Signal Strength Model Research Articles

Enhancing Location Accuracy by Minimizing RMS Using RSS-AMLE in WSN

Over the past decade, there has been significant growth in wireless sensor networks, particularly in the context of industrial applications. Mobile sensor networks have garnered research interest due to their ability to facilitate communication between various devices. Still, the mobility of these nodes gives rise to challenges such as network coverage and connectivity issues. Addressing these challenges necessitates accurate estimation of sensor node locations, a critical factor in network performance. Numerous methods, such as Angle of Arrival (AOA) and Time of Arrival (TOA), have been proposed for node localization. Still, these methods are plagued by localization errors and high implementation costs. To overcome these localization errors in wireless sensor networks, we present an adaptive approach based on the Received Signal Strength (RSS) model. This model views localization as a non-convex problem and employs an adaptive maximum likelihood estimation to minimize localization errors. An extensive simulation study is carried out to measure the performance of the intended approach in minimizing the localization error. The results unequivocally demonstrate that our localization scheme achieves higher accuracy in locating sensor nodes while reducing deployment costs. Comparative analysis against existing methods further underscores the significance of our approach.

Cooperative Localization in Hybrid Active and Passive Wireless Sensor Networks With Unknown Tx Power

Hybrid active and passive wireless sensor networks (HWSNs) gain advantages in extending the network lifetime and reducing the overall cost. Because the passive sensors without battery harvest the energy from distributed active sensor signal beam, and only a few active sensors can maintain a large-scale network. Thus, how to track the passive sensor’s location is essential for network management. Since the active sensors are sparsely deployed, cooperative localization which employs passive sensors to locate themselves together is a promising solution. In this paper, we analyze the energy beam generated by the active sensors on the cooperative localization accuracy of the passive sensors. We consider the spatial-temporal cooperative localization based on the received signal strength (RSS) model with unknown Tx power information of each sensor due to the limited processing capabilities, circuit complexity and energy constraints. We formulate the Fisher information matrix (FIM) and the corresponding Cramér-Rao lower bound (CRLB) for the static full connected network and dynamic spatial-temporal recursive network. Accordingly, energy beamforming schemes are proposed to optimize localization accuracy and energy efficiency problems. For the optimal localization problem, we derive the closed-form solution of the optimal energy beamforming wave. For the optimal energy efficiency problem, we propose a semi-definite programming (SDP) solution to achieve optimal energy consumption with a self-calibration method, which can address the over-relax problem. Extensive simulation results indicate that our proposed beamforming schemes have high localization accuracy and lower power consumption compared with the existing power allocation-based schemes.

Bayesian location estimation of mobile devices using a signal strength model

Mobile network operator (MNO) data are a rich data source for various topics in official statistics, such as present population, mobility, migration, and tourism. Estimating the geographic location of mobile devices is an essential step for statistical inference. Most studies use Voronoi tessellation for this, which is based on the assumption that mobile devices are always connected to the nearest radio cell. We propose an alternative location estimation method following a Bayesian approach and using a physical model for the received signal strength. Our Bayesian framework allows for different modules of prior knowledge about where devices are expected to be, and different modules for the likelihood of connection given a geographic location. We discuss and compare the use of several prior modules, including one that is based on land use. For the likelihood module we propose a signal strength model using radio cell properties such as antenna height, propagation direction, and power. Using Bayes' rule, we derive a posterior probability distribution that is an estimate of the geographic location, which can be used for further statistical inference. We describe the method and provide illustrations of a fictional example that resembles a real-world situation. The method has been implemented in the R packages mobloc and mobvis, which are briefly described.

