Signal Strength Measurements Research Articles

On the application of support vector machines to the prediction of propagation losses at 169 MHz for smart metering applications

Recently, the need of deploying new wireless networks for smart gas metering has raised the problem of radio planning in the 169 MHz band. Unluckily, software tools commonly adopted for radio planning in cellular communication systems cannot be employed to solve this problem because of the substantially lower transmission frequencies characterising this application. In this study, a novel data-centric solution, based on the use of support vector machine techniques for classification and regression, is illustrated. The proposed method requires the availability of a limited set of received signal strength measurements and the knowledge of a three-dimensional map of the propagation environment of interest and generates both an estimate of the coverage area and a prediction of the field strength within it. Various numerical results show that the proposed method is able to achieve good accuracy at the price of an acceptable computational cost and of a limited effort for the acquisition of measurements in the considered environments.

Eukaryotic translational termination efficiency is influenced by the 3' nucleotides within the ribosomal mRNA channel.

When a stop codon is at the 80S ribosomal A site, there are six nucleotides (+4 to +9) downstream that are inferred to be occupying the mRNA channel. We examined the influence of these downstream nucleotides on translation termination success or failure in mammalian cells at the three stop codons. The expected hierarchy in the intrinsic fidelity of the stop codons (UAA>UAG>>UGA) was observed, with highly influential effects on termination readthrough mediated by nucleotides at position +4 and position +8. A more complex influence was observed from the nucleotides at positions +5 and +6. The weakest termination contexts were most affected by increases or decreases in the concentration of the decoding release factor (eRF1), indicating that eRF1 binding to these signals was rate-limiting. When termination efficiency was significantly reduced by cognate suppressor tRNAs, the observed influence of downstream nucleotides was maintained. There was a positive correlation between experimentally measured signal strength and frequency of the signal in eukaryotic genomes, particularly in Saccharomyces cerevisiae and Drosophila melanogaster. We propose that termination efficiency is not only influenced by interrogation of the stop signal directly by the release factor, but also by downstream ribosomal interactions with the mRNA nucleotides in the entry channel.

The Accurate Location Estimation of Sensor Node Using Received Signal Strength Measurements in Large-Scale Farmland

The range measurement is the premise for location, and the precise range measurement is the assurance of accurate location. Hence, it is essential to know the accurate internode distance. It is noted that the path loss model plays an important role in improving the quality and reliability of ranging accuracy. Therefore, it is necessary to investigate the path loss model in actual propagation environment. Through the analysis of experiments performed at the wheat field, we find that the best fitted parametric exponential decay model (OFPEDM) can achieve a higher distance estimation accuracy and adaptability to environment variations in comparison to the traditional path loss models. Based on the proposed OFPEDM, we perform the RSSI-based location experiments in wheat field. Through simulating the location characteristics in MATLAB, we find that for all the unknown nodes, the location errors range from 0.0004 m to 5.1739 m. The location error in this RSSI-based location algorithm is acceptable in the wide areas such as wheat field. The findings in this research may provide reference for location estimation in large-scale farmland.

An Interference Contribution Rate Based Small Cells On/Off Switching Algorithm for 5G Dense Heterogeneous Networks

Dense heterogeneous network serves as one of the most promising technologies for the upcoming 5G network. However, with a larger number of densely deployed small cells, the problem of co-channel interference and power consumption of 5G small cell base stations (also known as S-gNBs) becomes more severe. In order to improve the power efficiency and alleviate the co-channel interference, S-gNBs in the network need to be dynamically switched on or off via appropriate criteria or strategies. This paper proposes an interference contribution rate (ICR) based small cell on/off switching algorithm. With the incorporation of network adjacency matrix (NAM), the proposed algorithm circumvents the complicated computation of the ICRs of the S-gNBs in the network and hence yields less computational complexity. In addition, the proposed algorithm identifies the S-gNBs that need to be switched on or off based on the NAM and the serving signal strength measurements of user equipment’s and therefore involves less signaling information in the procedure of switching on/off decision. Simulation results verify that the proposed algorithm effectively diminishes the co-channel interference among the small cells, ameliorates the network power efficiency and total data rate, and meanwhile maintains a lower traffic loss in the network.

