Literatura académica sobre el tema "Received signal power (RSS)"
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Artículos de revistas sobre el tema "Received signal power (RSS)"
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Xu, Yihuai, Xin Hu, Yimao Sun, et al. "High-Accuracy Height-Independent 3D VLP Based on Received Signal Strength Ratio." Sensors 22, no. 19 (2022): 7165. http://dx.doi.org/10.3390/s22197165.
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Visible light positioning (VLP) has attracted intensive attention from both academic and industrial communities thanks to its high accuracy, immunity to electromagnetic interference, and low deployment cost. In general, the receiver in a VLP system determines its own position by exploring the received signal strength (RSS) from the transmitter according to a pre-built RSS attenuation model. In such model-based methods, the LED’s emission power and the receiver’s height are usually required known and constant parameters to obtain reasonable positioning accuracy. However, the LED’s emission power is normally time-varying due to the fact that the LED’s optical output power is prone to changing with the LED’s temperature, and the receiver’s height is random in a realistic application scenario. To this end, we propose a height-independent three-dimensional (3D) VLP scheme based on the RSS ratio (RSSR), rather than only using RSS. Unlike existing RSS-based VLP methods, our method is able to independently find the horizontal coordinate, i.e., two-dimensional (2D) position, without a priori height information of the receiver, and also avoids the negative effect caused by fluctuation of the LED’s emission power. Moreover, we can further infer the height of the receiver to achieve three-dimensional (3D) positioning by iterating the 2D results back into positioning equations. To quickly verify the proposed scheme, we conduct theoretical analysis with mathematical proof and experimental results with real data, which confirm that the proposed scheme can achieve high position accuracy without known information of the receiver’s height and LED’s emission power. We also implement a VLP prototype with five LED transmitters, and experimental results show that the proposed scheme can achieve very low average errors of 2.73 cm in 2D and 7.20 cm in 3D.
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Raes, Willem, Nicolas Knudde, Jorik De Bruycker, Tom Dhaene, and Nobby Stevens. "Experimental Evaluation of Machine Learning Methods for Robust Received Signal Strength-Based Visible Light Positioning." Sensors 20, no. 21 (2020): 6109. http://dx.doi.org/10.3390/s20216109.
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In this work, the use of Machine Learning methods for robust Received Signal Strength (RSS)-based Visible Light Positioning (VLP) is experimentally evaluated. The performance of Multilayer Perceptron (MLP) models and Gaussian processes (GP) is investigated when using relative RSS input features. The experimental set-up for the RSS-based VLP technology uses light-emitting diodes (LEDs) transmitting intensity modulated light and a single photodiode (PD) as a receiver. The experiments focus on achieving robustness to cope with unknown received signal strength modifications over time. Therefore, several datasets were collected, where per dataset either the LEDs transmitting power is modified or the PD aperture is partly obfuscated by dust particles. Two relative RSS schemes are investigated. The first scheme uses the maximum received light intensity to normalize the received RSS vector, while the second approach obtains RSS ratios by combining all possible unique pairs of received intensities. The Machine Learning (ML) methods are compared to a relative multilateration implementation. It is demonstrated that the adopted MLP and GP models exhibit superior performance and higher robustness when compared to the multilateration strategies. Furthermore, when comparing the investigated ML models, the GP model is proven to be more robust than the MLP for the considered scenarios.
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Veselý, Jiří, Petr Hubáček, and Jana Olivová. "The Power Gain Difference Method Analysis." Sensors 20, no. 11 (2020): 3018. http://dx.doi.org/10.3390/s20113018.
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In this paper, we propose a new approach to passively locate the 3D position of a signal source. This novel technique, called the power gain difference (PGD), is based only on measuring the received signal strength (RSS) with multiple sensors deployed in the area of interest, while the target transmit power or the equivalent isotropic radiated power (EIRP) is assumed to be unknown. Next, the signal source position is estimated using the knowledge of the ratios of RSS measured on different sensors. First, this article presents the geometric representation and the analytical solution of the model of the PGD technique. Second, the PGD dilution of precision was analyzed in order to gauge the accuracy of measuring the RSS. Finally, a numerical simulation of the performance of the proposed method was carried out and the results are discussed. It seems that the PGD technique has the potential to be a simple and effective solution of the 3D localization problem.
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Martínez-Ciro, Roger Alexander, Francisco Eugenio López-Giraldo, José Martín Luna-Rivera, and Atziry Magaly Ramírez-Aguilera. "An Indoor Visible Light Positioning System for Multi-Cell Networks." Photonics 9, no. 3 (2022): 146. http://dx.doi.org/10.3390/photonics9030146.
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Indoor positioning systems based on visible light communication (VLC) using white light-emitting diodes (WLEDs) have been widely studied in the literature. In this paper, we present an indoor visible-light positioning (VLP) system based on red–green–blue (RGB) LEDs and a frequency division multiplexing (FDM) scheme. This system combines the functions of an FDM scheme at the transmitters (RGB LEDs) and a received signal strength (RSS) technique to estimate the receiver position. The contribution of this work is two-fold. First, a new VLP system with RGB LEDs is proposed for a multi-cell network. Here, the RGB LEDs allow the exploitation of the chromatic space to transmit the VLP information. In addition, the VLC receiver leverages the responsivity of a single photodiode for estimating the FDM signals in RGB lighting channels. A second contribution is the derivation of an expression to calculate the optical power received by the photodiode for each incident RGB light. To this end, we consider a VLC channel model that includes both line-of-sight (LOS) and non-line-of-sight (NLOS) components. The fast Fourier transform (FFT) estimates the powers and frequencies of the received FDM signal. The receiver uses these optical signal powers in the RSS-based localization application to calculate the Euclidean distances and the frequencies for the RGB LED position. Subsequently, the receiver’s location is estimated using the Euclidean distances and RGB LED positions via a trilateration algorithm. Finally, Monte Carlo simulations are performed to evaluate the error performance of the proposed VLP system in a multi-cell scenario. The results show a high positioning accuracy performance for different color points. The average positioning error for all chromatic points was less than 2.2 cm. These results suggest that the analyzed VLP system could be used in application scenarios where white light balance or luminaire color planning are also the goals.
