Received Signal Strength Observations Research Articles

Sparsity-enabled radio tomographic imaging using quantized received signal strength observations

Detection and tracking of objects in wireless sensor networks (WSN) is one of the active research areas in the signal processing community, where device-free localization (DFL) techniques are the potential contributors. Radio tomographic imaging (RTI) is one such DFL technique where the loss field surrounded by any obstacle can be visualized with the help of the received signal strength (RSS) of various transceiver nodes. Due to strict communication constraints and the necessity of low-complexity sensors, we are motivated towards quantized RSS (q-RSS) observations in the RTI system. The RSS data is digitized before sending it to the fusion center (FC). Therefore, uncertainty due to RSS quantization error leads to momentous performance degradation of the RTI system. The proposed support vector regression (SVR) technique reduces this uncertainty. The ϵ-SVR and ν-SVR techniques are affected by noise levels, and their performance is enhanced by the use of fused l2-SVR (F-l2-SVR). However, these estimators have higher computational complexity due to the large number of support vectors. Hence, a robust sparsity-based estimator using the linear programming SVR (LP-SVR) is proposed to reduce the computational complexity. Further, a novel fused lasso-based l1-SVR (FL-l1-SVR) estimator is proposed that provides estimated spatial loss field (SLF) as close as to the true SLF. Finally, the performance metrics and the reduced number of support vectors lead to the efficacy of the proposed FL-l1-SVR estimator over other estimators under q-RSS observations in the RTI system.

Sparse Bayesian multiple sources localization using variational approximation for Laplace priors

Multiple sources localization (MSL) has been playing an important role in various applications of wireless sensor networks. In this paper, sparse representation-based MSL with received signal strength (RSS) as observations is addressed within the sparse Bayesian learning framework. Conventional approaches are usually based on the assumption of Gaussian prior for the sparse coefficients. In this paper, Laplacian prior is employed to model the sparse coefficient for improved sparsity performance. To tackle the problem that Laplace prior is not conjugate to Gaussian likelihood, a variational approximation is employed for Bayesian inference. Finally, a multiple sources localization method is developed. Furthermore, to reduce the computing complexity and promote sparsity, a fast multiple sources localization method is further developed. In the proposed methods, the source locations, sparse coefficients, and all necessary model parameters are adaptively deduced solely from the RSS observations. Meanwhile, by employing Laplacian prior for the sparse coefficients, better sparse recovery can be achieved. Experimental results demonstrate the effectiveness of the proposed methods.

An Efficient Indoor Positioning Method Based on Wi-Fi RSS Fingerprint and Classification Algorithm.

Wi-Fi received signal strength (RSS) fingerprint-based indoor positioning has been widely used because of its low cost and universality advantages. However, the Wi-Fi RSS is greatly affected by multipath interference in indoor environments, which can cause significant errors in RSS observations. Many methods have been proposed to overcome this issue, including the average method and the error handling method, but these existing methods do not consider the ever-changing dynamics of RSS in indoor environments. In addition, traditional RSS-based clustering algorithms have been proposed in the literature, but they make clusters without considering the nonlinear similarity between reference points (RPs) and the signal distribution in ever-changing indoor environments. Therefore, to improve the positioning accuracy, this paper presents an improved RSS measurement technique (IRSSMT) to minimize the error of RSS observation by using the number of selected RSS and its median values, and the strongest access point (SAP) information-based clustering technique, which groups the RPs using their SAP similarity. The performance of this proposed method is tested by experiments conducted in two different experimental environments. The results reveal that our proposed method can greatly outperform the existing algorithms and improve the positioning accuracy by 89.06% and 67.48%, respectively.

Calibrating Multi-Channel RSS Observations for Localization Using Gaussian Process

This letter proposes to use a Gaussian process regression model to compensate for frequency dependent shadowing effects and multipath in received signal strength (RSS) observations. Parametric multi-channel RSS calibration models are introduced and characterized by the inter-channel bias and the scale factor terms. With the proposed calibration model, multi-channel RSS observations can be more effectively combined for localization over large space. Field tests with BLE 4.2 wireless radio broadcasting at channels 0, 12, and 39 have been conducted over a 9600 $\text{m}^{2}$ outdoor area. Test results with sufficient and insufficient multi-channel RSS observations both confirm improved positioning performance by using the proposed model.