Efficient key generation leveraging wireless channel reciprocity for MANETs

Wireless channel reciprocity ensures that transceivers can generate the shared secret key from their communicating channels. However, various interferences in real environments make channel measurement sequences of different transceivers are highly correlated accompanying with many discrepancies. Then, if these measurements are directly quantified (and encoded) to bit sequences, the bit mismatch rate might be extremely high. Consequently, the efficiency and security of key generation might be reduced since some information might be leaked during key agreement. In this paper, an efficient key generation scheme is proposed, which is available to generate the shared secret key for transceivers even facing the referred challenge. During key generation, curve fitting is implemented to pre-process measurements so that the reciprocity of outputs of curve fitting processes are better than original channel measurements. Then, the bit mismatch rate can be greatly reduced when these outputs are used to generate bit sequences. To extract the shared bit sequence, the interactive Cascade is implemented during information reconciliation. Finally, 2-universal hash functions are used to achieve privacy amplification so that the leaked information can be eliminated and randomness of the shared key can be guaranteed. Several experiments in real mobile scenarios are conducted to evaluate the proposed scheme, where the received signal strength measurements are derived from heterogeneous devices. The results show that the implementation of pre-processing can effectively reduce the bit mismatch rate and the proposed scheme can efficiently generate shared secret keys for transceivers in real environments.

In situ antenna diagnostics and characterization system based on RFID and Remotely Piloted Aircrafts

A cost-effective solution for in situ antenna characterization and diagnostics based on Radio Frequency IDentification technology and Remotely Piloted Aircrafts is presented. The cost-effectiveness of the proposed solution is achieved by replacing the radio frequency equipment on-board the small aircrafts with Radio Frequency IDentification tags, while the Antenna Under Test is connected to a Radio Frequency IDentification reader, thus reducing the weight and complexity of the payload of the aircrafts dedicated to antenna measurement task. Received Signal Strength measurements are geo-referred with centimeter-level accuracy thanks to a Real Time Kinematic system. An iterative phase retrieval technique based on the Sources Reconstruction Method is used to recover an equivalent magnetic currents distribution on the Antenna Under Test aperture plane. The reconstructed currents distribution provides antenna diagnostics information and enables the calculation of the antenna radiation pattern. The presented method has been validated by means of measurements in the UHF band for two different antenna arrays with excellent results.

Neighbor-Aided Localization in Vehicular Networks

We address the problem of localization in vehicular ad hoc networks. Our goal is to leverage vehicle communications and smartphone sensors to improve the overall localization performance. Assuming vehicles are equipped with the IEEE 802.11p wireless interfaces, we employ a two-stage Bayesian filter to track the vehicle’s position: an unscented Kalman filter for heading estimation using smartphone inertial sensors, and a particle filter that fuses vehicle-to-vehicle signal strength measurements received from mobile anchors whose positions are uncertain, with velocity, GPS position, and map information. Our model leads to a robust localization system and is able to provide useful position information even in the absence of GPS data. We evaluate the algorithm performance using real-world measurements collected from four communicating vehicles in an urban scenario, and considering different combinations of location information sources.

Efficient key generation leveraging channel reciprocity and balanced gray code

Key generation leveraging wireless channel reciprocity can establish secret keys from unauthenticated broadcast channels and thus protect the communication of wireless networks. However, in real environments, the measurements collected by the involved transceivers might have many discrepancies due to the non-simultaneous directional measurements and other factors, such as hardware differences and asymmetric interferences. Meanwhile, if the multi-level quantization in conjunction with source coding is implemented to convert measurements to bits, the resulting high bit mismatch rate makes key agreement inefficient and insecure. In this paper, key generation schemes leveraging channel reciprocity and balanced gray code are proposed. To enhance the efficiency of key generation, an efficient encoding scheme based on balanced gray code is carried out during key generation. The proposed encoding scheme can simultaneously reduce the bit mismatch rate and improve the secret bit rate. In addition, the code book is reusable once it is generated. The proposed key generation schemes can be classified into the uniform and non-uniform schemes according to different quantization processes. Several experiments in real mobile environments are conducted to validate the proposed schemes. The received signal strength measurements are extracted from heterogeneous devices as statistics of channel states in each experiment. The results of these experiments show that (1) the proposed encoding scheme significantly enhances the efficiency of key generation; (2) both the uniform and non-uniform schemes can efficiently generate secret keys between heterogeneous transceivers.

Do it yourself: optical spectrometer for physics undergraduate instruction in nanomaterial characterization

In this paper, we report on a homemade optical spectrometer using diffraction grating and image processing techniques. This device was designed to produce spectral images that could then be processed by measuring signal strength (pixel intensity) to obtain the light source, transmittance, and absorbance spectra of the liquid sample. The homemade optical spectrometer consisted of: (i) a white LED as a light source, (ii) a cuvette or sample holder, (iii) a slit, (iv) a diffraction grating, and (v) a CMOS camera (webcam). In this study, various concentrations of a carbon nanoparticle (CNP) colloid were used in the particle size sample test. Additionally, a commercial optical spectrometer and tunneling electron microscope (TEM) were used to characterize the optical properties and morphology of the CNPs, respectively. The data obtained using the homemade optical spectrometer, commercial optical spectrometer, and TEM showed similar results and trends. Lastly, the calculation and measurement of CNP size were performed using the effective mass approximation (EMA) and TEM. These data showed that the average nanoparticle sizes were approximately 2.4 nm and 2.5 ± 0.3 nm, respectively. This research provides new insights into the development of a portable, simple, and low-cost optical spectrometer that can be used in nanomaterial characterization for physics undergraduate instruction.