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Ahmad, A., P. Claudio, A. Alizadeh Naeini, and G. Sohn. "WI-FI RSS FINGERPRINTING FOR INDOOR LOCALIZATION USING AUGMENTED REALITY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2020 (August 3, 2020): 57–64. http://dx.doi.org/10.5194/isprs-annals-v-4-2020-57-2020.
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Abstract. Indoor localization has attracted the attention of researchers for wide applications in areas like construction, facility management, industries, logistics, and health. The Received Signal Strength (RSS) based fingerprinting method is widely adopted because it has a lower cost over other methods. RSS is a measurement of the power present in the received radio signal. While this fingerprinting method is very popular, there is a significant amount of effort required for collecting fingerprints for indoor space. In this paper, we propose an RSS fingerprinting method using Augmented Reality (AR) that does not rely on an external sensor resulting in ease of use and maintenance. This method uses spatial mapping techniques to help align the floor plan of existing buildings; then, after the alignment, we map local device coordinates to global coordinates. After this process, we partition the space in equally distanced reference points for RSS fingerprint collection. We developed an application for Microsoft HoloLens to align the floor plan and collect fingerprints on reference points. Then we tested collected fingerprints with existing RSS based indoor localization methods for its accuracy and performance.
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Muttair, Karrar Shakir, Mahmood Farhan Mosleh, and Oras Ahmed Shareef. "Optimal transmitter location using multi-scale algorithm based on real measurement for outdoor communication." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1384. http://dx.doi.org/10.11591/ijai.v11.i4.pp1384-1394.
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In the era of wireless network communication, the demand for determining the best transmitter (Tx) location is constantly increasing especially in outdoor environments and localization-based services. In this paper, localization optimal location of a Tx has been achieved in electrical engineering technical college by using received signal strength (RSS). We install practical devices in the college to measure the received signal strength in all the college regions. Where each device consists of two pieces which are the AirMAX sector directional antenna and signal broadcast piece (router board). We measured RSS in four directions for each received point in all selected regions using the NetSpot software. An algorithm was created in MATLAB software to determine the average received power (ARP), average signal quality (ASQ) as well as calculate the average coverage (AC) for each Tx device location based on the locations deployed in the real environment. Overall, the results obtained showed that this algorithm effectively demonstrated that it is able to determine the best location of a Tx device compared to other deployed locations in the college. Furthermore, we find large effects when a Tx location changes from one location to another on the RSS because of the surrounding environment effects.
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Sergi, Simone, Fabrizio Pancaldi, and Giorgio M. Vitetta. "Cluster-Based Ranging for Accurate Localization in Wireless Sensor Networks." International Journal of Navigation and Observation 2010 (July 29, 2010): 1–11. http://dx.doi.org/10.1155/2010/460860.
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A novel ranging technique based on received signal strength (RSS) and suitable to indoor scenarios is illustrated. In the proposed technique, multiple power measurements, associated with the signals radiated by a cluster of nodes surrounding a given target, are jointly processed to improve the quality of RSS-based estimation of the distance between the target and an anchor. Specific algorithms for the generation of a cluster and for the acquisition of power measurements are described. Simulation results show that, when used in indoor positioning systems, the proposed ranging technique is substantially more accurate than noncooperative strategies. In addition, it allows to concentrate significant processing tasks in a limited number of fixed anchors, so reducing maintenance costs and making it possible to adopt cheap and simple portable wireless nodes.
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Rzymowski, Mateusz, Krzysztof Nyka, and Lukasz Kulas. "Direction of Arrival Estimation Based on Received Signal Strength Using Two-Row Electronically Steerable Parasitic Array Radiator Antenna." Sensors 22, no. 5 (2022): 2034. http://dx.doi.org/10.3390/s22052034.
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In this paper, we present a novel approach to direction-of-arrival (DoA) estimation using two-row electronically steerable parasitic array radiator (ESPAR) antenna which has 12 passive elements and allows for elevation and azimuth beam switching using a simple microcontroller, relying solely on received signal strength (RSS) values measured at the antenna output port. To this end, we thoroughly investigate all 18 available 3D antenna radiation patterns of the antenna measured in an anechoic chamber with respect to radiation coverage in the horizontal and vertical direction and propose a generalization of the power-pattern cross-correlation (PPCC) algorithm involving a high number of multiple calibration planes (MCP) as well as specific combinations of radiation pattern sets. Additionally, a new way of RSS-based DoA estimation accuracy assessment, which involves thorough testing conducted along the elevation direction when RF signals impinging on the antenna arrive from arbitrary θ angles, has been reported in this paper to verify the overall algorithm’s performance. The results obtained for different signal-to-noise ratio (SNR) levels indicate that two-row ESPAR antenna can produce, even for low SNR values, accurate DoA estimation in the horizontal plane without prior knowledge about the elevation direction of the unknown RF signals by using appropriate combinations of only 12 3D antenna radiation patterns.
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Bi, Jingxue, Yunjia Wang, Xin Li, Hongxia Qi, Hongji Cao, and Shenglei Xu. "An Adaptive Weighted KNN Positioning Method Based on Omnidirectional Fingerprint Database and Twice Affinity Propagation Clustering." Sensors 18, no. 8 (2018): 2502. http://dx.doi.org/10.3390/s18082502.