Enabling Plug-and-Play and Crowdsourcing SLAM in Wireless Communication Systems

Simultaneous localization and mapping (SLAM) during communication is emerging. This technology promises to provide information on propagation environments and transceivers’ location, thus creating several new services and applications for the Internet of Things and environment-aware communication. Using crowdsourcing data collected by multiple agents appears to be much potential for enhancing SLAM performance. However, the measurement uncertainties in practice and biased estimations from multiple agents may result in serious errors. This study develops a robust SLAM method with measurement plug-and-play and crowdsourcing mechanisms to address the above problems. First, we divide measurements into different categories according to their unknown biases and realize a measurement plug-and-play mechanism by extending the classic belief propagation (BP)-based SLAM method. The proposed mechanism can obtain the time-varying agent location, radio features, and corresponding measurement biases (such as clock bias, orientation bias, and received signal strength model parameters), with high accuracy and robustness in challenging scenarios without any prior information on anchors and agents. Next, we establish a probabilistic crowdsourcing-based SLAM mechanism, in which multiple agents cooperate to construct and refine the radio map in a decentralized manner. Our study presents the first BP-based crowdsourcing that resolves the “double count” and “data reliability” problems through the flexible application of probabilistic data association methods. Numerical results reveal that the crowdsourcing mechanism can further improve the accuracy of the mapping result, which, in turn, ensures the decimeter-level localization accuracy of each agent in a challenging propagation environment.

Target Localization in Wireless Sensor Networks Based on Received Signal Strength and Convex Relaxation.

A new positioning algorithm based on RSS measurement is proposed. The algorithm adopts maximum likelihood estimation and semi-definite programming. The received signal strength model is transformed to a non-convex estimator for the positioning of the target using the maximum likelihood estimation. The non-convex estimator is then transformed into a convex estimator by semi-definite programming, and the global minimum of the target location estimation is obtained. This algorithm aims at the known problem and then extends its application to the case of unknown. The simulations and experimental results show that the proposed algorithm has better accuracy than the existing positioning algorithms.

FusionVLP: The Fusion of Photodiode and Camera for Visible Light Positioning

The previous Photodiode-only (PD-only) VLP systems might not provide an accurate positioning solution in an interference environment. Meanwhile, the previous camera-only VLP systems might need a specific camera with a high resolution and a large Field Of View (FOV). This article proposes a Visible Light Positioning (VLP) system (FusionVLP) based on the fusion of the measurements from a standard PD and a low-cost camera to compensate their individual deficiencies, thus improving VLP accuracy and achieving great potential for low-cost deployment. FusionVLP has an Ensemble Kalman Filter (EnKF) module for real-time positioning and a Fixed-Lag Ensemble Kalman Smoother (FLEnKS) module for semi-real-time positioning for different application requirements. Furthermore, a new two-step adjusted PD Received Signal Strength (RSS) model with adaptive parameter training is proposed to mitigate the model error of the traditional model, thus improving VLP accuracy. To further enhance the positioning accuracy, a novel positioning method based on a typical rectangular ceiling pattern is proposed, thus providing a camera-based specific position measurement for data fusion and improving the positioning by 36.27% (2D) and 35.61% (3D). The field experiments in an interference environment show that FusionVLP achieves the accuracy of 8.67 cm (2D)/12.15 cm (3D) using FLEnKS.

Machine Learning-Based Radio Coverage Prediction in Urban Environments

AIM: Having a reliable prediction model of radio signal strength is an essential tool for planning and designing a radio network. Given a geographic region, and associated power estimates linked to the transmitter placements, our objective is to develop machine learning models to predict the strength of the radio signals. BACKGROUND: The propagation model is often used to determine the optimal location of radio transmitters in order to optimize the power coverage in a geographic area of interest. However, it is often a costly operation to obtain the exact power measurements over a region for a given set of transmitter locations. Therefore, fast prediction methods are needed to estimate the power values given limited data. METHODOLOGY: We consider a dataset consisting of simulated power at each point in an environment for a given set of transmitter locations. We experiment with various machine learning models, namely, generalized linear models (GLMs), neural networks (NNs), and k-nearest neighbor (KNN), to estimate the power values for a given transmitter placement. We investigate various feature engineering approaches to enhance the predictive performance of the machine learning models. RESULTS: We observe that employed feature engineering methods such as polynomial degrees and transmitter to cluster distances significantly improve the prediction accuracy. In particular, GLM model performance notably improves thanks to these extracted features, where mean absolute error (MAE) is reduced around 77% from 11.37 dB to 2.55 dB. We note that KNN with k = 2 and DNN models perform better than NN and GLM. KNN has the best performance with an average MAE of 0.65dB and also substantially faster to train than NN/DNN models. In addition, our analysis shows that, to train a well-performing machine learning model, it is sufficient to use a dataset consisting of measurements at a fraction of the potential transmitter locations in a given region. CONCLUSIONS: Machine learning methods are highly effective for the coverage prediction task. Using carefully engineered features, simple models such as GLMs and KNNs can be as effective as more complex ones, especially for small datasets.