A Sparse Manifold Learning Approach to Robust Indoor Positioning Based on Wi-Fi RSS Fingerprinting

The emerging location-based applications depend on the fast and accurate positioning of mobile targets. Wi-Fi received signal strength (RSS) fingerprinting provides a promising solution to localize an object in indoor environments. Among the factors challenging the RSS fingerprinting based algorithms are the site survey cost and the time-varying environment, given the unreliable signal qualities. Here we present a novel approach to indoor object positioning using the manifold assumption on the radio map in RSS-location space. Thinking the measured RSS from one access point (AP) in different locations are randomly drawn from a nonlinear manifold (ground truth), we propose an expectation-maximization (EM) style algorithm to reconstruct the sparse representation of this manifold from the noisy RSS observations. Motivated by the observation that the radio map has a strong local correlation in the RSS-location space, we introduce a multi-scale constrained quadratic programming to approximate the manifold. Within limited iterations, we can estimate the ground truth RSS values and parameters simultaneously. As a result, the learned manifold is exploited to predict the object's position: we develop a positioning algorithm by minimizing the manifold distortion effort which integrates both measurement error and manifold shape preservation. We conducted extensive simulations and experiments in different settings, testing the datasets collected in a building in the last 8 months. The results showed that the proposed approach was adaptive to the varying environmental noise levels, presenting robust positioning performance.

Received signal strength–based indoor localization using a robust interacting multiple model–extended Kalman filter algorithm

Due to the vast increase in location-based services, currently there exists an actual need of robust and reliable indoor localization solutions. Received signal strength localization is widely used due to its simplicity and availability in most mobile devices. The received signal strength channel model is defined by the propagation losses and the shadow fading. In real-life applications, these parameters might vary over time because of changes in the environment. Thus, to obtain a reliable localization solution, they have to be sequentially estimated. In this article, the problem of tracking a mobile node by received signal strength measurements is addressed, simultaneously estimating the model parameters. Particularly, a two-slope path loss model is assumed for the received signal strength observations, which provides a more realistic representation of the propagation channel. The proposed methodology considers a parallel interacting multiple model–based architecture for distance estimation, which is coupled with the on-line estimation of the model parameters and the final position determination via Kalman filtering. Numerical simulation results in realistic scenarios are provided to support the theoretical discussion and to show the enhanced performance of the new robust indoor localization approach. Additionally, experimental results using real data are reported to validate the technique.

Joint Indoor Localization and Radio Map Construction with Limited Deployment Load

One major bottleneck in the practical implementation of received signal strength (RSS) based indoor localization systems is the extensive deployment efforts required to construct the radio maps through fingerprinting. In this paper, we aim to design an indoor localization scheme that can be directly employed without building a full fingerprinted radio map of the indoor environment. By accumulating the information of localized RSSs, this scheme can also simultaneously construct the radio map with limited calibration. To design this scheme, we employ a source data set that possesses the same spatial correlation of the RSSs in the indoor environment of interest. The knowledge of this data set is then transferred to a limited number of calibration fingerprints and one or several RSS observations with unknown locations, in order to perform direct localization of these observations using manifold alignment. We test two different source data sets, namely a simulated radio propagation map and the environment's plan coordinates. For moving users, we exploit the correlation of their observations to improve their localization accuracy. The online testing in two indoor environments shows that the plan coordinates achieve better results than the simulated radio maps, and a negligible degradation with 70-85 percent reduction in the calibration load.