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The human body has a great influence on Wi-Fi signal power. A fixed K value leads to localization errors for the K-nearest neighbor (KNN) algorithm. To address these problems, we present an adaptive weighted KNN positioning method based on an omnidirectional fingerprint database (ODFD) and twice affinity propagation clustering. Firstly, an OFPD is proposed to alleviate body’s sheltering impact on signal, which includes position, orientation and the sequence of mean received signal strength (RSS) at each reference point (RP). Secondly, affinity propagation clustering (APC) algorithm is introduced on the offline stage based on the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed for estimating user’s position during online stage. K initial RPs can be obtained by KNN, then they are clustered by APC algorithm based on their position-domain distances. The most probable sub-cluster is reserved by the comparison of RPs’ number and signal-domain distance between sub-cluster center and the online RSS readings. The weighted average coordinates in the remaining sub-cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 m, the root mean square error of 1.5 m. Experimental results show that our proposed method outperforms traditional fingerprinting methods.
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Perihanoglu, G. M., and H. Karaman. "SPATIAL PREDICTION OF RECEIVED SIGNAL STRENGTH FOR CELLULAR COMMUNICATION USING SUPPORT VECTOR MACHINE AND K-NEAREST NEIGHBOURS REGRESSION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W9-2024 (March 8, 2024): 291–97. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w9-2024-291-2024.
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Abstract. Signal strength maps are of great importance for cellular system providers in network planning and operation. Accurate prediction of signal strength is important for solving problems such as link quality. In this study, Received Signal Strength (RSS) prediction model is proposed for the 900 MHz band in the Van Yüzüncü Yıl University campus environment by using machine learning regression methods such as K- Nearest Neıghbours (KNN) and Support Vector Regression (SVR) together with Geographic Information Systems. For the training of this model, signal strength values taken from the RF Spectrum Analyser at different locations and distances were used. In addition, spatial data sets such as the digital elevation model, location of base stations and measurement stations, building heights and location, and land use/cover were used in the model. The effect of these data sets on RSS power is included in the model. The model aims to predict RSS accurately, visualize the estimated signal strength, and analyze the signal field strength coverage. Different kernels from the SVR model such as Polynomial, , and Sigmoid were tested. To increase the success of the model, appropriate parameter values were selected and configured according to SVR and KNN methods. For 900 MHz, the performances of SVR and KNN models were compared and the results of the models were verified using root mean squares (RMSE). Among the measured data, the lowest prediction is found in KNN Manhattan. According to the results of the simulation was observed that the SVR model created with spatial data performs better for Signal Strength. Finally, the lowest RMSE value (1.71 dB) was obtained from the Sigmoid kernel in the best signal strength estimation SVR model. The SVR model is recommended for Campus Area signal strength estimation.
Tesis sobre el tema "Received signal power (RSS)"
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Obeidat, Huthaifa A. N. "Investigation of Indoor Propagation Algorithms for Localization Purposes: Simulation and Measurements of Indoor Propagation Algorithms for Localization Applications using Wall Correction Factors, Local Mean Power Estimation and Ray Tracing Validations." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17385.
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The objective of this work is to enhance the awareness of the indoor propagation behaviour, by a set of investigations including simulations and measurements. These investigations include indoor propagation behaviour, local mean power estimation, proposing new indoor path loss model and introducing a case study on 60 GHz propagation in indoor environments using ray tracing and measurements.
A summary of propagation mechanisms and manifestations in the indoor environment is presented. This comprises the indoor localization techniques using channel parameters in terms of angle of arrival (AOA), time of arrival (TOA) and received signal strength (RSS). Different models of path loss, shadowing and fast fading mechanisms are explored. The concept of MIMO channels is studied using many types of deterministic channel modelling such as Finite Difference Time Domain, Ray tracing and Dominant path model.
A comprehensive study on estimating local average of the received signal strength (RSS) for indoor multipath propagation is conducted. The effect of the required number of the RSS data and their Euclidian distances between the neighbours samples are investigated over 1D, 2D and 3D configurations. It was found that the effect of fast fading was reduced sufficiently using 2D horizontal’s arrangement with larger spacing configuration.
A modified indoor path loss prediction model is presented namely effective wall loss model (EWLM). The modified model with wall correction factors is compared to other indoor path loss prediction models using simulation data (for 2.4, 5, 28, 60 and 73.5 GHz) and real-time measurements (for 2.4 and 5 GHz). Different operating frequencies and antenna polarizations are considered to verify the observations. In the simulation part, EWLM shows the best performance among other models. Similar observations were recorded from the experimental results.
Finally, a detailed study on indoor propagation environment at 60 GHz is conducted. The study is supported by Line of Sight (LoS) and Non-LoS measurements data. The results were compared to the simulated ones using Wireless-InSite ray tracing software. Several experiments have confirmed the reliability of the modelling process based on adjusted material properties values from measurements.
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Liu, Siyang. "Efficient machine learning techniques for indoor localization in wireless communication systems." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST188.