RSS Models for Respiration Rate Monitoring

Received signal strength based respiration rate monitoring is emerging as an alternative non-contact technology. These systems make use of the radio measurements of short-range commodity wireless devices, which vary due to the inhalation and exhalation motion of a person. The success of respiration rate estimation using such measurements depends on the signal-to-noise ratio, which alters with properties of the person and with the measurement system. To date, no model has been presented that allows evaluation of different deployments or system configurations for successful breathing rate estimation. In this paper, a received signal strength model for respiration rate monitoring is introduced. It is shown that measurements in linear and logarithmic scale have the same functional form, and the same estimation techniques can be used in both cases. The model is numerically and empirically evaluated, and its properties are discussed in depth. The most important model implications are validated under varying signal-to-noise ratio conditions using the performances of three estimators: batch frequency estimator, recursive Bayesian estimator, and model-based estimator. The results are in coherence with the findings, and they imply that different estimators are advantageous in different signal-to-noise ratio regimes.

Unknown RSSD-Based Localization CRLB Analysis With Semidefinite Programming

Passive source localization is an important research area with numerous applications in mobile communications and signal processing. This paper presents an analysis of energy-based localization performance with unknown transmit power using received signal strength (RSS) and RSS difference (RSSD) models. These models are widely used because of their low cost and simple implementation. Previous studies were based on the assumption that the source node transmit power is known, which is not practical in many situations. The Cramer–Rao lower bound for the RSS and RSSD models with correlated noise is derived as a performance benchmark for the mean squared error (MSE) of the location and transmit power estimation. It is shown that the MSE can be factored into two independent terms corresponding to the geometric distribution of the sensors and the channel parameters including the noise variance. The effect of the sensor and source node positions on the location accuracy and the MSE is derived via the geometric dilution of precision (GDOP). The GDOP is then used to evaluate the effect of the joint estimation of unknown power and source location on the performance. Lower bounds on the GDOP are derived to obtain the minimum MSE. The RSSD solution is obtained by formulating the nonlinear and nonconvex objective functions into a convex optimization problem through relaxation and semidefinite programming. Simulation results are presented with the source inside or outside the sensor network.

A Bayesian Filtering Approach for Error Mitigation in Ultra-Wideband Ranging.

Ultra-wideband (UWB) sensors have been widely used in multi-robot systems for cooperative tracking and positioning purposes due to their advantages such as high ranging accuracy and good real-time performance. In order to reduce the influence of non-line-of-sight (NLOS) UWB communication caused by the presence of obstacles on ranging accuracy in indoor environments, the paper proposes a novel Bayesian filtering approach for UWB ranging error mitigation. Nonparametric UWB sensor models, namely received signal strength (RSS) model and time of arrival (TOA) model, are constructed to capture the probabilistic noise characteristics under the influence of different obstruction conditions and materials within a typical indoor environment. The proposed Bayesian filtering approach can be used either as a standalone error mitigation approach for peer-to-peer (P2P) ranging, or as a part of a higher level Bayesian state estimation framework. Experiments were conducted to validate and evaluate the proposed approach in two configurations, i.e., inter-robot ranging, and mobile robot tracking in a wireless sensor network. The experimental results show that the proposed method can accurately identify the line-of-sight (LOS) and NLOS scenarios with wood and metal obstacles in a probabilistic representation and effectively improve the ranging/tracking accuracy. In addition, the low computational overhead of the approach makes it attractive in real-time systems.

Reassessing the Evidence for Capacity Limits in Neural Signals Related to Working Memory.