RSS-Based Self-Localization Framework for Future Wireless Networks

The localization is an important asset in all existing and emerging wireless networking solutions since it can extend the radio environmental awareness and assist in providing better network operation. The received signal strength (RSS) based non-Bayesian transmitter localization is especially interesting due to the inherent presence of the RSS observations in all commercial radio devices and the instantaneous estimations without the need for extensive training/learning phases. The existing RSS based localization solutions neglect the problems arising from the inherent sensing network topology uncertainty. The sensors, usually assumed to have a-priori known positions, are often a subject of a previous estimation which propagates errors in the transmitter localization procedure, and, hence, results in significant transmitter localization performance degradation. This paper presents a recently developed generic RSS based joint transmitter/sensors localization framework, founded on the assumption of uncertain topology information. The derived joint maximum likelihood (JML) algorithm simultaneously estimates the transmitter and uncertain sensor positions providing twofold gains: improving the transmitter localization and reducing the network topology uncertainty. The paper broadly evaluates the JML algorithm, emphasizing the substantial localization gains originating from the joint transmitter/sensor position estimation. The results prove up to 85 % sensor position uncertainty reduction with the general system model with multiple transmitters locations and multiple previous estimations of the sensors positions. The paper also derives the theoretical lower bounds of the joint estimation framework, and proves the convergence of the JML algorithm. The presented joint estimation framework is applicable to a variety of wireless networking applications. It can provide self-awareness in future wireless networks and cope with the environment and topology dynamism in wireless ad-hoc networks.

A Mobile-Beacon-Assisted Sensor Network Localization Based on RSS and Connectivity Observations

Determining the physical positions of sensors has been a fundamental and crucial problem in wireless sensor networks (WSNs). Due to the inherent characteristics of WSNs, extremely limited resources available at each low-cost and tiny sensor node, connectivity-based range-free solutions could be a better choice to feature a low overall system cost. However, localization by means of mere connectivity may underutilize the proximity information available from neighborhood sensing. Although received signal strength (RSS) values are irregular and highly dynamic, it might provide heuristic information about which neighboring nodes are closer and which are further. We propose a distributed Mobile-beacon-assisted localization scheme based on RSS and Connectivity observations (MRC) with a specific trajectory in static WSNs. To ensure that MRC performs as true to reality as possible, we propose two improved approaches based on MRC to consider irregular radio scenario in the noisy environment. Comparing the performance with three typical range-free localization methods in static WSNs, our lightweight MRC algorithm with limited computation and storage overhead is more suitable for very low-computing power sensor nodes that can efficiently outperform better with only basic arithmetic operations in the simulation and physical environments.

Opportunistic spectrum sharing with multiple cochannel primary transmitters

We present a distributed, collaborative algorithm to enable opportunistic spectrum access for cognitive radios in the presence of multiple cochannel transmitters. A spectrum hole detection and estimation technique based on received signal strength observations is developed, which allows the coexistence of both licensed and unlicensed transmitters. We address the issues of how to perform collaborative spectrum sensing in the presence of multiple cochannel transmitters and how to determine the maximum transmit power that can be used for a given frequency channel by a cognitive radio while avoiding harmful interference to the licensed network. Simulation results are provided to validate the feasibility and performance of the proposed scheme.

Opportunistic spectrum sharing with multiple cochannel primary transmitters

We present a distributed, collaborative algorithm to enable opportunistic spectrum access for cognitive radios in the presence of multiple cochannel transmitters. A spectrum hole detection and estimation technique based on received signal strength observations is developed, which allows the coexistence of both licensed and unlicensed transmitters. We address the issues of how to perform collaborative spectrum sensing in the presence of multiple cochannel transmitters and how to determine the maximum transmit power that can be used for a given frequency channel by a cognitive radio while avoiding harmful interference to the licensed network. Simulation results are provided to validate the feasibility and performance of the proposed scheme.

Kernel-Based Positioning in Wireless Local Area Networks

The recent proliferation of Location-Based Services (LBSs) has necessitated the development of effective indoor positioning solutions. In such a context, Wireless Local Area Network (WLAN) positioning is a particularly viable solution in terms of hardware and installation costs due to the ubiquity of WLAN infrastructures. This paper examines three aspects of the problem of indoor WLAN positioning using received signal strength (RSS). First, we show that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results. Second, we explore the problem of access point (AP) selection for positioning and demonstrate the need for further research in this area. Third, we present a kernelized distance calculation algorithm for comparing RSS observations to RSS training records. Experimental results indicate that the proposed system leads to a 17 percent (0.56 m) improvement over the widely used K-nearest neighbor and histogram-based methods.