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Avec le développement rapide de l'Internet des objets (IoT), le besoin de services de localisation en intérieur, tels que la gestion des actifs, la navigation et le suivi, a également augmenté au fil du temps. Pour la localisation indoor, les systèmes de navigation par satellite tels que le GPS ont un usage limité par l'absence de visibilité directe avec les satellites.Diverses solutions ont été proposées pour la localisation en intérieur, telles que la trilatération, la triangulation et la navigation à l'estime, mais leurs performances sont limitées par les conditions du canal intérieur, telles que masquage et l'évanouissement par trajets multiples. En exploitant la correspondance entre les mesures des caractéristiques du signal sans fil et les positions, les méthodes basées sur l'empreinte digitale ont montré qu'elles pouvaient fournir de bonnes performances de localisation avec suffisamment de données. Cependant, la localisation indoor est toujours confrontée à des défis tels que l'évolutivité, le coût et la complexité, la confidentialité, etc.L'objectif de cette thèse est d'améliorer l'efficience de la localisation indoor en utilisant des techniques d'apprentissage automatique. Nous divisons le processus de localisation en deux phases : la phase de cartographie radio hors ligne et la phase de localisation en ligne. Pendant la phase hors ligne, nous introduisons l'analyse des jeux de données comme une étape intermédiaire entre la création des jeux de données et la localisation. Nous proposons deux indicateurs numériques de qualité des jeux de données qui peuvent fournir un retour d'information pour améliorer la carte radio. De plus, l'extraction de caractéristiques et le traitement des données à l'aide d'outils d'apprentissage automatique sont intégrés dans un but d'efficience, en réduisant la taille des données et la complexité de calcul, tout en améliorant les performances de localisation. Pour être plus précis, nous proposons une méthode de cartographie radio basée sur les k-means qui peut réduire le nombre d'empreintes de plus de 90 % sans perdre d'informations utiles dans la carte radio ni dégrader les performances de localisation. En explorant la nature hiérarchique des grands ensembles de données, nous proposons une méthode d'extraction de caractéristiques hiérarchiques qui peut réduire davantage la complexité de la localisation sans en compromettre les performances.Pour la phase de localisation en ligne, nous explorons à la fois l'apprentissage automatique traditionnel et l'apprentissage profond. Nous présentons d'abord plusieurs méthodes traditionnelles d'apprentissage automatique et comparons les performances de localisation sur des ensembles de données publiques. Nous cherchons à améliorer les performances de localisation des méthodes traditionnelles.Pour faire face aux problèmes de confidentialité et de complexité, nous introduisons un cadre d'apprentissage fédéré pour le problème de la localisation indoor. Dans ce cadre, les clients partagent uniquement leurs modèles locaux avec le serveur central au lieu des données d'empreintes radios. Nous comparons d'abord les performances de l'apprentissage fédéré et centralisé. Ensuite, nous étudions l'impact du nombre de clients et de la taille des données locales. Toujours dans un objectif d'efficience, afin d'économiser les ressources, nous cherchons à réduire le coût de communication pendant le processus d'apprentissage. Nous évaluons différentes techniques, notamment la sélection des clients, l'accumulation du gradient et la compression du modèle. Une méthode de compression efficace est proposée pour compresser les modèles locaux, ce qui permet de réduire le coût de communication de la liaison montante de 91,5 % sans compromettre les performances de localisation. Enfin, nous considérons des limites sur la capacité des liens montants et évaluons différentes stratégies de compression<br>With rapid development of Internet of Things (IoT), the need of indoor location-based services such as asset management, navigation and tracking has also grown overtime. For indoor localization, navigation satellite systems such as GPS has limited usage since a direct line-of-sight to satellites is unavailable.Various solutions have been proposed for indoor localization such as trilateration, triangulation, dead reckoning, but their performance is limited by indoor channel conditions, such as shadowing and multipath fading. By exploiting the mapping between wireless signal feature measurements and positions, fingerprinting based methods have shown the potential to provide good localization performance with sufficient data. However, indoor localization still faces challenges like scalability, cost and complexity, privacy, etc.The focus of this thesis is to improve efficiency of indoor localization using machine learning techniques. We divide the localization process into two phases: offline radio mapping phase and online localization phase. During the offline phase, we introduce dataset analysis as an intermediate step between dataset creation and localization. We propose two numerical dataset quality indicators which can provide feedback to improve the radio map. Moreover, feature extraction and dataset processing using machine learning tools are integrated to improve efficiency by reducing the data size and computation complexity while improving localization performance. We propose a k-means based radio mapping method which can reduce the number of fingerprints by over % without losing useful information in the radio map or degrading localization performance. By exploring the hierarchical nature of large datasets, we propose a hierarchical feature extraction method which can further reduce localization complexity without compromising localization performance.For the online localization phase, we explore both traditional machine learning and deep learning. We first introduce several traditional machine learning methods and compare the localization performance on public datasets. We aim to improve localization performance of traditional methods.To cope with privacy and complexity issue, we introduce federated learning framework for indoor localization problem. In this framework, the clients share only their local models to the central server instead of the fingerprinting data. We first compare the performance with federated and centralized learning. Then, we further study the impact on different client numbers and local data size. To reduce communication cost during the training process, we evaluate different measures including client selection, gradient accumulation and model compression. An efficient compression method is proposed to compress local models which can reduce the uplink communication cost by 91.5% without compromising localization performance. At last, we consider a limit on uplink capacity and evaluate different compression strategies
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Zegeye, Wondimu K., and Seifemichael B. Amsalu. "Minimum Euclidean Distance Algorithm for Indoor WiFi Received Signal Strength (RSS) Fingerprinting." International Foundation for Telemetering, 2016. http://hdl.handle.net/10150/624190.
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While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment
effort is a key challenge. In this paper, indoor localization with no pre-deployment effort in an indoor
space, such as an office building corridor, with WiFi coverage but no apriori knowledge of the placement
of the access points(APs) is implemented for mobile devices. WiFi Received Signal Strength(RSS) in the
considered environment is used to build radio maps using WiFi fingerprinting approach. Two architectures
are developed based on this localization algorithm. The first one involves a client-server approach
where the localization algorithm runs on the server whereas the second one is a standalone architecture
and the algorithm runs on the SD card of the mobile device.
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Li, Kejiong. "Indoor and outdoor location estimation in large areas using received signal strength." Thesis, Queen Mary, University of London, 2013. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8537.