In 2004, two landmark studies described the discovery of brain imaging (functional magnetic resonance imaging and electroencephalography) signals that increase with the number of items held in visual working memory (WM). These studies claimed that the signals leveled off (plateaued) once the number of memoranda reached the capacity of WM, as estimated by the prevailing model of the time. However, alternative models were not considered, and changing concepts of WM in the more than a decade since these studies were published necessitate a re-evaluation of their findings; newer models that provide the most accurate account of behavioral data do not incorporate a fixed limit on the number of items stored. Furthermore, an important claim made about the original studies, that signals plateau at each individual’s estimated capacity, has never been tested. Here, we pit the plateau model of signal strength against an alternative, saturation model, a biophysically plausible account in which signals increase continuously without plateau. We show that the saturation model provides a better description of the original data, challenging the assumption that imaging results provide evidence for a fixed item limit in WM.

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.

The study of access point outdoor coverage deployment for wireless digital campus network

Wireless local area network design needs more development to obtain appropriate and effective results. Site surveys in the design process give realistic results, but require time and effort. Developing ways of predicting signal strength using empirical models can give appropriate results in access point placement to get good signal coverage. Geospatial analysis, such as inverse distance weighting, Kriging and global polynomial interpolation, has been compared. This study showed that Kriging analysis is an appropriate method to predict value the coverage area. Furthermore, predictive signal strength models such as classical, empirical and COST 231 Hatta models have been studied. The empirical model was shown to do the best predictive calculations. The empirical model used to predict signal strength combined with Kriging geographical statistical analysis gave usable signal coverage prediction for access point placement. This model will support GIS spatial analysis tools to perform effective planning in access point placement.

A Three-State Received Signal Strength Model for Device-Free Localization

The indoor radio propagation channel is typically modeled as a two-state time-variant process, where one of the states represents the channel when the environment is static, whereas the other state characterizes the medium when it is altered by people. In this paper, the aforementioned process is augmented with an additional state. It is shown that the changes in received signal strength are dictated by: 1) electronic noise, when a person is not present in the monitored area; 2) reflection, when a person is moving in the close vicinity of line-of-sight; and 3) shadowing, when a person is obstructing the line-of-sight component of the transmitter-receiver pair. Statistical and spatial models for the three states are derived, and the models are empirically validated. Based on the models, a link line monitoring system is designed, which aims to, first, estimate the temporal state of the channel using a hidden Markov model, and, second, track a person using a particle filter. The results suggest that the presented system outperforms other state-of-the-art systems in terms of localization accuracy while increasing size of the link's sensing region.

Radio Frequency Mapping using an Autonomous Robot: Application to the 2.4 GHz Band

Radio signal strength measurement systems are essential to build a Radio Frequency (RF) mapping in indoor and outdoor environments for different application scenarios. This paper presents an autonomous robot making the construction of a radio signal mapping, by collecting and forwarding different useful information related to all access point devices and inherent to the robot towards the base station. A real case scenario is considered by measuring the RF field from our department network. The RF signal mapping consistency is shown by fitting the measurements with the radio signal strength model in two-dimensional area, and a path-loss exponent of 2.3 is estimated for the open corridor environment.

A Link Quality Estimation model of Wireless Sensor Networks for Smart Distribution Grid

Recent advances in Micro-electro-mechanical Systems made Wireless Sensor Network (WSN) a viable solution for Smart Distribution Grid communication. However, the transitional and environmentrelated features seriously affect communication link quality in Wireless-Sensor-Network-based smart distribution grid application. In this paper, a novel Link Quality Estimation model is presented to estimate link quality in harsh Distribution Grid environments. By theoretical analysis, background noise fluctuation and signal strength attenuation are considered as main factors affecting link quality. Consequently, Alpha-stable distribution fitting is used for modeling background noise, and Log-distance path loss model is adopted for received signal strength model. In addition, the method of estimating parameters in presented Link Quality Estimation model is introduced along. The experiment result demonstrates that, Link Quality Estimation model is a better and more realistic and accuracy choice.