A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques

In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both methods consists of three steps: 1) coarse localization, obtained from comparing the online measurements with clustered radio map. A novel graph-based method is proposed to cluster the offline fingerprints. In the online phase, a Region Of Interest (ROI) is selected within which we search for the user's location; 2) Access Point (AP) selection; and 3) fine localization through the novel sparse recovery algorithms. Since the online measurements are subject to inordinate measurement readings, called outliers, the sparse recovery methods are modified in order to jointly estimate the outliers and user's position vector. The outlier detection procedure identifies the APs whose readings are either not available or erroneous. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with significantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods.

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.

ARTI: An Adaptive Radio Tomographic Imaging System

Radio tomographic imaging systems use received signal strength measurements between static wireless sensors to image the changes in the radio propagation environment in the area of the sensors, which can be used to localize a person causing the change. To date, spatial models used for such systems are set a priori and do not change. Imaging and tracking performance suffers because of the mismatch between the model and the measurements. Collecting labeled training data requires intensive effort, and the data degrade quickly as the environment changes. This paper provides a means for a radio tomographic imaging system to bootstrap to improve its spatial models using unlabeled data, iteratively improving itself over time. A collection of tracking filters are presented to improve the accuracy of image and coordinate estimates. This paper presents an online method to use these estimates to instantaneously update spatial model parameters. Further, a smoothing method is presented to fine-tune the model with a given finite latency. The development efforts are evaluated using simulations and validated with real-world experiments conducted in three different environments. With respect to another state-of-the-art radio tomographic imaging system, the results suggest that the presented system increases the median tracking accuracy by twofold in the most challenging environment and by threefold when the model parameters are trained using the smoothing method.

Structured Group Sparsity: A Novel Indoor WLAN Localization, Outlier Detection, and Radio Map Interpolation Scheme

This paper introduces novel schemes for indoor localization, outlier detection, and radio map interpolation using wireless local area networks. The localization method consists of a novel multicomponent optimization technique that minimizes the squared $\ell _{2}$ -norm of the residuals between the radio map and the online received signal strength (RSS) measurements, the $\ell _{1}$ -norm of the user's location vector, and weighted $\ell _{2}$ -norms of layered groups of reference points (RPs). RPs are grouped using a new criterion based on the similarity between the so-called access point (AP) coverage vectors. In addition, since AP readings are prone to containing inordinate readings, called outliers, an augmented optimization problem is proposed to detect the outliers and localize the user with cleaned online measurements. Moreover, a novel scheme to record fingerprints from a smaller number of RPs and estimate the radio map at RPs without recorded fingerprints is developed using sparse recovery techniques. All localization schemes are tested on RSS fingerprints collected from a real environment. The overall scheme has comparable complexity with competing approaches, while it performs with high accuracy under a small number of APs and finer granularity of RPs.

Robust Differential Received Signal Strength-Based Localization

Source localization based on signal strength measurements has become very popular due to its practical simplicity. However, the severe nonlinearity and non-convexity make the related optimization problem mathematically difficult to solve, especially when the transmit power or the path-loss exponent (PLE) is unknown. Moreover, even if the PLE is known but not perfectly estimated or the anchor location information is not accurate, the constructed data model will become uncertain, making the problem again hard to solve. This paper particularly focuses on differential received signal strength (DRSS)-based localization with model uncertainties in case of unknown transmit power and PLE. A new whitened model for DRSS-based localization with unknown transmit powers is first presented and investigated. When assuming the PLE is known, we introduce two estimators based on an exact data model, an advanced best linear unbiased estimator (A-BLUE) and a Lagrangian estimator (LE), and then we present a robust semidefinite programming (SDP)-based estimator (RSDPE), which can cope with model uncertainties (imperfect PLE and inaccurate anchor location information). The three proposed estimators have their own advantages from different perspectives: the A-BLUE has the lowest complexity; the LE holds the best accuracy for a small measurement noise; and the RSDPE yields the best performance under a large measurement noise and possesses a very good robustness against model uncertainties. Finally, we propose a robust SDP-based block coordinate descent estimator (RSDP-BCDE) to deal with a completely unknown PLE and its performance converges to that of the RSDPE using a perfectly known PLE.