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Location estimation when deployed on wireless networks supports a range of services including user tracking and monitoring, health care support and push and pull marketing. The main subject of this thesis is improving indoor and outdoor location estimation accuracy using received signal strength (RSS) from neighbouring base stations (BSs) or access points (APs), without using the global positioning system (GPS) or triangulation methods. For the outdoor environment, state-of-the-art deterministic and probabilistic algorithms are adapted to exploit principal components (PCs) and clustering. The accuracy is compared with K-nearest neighbour (KNN) algorithms using different partitioning models. The proposed scheme clusters the RSS tuples based on deviations from an estimated RSS attenuation model and then transforms the raw RSS in each cluster into new uncorrelated dimensions, using PCs. As well as simple global dimensionality reduction using PCs, the data reduction and rotation within each cluster improves estimation accuracy because a) each cluster can model the different local RSS distributions and b) it efficiently preserves the RSS correlations that are observed (some of which are substantial) in local regions and which independence approximations ignore. Different simulated and real environments are used for the comparisons. Experimental results show that positioning accuracy is significantly improved and fewer training samples are needed compared with traditional methods. Furthermore, a technique to adjust RSS data so that radio maps collected in different environmental conditions can be used together to enhance accuracy is also demonstrated. Additionally, in the radio coverage domain, a non-parametric probability approach is used for the radio reliability estimation and a semi-supervised learning model is proposed for the monitoring model training and evolution according to real-time mobile users’ RSS feedback. For the indoor environment, an approach for a large multi-story indoor location estimaiii tion using clustering and rank order matching is described. The accuracies using WiFi RSS alone, cellular GSM RSS alone and integrated WiFi and GSM RSS are presented. The methods were tested on real indoor environments. A hierarchical clustering method is used to partition the RSS space, where a cluster is defined as a set of mobile users who share exactly the same strongest RSS ranking set of transmitters. The experimental results show that while integrating of WiFi RSS with GSM RSS creates a marginal improvement, the GSM data can be used to ameliorate the loss of accuracy when APs fail.
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Galbraith, Andrew. "Multilateration in Direct ShortRange Communications Networks : Utilising Basic Safety Messages and Received Signal Strength Ranging." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-97682.
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Traditional satellite positioning systems have limited resolution and have proved inaccuratein areas such as urban canyons where signals are subject to bounce phenomena or indeed may be entirely unavailable. An alternative method of positioning is that of tri/multilateration, which uses known positions and distances from beacon points to locate a receiver. In this project, a software was developed which used DSRC Basic Safety Messages (containing locational information) in combination with Received Signal Strength metrics (translated to distance information) to carry out such positioning in static environments. Initial studies confirmed that a signal received on the Craton 2 hardware was subject to considerable signal strength spread approximating a Gaussian distribution. A software was developed to simulate BSMs, including a measure of perturbation, over TCP. Three different traffic scenarios were constructed. Furthermore, multilaterationsoftware was developed to receive the BSMs and calculate position using three separate algorithms. The performance of these algorithms in the three different traffic scenarios was then evaluated. Lastly, the multilateration software was further developed to allow for the capture and processing of real BSMs sent on the 5.9 GHzband. The multilateration software was capable of determining the location of the receiver to varying degrees of accuracy, depending on the geometrical distribution of surrounding vehicles and the algorithm used to multilaterate. The 3D Linear Least Squares method performed well in situations where beacons were well spaced in three dimensions. Other implemented multilateration algorithms, i.e., a 2D Linear Least Squares method and a 3D Gauss Newton method, performed better in typical traffic scenarios where vehicles tend to be coplanar.The software developed provides a useful starting point for further developmentof static, but also dynamic, multilateration algorithms.
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Tadokoro, Yukihiro, Hiraku Okada, Takaya Yamazato, and Masaaki Katayama. "The Optimum Received Signal-Power Distribution for CDMA Packet Communication Systems Employing Successive Interference Cancellation." IEEE, 2004. http://hdl.handle.net/2237/7763.
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Sundberg, Simon. "Localization of eNodeBs with a Large Set of Measurements from Train Routers." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-75456.
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This master thesis investigates the possibility of locating LTE base stations, known as eNodeBs, using signal measurements collected by routers on trains. Four existing algorithms for transmitter localization are adopted: the centroid, strongest signal, Monte Carlo path loss simulation and power difference of arrival (PDoA) methods. An improved version of Monte Carlo path loss simulation called logloss fitting is proposed. Furthermore, a novel localization method called sector fitting is presented, which operates solely on the cell identity and geographical distribution of the measurements. The methods are evaluated for a set of manually located eNodeBs, and the results are compared to other external systems that can be used to locate eNodeBs. It is found that the novel sector fitting algorithm is able to considerably improve the accuracy of the logloss fitting and PDoA methods, but weighted centroid is overall the most accurate of the considered methods, providing a median error of approximately 1 km. The Google Geolocation API and Mozilla Location Service still provides estimates that are generally closer to the true location than any of the considered methods. However, for a subset of eNodeBs where measurements from all sectors are available, the novel sector fitting algorithm combined with logloss fitting outperforms the external systems. Therefore, a hybrid approach is suggested, where sector fitting combined with logloss fitting or weighted centroid is used to locate eNodeBs that have measurements from all sectors, while Google Geolocation API or Mozilla Location Service is used to locate the remaining eNodeBs. It is concluded that while the localization performance for those eNodeBs that have measurements from all sectors is relatively good, further improvements to the overall results can likely be obtained in future work by considering environmental factors, the angular losses introduced by directional antennas, and the effects of downlink power control.
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Ahmed, Rana R. "Performance Modelling and Analysis of a New CoMP-based Handover Scheme for Next Generation Wireless Networks. Performance Modelling and Analysis for the Design and Development of a New Handover Scheme for Cell Edge Users in Next Generation Wireless Networks (NGWNs) Based on the Coordinated Multi-Point (CoMP) Joint Transmission (JT) Technique." Thesis, University of Bradford, 2017. http://hdl.handle.net/10454/16785.
