellular and D2D modes, the allocated resource block (RB) is the one with the highest signal to interference plus noise ratio (SINR) in order to increase the throughput, under the condition of providing minimum SINR requirement of CUE. This is achieved via transmission power control of the D2D pair. For performance evaluation, we studied the effects of increasing velocity of D2D and cellular users, number of users, and SINR threshold. The results indicate that the proposed solution fairly manages the communication mode of mobile users and incurs improvement in system throughput.

Technique for order of preference by similarity to ideal solution based predictive handoff for heterogeneous networks

This study presents an efficient handoff algorithm for heterogeneous networks comprising macrocells, microcells, picocells, and femtocells. The proposed algorithm is based on call admission control (CAC) for selecting target base station (BS) from a list of neighbouring candidate BSs, as well as using the technique for order of preference by similarity to ideal solution (TOPSIS) as decision method. The introduced algorithm takes into account multiple criteria including measured received signal strength (RSS) and signal-to-interference-plus noise ratio (SINR), predicted RSS and SINR, and number of free resource blocks of neighbouring BSs. With the use of TOPSIS, neighbouring BSs are ordered based on their priority determined using the five mentioned criteria. Selection of target BS from ordered list is then performed using CAC. Also, coverage expansion is used to retain the connection when there is not any BS for handoff. In addition, in order to predict the RSS and SINR samples, the logistic smooth transition autoregressive model is used. Performance of the proposed algorithm is evaluated in terms of ping-pong rate, outage probability, and throughput. The results indicate efficiency of the proposed algorithm in reducing number of unnecessary handoffs while significantly increasing throughput and decreasing connection dropping when compared with conventional algorithms.

A novel radio map construction method to reduce collection effort for indoor localization

Abstract Indoor localization using the fingerprinting technique, namely, the radio map, has attracted much attention in the research community recently. However, constructing a complete radio map is extremely labor-extensive and time-consuming, especially for a wide area. Although some works have been done to reduce the number of calibration points, the accuracy decreases if there are not enough fingerprints. In this paper, we propose a novel method based on the radio propagation model to construct a radio map with full fingerprints. In the radio map, the calibration points (CPs), i.e., fingerprints, are classified into two categories: the primary CPs chosen to collect received signal strength (RSS) artificially, and the secondary CPs obtained through some calculations. Based on the radio map constructed, we employ the Voronoi diagram to divide the area into several Voronoi regions and restrict the localization algorithm to run in a specific Voronoi region to reduce computational complexity. The comparison results show that our method saves a lot of time and human effort in collecting RSS samples, and achieves much higher accuracy than other existing schemes.

Error Estimation Method for Matrix Correlation-Based Wi-Fi Indoor Localization

A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half.

Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.

Outdoor Location Estimation in Changeable Environments

One approach to location estimation constructs a radio map of received signal strength (RSS) measurements at different known locations. However, location-based systems that depend on RSS alone are susceptible to inaccuracies caused by several factors, such as changes in humidity, temperature, the number of users and the physical environment. In this paper, we present a novel algorithm in which one radio map is generated as a reference map and adjusted for different run-time environmental conditions. A small sample of new RSS samples for the new environment is collected, with locations, and used to build a model to calibrate new measurements to the reference radio map. The calibration is not uniform and depends on the observed RSS. The effectiveness of the proposed method is demonstrated using real GSM data sets collected from a three-day music festival in London Victoria Park. Results are presented with and without applying the correction. A state-of-the-art cluster-based deterministic location estimation algorithm is used throughout.

Efficient base station connectivity area discovery

Many applications of autonomy are significantly complicated by the need for wireless networking, with challenges including scalability and robustness. Radio accomplishes this in a complex environment, but suffers from rapid signal strength variation and attenuation typically much worse than free space loss. In this paper, we propose and test algorithms to autonomously discover the connectivity area for a base station in an unknown environment using an average of received signal strength (RSS) values and a RSS threshold to delineate the goodness of the channel. We combine region decomposition and RSS sampling to cast the problem as an efficient graph search. The nominal RSS in a sampling region is obtained by averaging local RSS samples to reduce the small-scale fading variation. The RSS gradient is exploited during exploration to develop an efficient approach for discovery of the base station connectivity boundary in an unknown environment. Indoor and outdoor experiments demonstrate the proposed techniques. The results can be used for sensing and collaborative autonomy, building base station coverage maps in unknown environments, and facilitating multi-hop relaying to a base station.