Fusing different measurements and algorithms to improve RFID localization accuracy

This paper considers the topic of two-dimensional object localization using radio frequency identification (RFID) technology. One of the important problems that arise during the development of RFID positioning systems is choosing a localization algorithm and a type of measurement data. Usually developers try to find such a combination of an algorithm and measurement type that allows to achieve maximal localization accuracy for a particular scenario. However, in some situations there can be several combinations of algorithms and measurements with equally high accuracy. In order to simplify the choosing problem and to additionally increase accuracy it is developed a combinational localization method. The method is based on averaging position estimates formed by several point-based and zone-based algorithms that process different measurements. In our work there are used three point-based and three zone-based algorithms: a k nearest neighbors algorithm, trilateration, intersectional algorithm, the methods of support vector machine, artificial neural networks, and a naive Bayes classifier. As an input for the algorithms we utilized received signal strength, read rate, and proximity measurements. During the experiments we found that our method decreases the mean error by 15% and the maximum error by 14% compared to the best single algorithm.

Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction.

Device-free localization (DFL) is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF) DFL system, radio transmitters (RTs) and radio receivers (RXs) are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS) measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM) approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE) is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN), support vector machine (SVM), back propagation neural network (BPNN), as well as the well-known radio tomographic imaging (RTI) DFL approach.

Homing Guidance Using Spatially Quantized Signals

This paper considers homing guidance for a vehicle with a single omnidirectional receiver traveling to a stationary, omnidirectional transmitting beacon by using spatially quantized signal strength measurements. Two homing strategies are presented, and simulations are performed for cases with signal noise and vehicle turn rate limits. The first strategy is the Oyler strategy, which adapts a sliding mode controller and observer from the previous work. The second strategy is based on constant heading changes (CHCs) each time a range increment is detected, and this strategy is shown to be sufficient for homing. This study also discusses a signal filter designed to improve the homing controllers' performance. Performance metrics are developed for strategy evaluation and parameter optimization. The performance of each guidance strategy is shown through simulations for a variety of conditions. The Oyler strategy guides the vehicle to the beacon more efficiently than the constant heading change strategy, but it comes with a slight penalty in success rate.

Fuzzy extended Kalman filter for dynamic mobile localization in urban area using wireless network

The problem of accurate mobile positioning in cellular network is very challenging and still subject to intensive research, especially given the uncertainty pervading the signal strength measurements. This paper advocates the use of fuzzy based reasoning in conjunction with Kalman filtering like approach in order to enhance the localization accuracy. The methodology uses TEMS Investigation software to retrieve network information including signal strength and cell-identities of various base transmitter stations (BTS). The distances from the mobile station (MS) to each BTS are therefore generated using Walfish-Ikigami radio propagation model. The performances of the established hybrid estimator −fuzzy extended Kalman filter (FEKF)- are compared with extended Kalman filter approach and fuzzy-control based approach. Both simulation and real-time testing results demonstrate the feasibility and superiority of the FEKF localization based approach.

On Log-Normality of RSSI in Narrowband Receivers Under Static Conditions

A growing set of environmental sensing applications use received signal strength measurements of a static wireless network for unobtrusive monitoring purposes. The success of these systems, which typically process low-amplitude signals, depend strongly on the distribution of the measurements when there are no changes in the channel. In this letter, a statistical model for signal strength measurements acquired when the environment is static is studied. As previously empirically verified, it is shown that the measurements have log-normal distribution even in idealistic environments, which cannot be explained using log-normal shadow fading arguments. Quantization and round-off errors induced by different measurement system components are also considered, and their impact are analyzed. As a result, it is shown that the logarithmic received signal strength measurements under static channel conditions are samples from stationary Gaussian process independent of the environment.

Influence of cataract on image quality and macular thickness measured using spectral domain optical coherence tomography: a prospective cohort study

Background: Optical coherence tomography (OCT) is one of the most useful imaging techniques in the management of retinal diseases affecting the macula. Factors affecting the image quality of OCT may affect the macular thickness and hence treatment of retinal pathologies. This study was aimed to evaluate the influence of cataract on macular scans using spectral domain OCT.Methods: The prospective study was done in 36 patients with cataract. Cataract was graded as cortical, nuclear or posterior cataracts. All patients underwent small incision cataract surgery. Pre and post-operative imaging of the macula (4 weeks after surgery) using spectral domain OCT. The signal strength and central macular thickness (CMT) measurements were made before and after the surgery and analysed statistically.Results: The majority (66.7%) of patients had posterior cataracts. A statistically significant improvement in OCT image quality (as assessed by signal strength) was noted post operatively. This was maximum for posterior cataracts (2.9), followed by cortical cataract (1.6), while nuclear cataracts had the least increase in signal strength (1.3). The difference in signal strength gain between posterior and nuclear cataracts was found to be statistically significant (p = 0.05). The increase in CMT was clinically significant only in 8.2% of the study population. No patient with nuclear cataract had a clinically significant increase in CMT following surgery.Conclusions: Cataract can influence OCT image quality as well as central macular thickness measurements. Both were found to be affected maximally by posterior cataracts, followed by cortical, while nuclear cataracts had the least influence.