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Inter-Cell Interference (ICI) will be one of main problems for degrading the performance of future wireless networks at cell edge. This adverse situation will become worst in the presence of dense deployment of micro and macro cells. In this context, the Coordinated Multi-Point (CoMP) technique was introduced to mitigate ICI in Next Generation Wireless Networks (NGWN) and increase their network performance at cell edge. Even though the CoMP technique provides satisfactory solutions of various problems at cell edge, nevertheless existing CoMP handover schemes do not prevent unnecessary handover initialisation decisions and never discuss the drawbacks of CoMP handover technique such as excessive feedback and resource sharing among UEs. In this research, new CoMP-based handover schemes are proposed in order to minimise unnecessary handover decisions at cell edge and determine solution of drawbacks of CoMP technique in conjunction with signal measurements such as Reference Signal Received Power (RSRP) and Received Signal Received Quality (RSRQ). A combination of calculations of RSRP and RSRQ facilitate a credible decision making process of CoMP mode and handover mode at cell edge. Typical numerical experiments indicate that by triggering the CoMP mode along with solutions of drawbacks, the overall network performance is constantly increase as the number of unnecessary handovers is progressively reduced.
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Zang, Yuzhang. "UWB Motion and Micro-Gesture Detection -Applications to interactive electronic gaming and remote sensing." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/1241.
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The ultra-wideband (UWB) technology has a vast unlicensed frequency spectrum, which can support precise indoor positioning in orders of centimeters. The features of UWB signals can be utilized for variety of applications. In this project first we present an empirical channel models to analyze the localization accuracy of the UWB technology for interactive electronic gaming (Ping-Pong) in Line-of-Sight (LOS) and Obstructed LOS (OLOS) scenarios. Then we introduce a new concept that we refer to as micro-gesture detection to handle the more refined motions of the hand, such as rotation, while one antenna is held by the user using features of UWB signal. We use four specific features of the UWB signals: time of arrival, power of the first peak, total power, and the Root-Mean Square (RMS) of the delay spread, for this purpose. As the hand rotates the position of the antenna in the hand and the external antenna changes from LOS to OLOS. We demonstrate that features of the UWB signals are more useful than the RSS signal of the Wi-Fi to detect this class of micro-gestures. We foresee this micro-gesture detection capabilities become helpful for the people with limited ability or visually impaired for implementation of simplified sign language to communication with electronic devices located away from a person. We compare gesture detection using multiple features of the UWB signal with traditional gesture detection using the received signal strength (RSS) of the Wi-Fi signal.
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Fekih, Hassen Wiem. "A ubiquitous navigation service on smartphones." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI006.
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La navigation pédestre est un domaine de recherche en pleine croissance qui vise à développer des services assurant le positionnement et la navigation en continu des personnes à l'extérieur comme à l'intérieur de bâtiments. Dans cette thèse, nous proposons un prototype de service pour la navigation pédestre ubiquitaire qui tient compte des préférences de l'utilisateur et de la technologie de positionnement optimale disponible. Notre objectif principal est d'estimer, d'une façon continue, la position d'un piéton muni d'un smartphone. En premier lieu, nous proposons un nouvel algorithme, nommé UCOSA, qui permet de sélectionner la technologie de positionnement à adopter à tout moment le long du processus de navigation. L'algorithme UCOSA commence par inférer la nécessité de déclencher un processus de "handover" (changement de technologie) entre les technologies de positionnement détectées (i.e. quand les zones de couvertures se chevauchent) en utilisant la technique de la logique floue. Ensuite, il sélectionne la technologie optimale à l'aide d'une fonction qui calcule un score pour chaque technologie disponible et qui se compose de deux parties. La première partie représente les poids, calculés en utilisant la méthode d'analyse hiérarchique (AHP). Tandis que, la deuxième partie fournit les valeurs normalisées des paramètres considérés. L'algorithme UCOSA intègre aussi la technique de positionnement à l'estime appelé PDR afin d'améliorer le calcul de la position du smartphone. En second lieu, nous portons l'intérêt à la technique de positionnement par empreintes RSS dont le principe consiste à calculer la position du smartphone en comparant les valeurs RSSs enregistrées, en temps réel, avec les valeurs RSSs stockées dans une base de données (radiomap). La majorité des radiomaps sont représentées sous forme de grilles composées de points de référence (PR). Nous proposons une nouvelle conception de radiomap qui ajoute d'autres PRs au centre de gravité de chaque carré de la grille. En troisième lieu, nous abordons le problème de la construction du graphe modélisant un bâtiment multi-étages. Nous proposons un algorithme qui crée tout d'abord un graphe plan pour chaque étage, séparément, et qui relie ensuite les différents étages par des liens verticaux. En dernier lieu, nous étudions un nouvel algorithme nommé SIONA qui calcule et qui affiche d'une manière continue le chemin entre deux points situés à l'intérieur ou à l'extérieur d'un bâtiment. Plusieurs expériences réelles ont été réalisées pour évaluer les performances des algorithmes proposés avec des résultats prometteurs en termes de continuité et de précision (de l'ordre de 1.8 m) du service de navigation<br>Pedestrian navigation is a growing research field, which aims at developing services and applications that ensure the continuous positioning and navigation of people inside and outside covered areas (e.g. buildings). In this thesis, we propose a ubiquitous pedestrian navigation service based on user preferences and the most suitable efficient available positioning technology (e.g. WiFi, GNSS). Our main objective is to estimate continuously the position of a pedestrian carrying a smartphone equipped with a variety of technologies and sensors. First, we propose a novel positioning technology selection algorithm, called UCOSA for the complete ubiquitous navigation service in indoor and outdoor environments. UCOSA algorithm starts by inferring the need of a handover between the available positioning technologies on the overlapped coverage areas using fuzzy logic technique. If a handover process is required, a score is calculated for each captured Radio Frequency (RF) positioning technology. The score function consists of two parts: the first part represents the user preferences weights computed based on the Analytic Hierarchy Process (AHP). Whereas, the second part provides the user requirements (normalized values). UCOSA algorithm also integrates the Pedestrian Dead Reckoning (PDR) positioning technique through the navigation process to enhance the estimation of the smartphone's position. Second, we focus on the RSS fingerprinting positioning technique as it is the most widely used technique, which principle is to return the smartphone's position by comparing the real time recorded RSS values with the radiomap (i.e. a database of previous stored RSS values). Most of radiomap are organized in a grid, formed or Reference Point (RP): we propose a new design of radiomap which complements the grid with other RPs located at the center of gravity of each grid square. Third, we address the challenge of constructing a graph for a multi-floor building. We propose an algorithm that starts by creating the horizontal graph of each floor, separately, and then, adds vertical links between the different floors. Finally, we implement a novel algorithm, called SIONA that calculates and displays in a continuous manner the pathway between two distinct points being located indoor or outdoor. We conduct several real experiments inside the campus of the University of Passau in Germany to evaluate the performance of the proposed algorithms. They yield promising results in terms of continuity and accuracy (around 1.8 m indoor) of navigation service
Capítulos de libros sobre el tema "Received signal power (RSS)"
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Arthi, R., Digvijay Singh Rawat, Abhiviraj Pillai, Yash Nair, and S. S. Kausik. "Analysis of Indoor Localization Algorithm for WiFi Using Received Signal Strength." In Advances in Power Systems and Energy Management. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7504-4_40.