Enforcement of γδ-lineage commitment by the pre–T-cell receptor in precursors with weak γδ-TCR signals

Developing thymocytes bifurcate from a bipotent precursor into αβ- or γδ-lineage T cells. Considering this common origin and the fact that the T-cell receptor (TCR) β-, γ-, and δ-chains simultaneously rearrange at the double negative (DN) stage of development, the possibility exists that a given DN cell can express and transmit signals through both the pre-TCR and γδ-TCR. Here, we tested this scenario by defining the differentiation outcomes and criteria for lineage choice when both TCR-β and γδ-TCR are simultaneously expressed in Rag2(-/-) DN cells via retroviral transduction. Our results showed that Rag2(-/-) DN cells expressing both TCRs developed along the γδ-lineage, down-regulated CD24 expression, and up-regulated CD73 expression, showed a γδ-biased gene-expression profile, and produced IFN-γ in response to stimulation. However, in the absence of Inhibitor of DNA-binding 3 expression and strong γδ-TCR ligand, γδ-expressing cells showed a lower propensity to differentiate along the γδ-lineage. Importantly, differentiation along the γδ-lineage was restored by pre-TCR coexpression, which induced greater down-regulation of CD24, higher levels of CD73, Nr4a2, and Rgs1, and recovery of functional competence to produce IFN-γ. These results confirm a requirement for a strong γδ-TCR ligand engagement to promote maturation along the γδ T-cell lineage, whereas additional signals from the pre-TCR can serve to enforce a γδ-lineage choice in the case of weaker γδ-TCR signals. Taken together, these findings further cement the view that the cumulative signal strength sensed by developing DN cells serves to dictate its lineage choice.

A NOVEL HYBRID NODE LOCALIZATION ALGORITHM FOR WIRELESS SENSOR NETWORKS

Node self-localization is one of the supporting technologies in Wireless Sensor Networks (WSNs). In this study, a distributed hybrid localization algorithm based on divide-and-conquer and refinement method is proposed. Firstly, the effect of ranging error in the Received Signal Strength (RSS) model on node localization is analyzed and the topology relations of anchors and unknown nodes are derived when the least localization error is achieved and then, the localization-union scheme is designed and refinement method is presented to improve the estimate accuracy. Finally, the simulation results prove that our proposed algorithm has the merits of superior performance, simple design and less overhead. Especially, it can use sparse anchors to achieve high localization accuracy.

Mobility-Aided Wireless Sensor Network Localization via Semidefinite Programming

In this paper, considering a mobile wireless sensor network, we study the problem of exploiting sensor mobility information in the process of sensor localization under two range measurement models, namely the time-of-arrival (TOA) model and the received signal strength (RSS) model. To do so, for each model, we first derive the maximum likelihood (ML) location estimator for the case of error-free velocity measurements. As the corresponding optimization problems are non-convex, we resort to semi-definite relaxation (SDR) techniques to find approximate solutions to each problem using semi-definite programming (SDP). We then extend our results to the cases where the velocity measurements are subject to measurement errors. Our simulation results show that exploiting the mobility information in the localization process can significantly improve the performance of the sensor localization. Moreover, mobility-aided localization has the potential to address some of typical positioning problems, such as sensitivity to the ranging measurement errors and the requirement on the number of the anchors needed to uniquely localize the sensor nodes.

SpiderRadio: A Cognitive Radio Implementation Using IEEE 802.11 Components

In this paper, we present SpiderRadio, a software defined cognitive radio (CR) prototype for dynamic spectrum access (DSA) networking. The medium access control (MAC) layer of SpiderRadio is implemented in software on top of commodity IEEE 802.11a/b/g hardware. However, the proposed architecture and implementation are applicable to other spectrum bands as well. We also present a dynamic spectrum sensing methodology for primary incumbent detection. The proposed method is based on observing the PHY errors, received signal strength and statistical model building. For coordination among radio nodes, synchronization and fast channel switching, we present new communication protocols, design extended management frame structure and modify the hardware abstraction layer. Several fundamental tradeoffs (e.g., complexity versus network performance) to be considered during a dynamic spectrum access radio network prototype implementation are also discussed in detail. To demonstrate the practical capabilities of the proposed SpiderRadio prototype, we also present various testbed experimental measurement results.