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Lei, Qianqian, Erhu Zhao, Min Lin, and Yin Shi. "A Low Power Received Signal Strength Indicator for Short Distance Receiver." In Lecture Notes in Electrical Engineering. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01273-5_84.
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Han, Mingzhi, and Yongyi Mao. "An Indoor Floor Location Method Based on Minimum Received Signal Strength (RSS) Dynamic Compensation and Multi Label Classification." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20738-9_67.
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Sharma, Prachi, and Rajendra Kumar Dwivedi. "Detection of High Transmission Power Based Wormhole Attack Using Received Signal Strength Indicator (RSSI)." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2372-0_13.
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Elmokashfi, Ahmed, and Amund Kvalbein. "Chapter 5 Using Bluetooth for contact tracing." In Simula SpringerBriefs on Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05466-2_5.
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AbstractBluetooth data is used as the main method for contact tracing with Smittestopp. When two active devices are within Bluetooth range, they will record the ID of the paired device, along with information about the received signal quality. In this chapter, we describe how this method is implemented in Smittestopp, and how Bluetooth data is processed and analysed, to determine if an encounter between two users should be considered a qualified contact with a risk of contamination.We show that distance estimation based on Bluetooth signals is challenging due to differences between devices, lack of information on transmit power and varying environmental factors. Based on this experience, we propose a simple rule for identifying contacts based on received signal strength combined with information about the operating system type.
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Fuada, Syifaul, Mariella Särestöniemi, Marcos Katz, Simone Soderi, and Matti Hämäläinen. "Experimental Study of In-Body Devices Misalignment Impact on Light-Based In-Body Communications." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_30.
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AbstractOptical wireless communication (OWC) has emerged as a promising technology for implantable medical devices because it provides private and secure wireless links for patients, low-power consumption, and high-speed data transmission. The OWC system’s receiving end typically relies on a photodetector with a limited field-of-view, necessitating direct line-of-sight connections for effective transmission. The directional nature of light-tissue interaction on the in-body communication can be problematic as the quality of the optical signal is rapidly deteriorated due to the properties of biological tissues, including scattering, absorption, and reflection, leading to a substantial loss of optical beam power reaching the photodetector’s sensitive area. In this sense, any misalignment that occurs in the in-body device can directly impact the power level and further degrade the received signal quality. Numerous studies have been conducted on this topic in free-space environments; nevertheless, only a few results have been found for in-body cases. In this work, we experimentally demonstrate the impact of the in-body device misalignment on the OWC-based in-body communication system. Three cases were investigated: aligned systems, as well as lateral and angular misalignments. We considered an 810 nm Near-infrared (NIR) LED as a transmitter because the optical signal of the mentioned wavelength propagates better than other wavelengths through biological tissues. For the experiments, we used pure muscle and fat tissues with 15 mm thickness at different temperatures (23 ℃ and 37 ℃). We also tested with thicker meat samples (30 mm, 38 mm, and 40 mm, consisting of muscle + fat layers) at 37 ℃. This study adhered to ANSI.Z136.1–2007 safety standards. First, the results reveal that optical power still reaches the receiver in an aligned reference case at a meat thickness of 40 mm. Second, the in-body device misalignment significantly degrades the optical power density received, which is more pronounced under lateral than angular conditions. These misalignment effects must be carefully considered for further system enhancement when using OWC for the in-body communication system.
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Patwari, Neal, and Piyush Agrawal. "Calibration and Measurement of Signal Strength for Sensor Localization." In Localization Algorithms and Strategies for Wireless Sensor Networks. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-396-8.ch005.
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A number of practical issues are involved in the use of measured received signal strength (RSS) for purposes of localization. This chapter focuses on device effects and modeling problems which are not well covered in the literature, such as transceiver device manufacturing variations, battery effects on transmit power, nonlinearities in RSSI circuits, and path loss model parameter estimation. The authors discuss both the negative impacts of these effects and inaccuracies, and adaptations used by particular localization algorithms to be robust to them, without discussing any algorithm in detail. The authors present measurement methodologies to characterize these effects for wireless sensor nodes, and report the results from several calibration experiments to quantify each discussed effect and modeling issue.
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Fang, Shih-Hau. "Robustness in Fingerprinting-Based Indoor Positioning Systems." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3528-7.ch003.
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Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. Received signal strength (RSS), mostly utilized in Wi-Fi fingerprinting systems, is known to be unreliable due to two reasons: orientation mismatch and variations in hardware. This chapter introduces an approach based on histogram equalization to compensate for orientation mismatch in robust Wi-Fi localization. The proposed method involves converting the temporal-spatial radio signal strength into a reference function (i.e., equalizing the histogram). This chapter also introduces an enhanced positioning feature, which is called delta-fused principal strength, to enhance the robustness of Wi-Fi localization against the problem of heterogeneous hardware. This algorithm computes the pairwise delta RSS and then integrates with RSS using principal component analysis. The proposed methods effectively and efficiently improve the robustness of location estimation in the presence of mismatch orientation and hardware variations, respectively.
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Bri, Diana, Jaime Lloret, Carlos Turro, and Miguel Garcia. "Measuring Specific Absorption Rate by using Standard Communications Equipment." In Advances in Healthcare Information Systems and Administration. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0888-7.ch004.
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Specific Absorption Rate (SAR) is used to measure the body tissue exposure to electromagnetic fields. This chapter describes how SAR values can be estimated from a deployed Wireless Local Area Network (WLAN). We carried out this work using the Received Signal Strength (RSS) obtained from the access points. This parameter is easily obtained by an ordinary wireless network scanner. RSS variations are measured for a different number of people in the same room and without people. It will allow us to estimate how much energy is absorbed by a group of people and by a single person on average. Moreover, we have included the weight of the people in order to know the RSS lost by kilogram. These measurements were taken at the Higher Polytechnic School of Gandia, Universitat Politècnica de València, Spain, in two placements: the library and inside an anechoic camera.
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Shahra, Essa Qasem, Tarek Rahil Sheltami, and Elhadi M. Shakshuki. "A Comparative Study of Range-Free and Range-Based Localization Protocols for Wireless Sensor Network." In Sensor Technology. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2454-1.ch071.
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Wireless Sensor Network is deployed in many fields including military operations, mechanical applications, human services, smart homes, etc. However, deploying WSN encounters many challenges. One of the challenges is localizing the node position, especially mobile targets in critical situations. In this paper, the authors compare two types from range-free localization algorithms and one type from range-based algorithms, namely: Received Signal Strength (RSS), Centroid, and Distance Vector Hop (DV-Hops) protocols, using Cooja simulator. RSS localization algorithms require determining values of the RSS from the anchor nodes around the mobile node, to calculate the distance between the unknown mobile and the first three anchor nodes in the mobile range. The centroid localization requires only three anchors to compute the location of the mobile sensor without the need for distance measuring. Lastly, the DV-Hop algorithm uses routing tables of each anchor in the network topology to compute the Average Distance of Hops. The results show that rang-based algorithms are more accurate than range-free.
Actas de conferencias sobre el tema "Received signal power (RSS)"
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Kompostiotis, Dimitris, Dimitris Vordonis, and Vassilis Paliouras. "Received Power Maximization with Practical Phase-Dependent Amplitude Response in RIS-Aided OFDM Wireless Communications." In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10095408.
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Nilas, Phongchai, and Burin Baitoei. "Indoor Positioning System Based on Received Signal Strength: RSS." In 3rd Annual International Conference on Advanced Topics in Artificial Intelligence. Global Science Technology Forum, 2012. http://dx.doi.org/10.5176/2251-2179_atai12.19.
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Chandra, K. Ramesh, M. V. Pathi Amudalapalli, N. V. Satyanarayana, and Prudhvi Raj Budumuru. "Received Signal Strength (RSS) Based Channel Modelling, Localization and Tracking." In 2021 2nd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS). IEEE, 2021. http://dx.doi.org/10.1109/access51619.2021.9563331.
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Mailaender, Laurence. "Geolocation Bounds for Received Signal Strength (RSS) in Correlated Shadow Fading." In 2011 IEEE Vehicular Technology Conference (VTC Fall). IEEE, 2011. http://dx.doi.org/10.1109/vetecf.2011.6092847.
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Mailaender, Laurence. "On the CRLB scaling law for Received Signal Strength (RSS) geolocation." In 2011 45th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2011. http://dx.doi.org/10.1109/ciss.2011.5766210.
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Suwadi, Mike Yuliana, and Wirawan. "Polynomial Tope (PT) Key Group Generation Based Received Signal Strength (RSS)." In 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2021. http://dx.doi.org/10.1109/isriti54043.2021.9702835.
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Pajovic, Milutin, Philip Orlik, Toshiaki Koike-Akino, Kyeong Jin Kim, Hideto Aikawa, and Toshinori Hori. "An Unsupervised Indoor Localization Method Based on Received Signal Strength (RSS) Measurements." In GLOBECOM 2015 - 2015 IEEE Global Communications Conference. IEEE, 2014. http://dx.doi.org/10.1109/glocom.2014.7417708.
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Hashim, M. S. M., M. Azlan Shah Shahrol Aman, Loke Kah Wai, Teh Jia Yap, and M. Juhairi Aziz Safar. "Indoor localization approach based on received signal strength (RSS) and trilateration technique." In INTERNATIONAL CONFERENCE ON MATHEMATICS, ENGINEERING AND INDUSTRIAL APPLICATIONS 2016 (ICoMEIA2016): Proceedings of the 2nd International Conference on Mathematics, Engineering and Industrial Applications 2016. Author(s), 2016. http://dx.doi.org/10.1063/1.4965148.
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Pajovic, Milutin, Philip Orlik, Toshiaki Koike-Akino, Kyeong Jin Kim, Hideto Aikawa, and Toshinori Hori. "An Unsupervised Indoor Localization Method Based on Received Signal Strength (RSS) Measurements." In GLOBECOM 2015 - 2015 IEEE Global Communications Conference. IEEE, 2015. http://dx.doi.org/10.1109/glocom.2015.7417708.
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Wang, Sichun, Robert Inkol, and Brad R. Jackson. "Relationship between the maximum likelihood emitter location estimators based on received signal strength (RSS) and received signal strength difference (RSSD)." In 2012 26th Biennial Symposium on Communications (QBSC). IEEE, 2012. http://dx.doi.org/10.1109/qbsc.2012.6221353.
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