Relevant bibliographies by topics / Predicted Received Signal Strength (RSS)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Predicted Received Signal Strength (RSS)'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Predicted Received Signal Strength (RSS)"

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Ayidu, Nneka Joy, and V. O. Elaigwu. "PATHLOSS PREDICTION MODEL IN WLAN PROPAGATION." FUDMA JOURNAL OF SCIENCES 7, no. 3 (2023): 1–5. http://dx.doi.org/10.33003/fjs-2023-0703-1822.

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Pathloss propagation in urban, suburban, and rural environments has a significant impact on wireless communication networks. Different propagation models have been developed for network locations. The different terrains are unique in their topological features and environmental factors. Therefore, a propagation model suitable for one terrain may not be suitable for another propagation environment for pathloss prediction. This paper proposes a signal prediction model with an 802.11 b/g wireless local area network (WLAN) infrastructure at 2.4 GHz. The models are backed by extensive received sign
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Subedi, Santosh, and Jae-Young Pyun. "Lightweight Workload Fingerprinting Localization Using Affinity Propagation Clustering and Gaussian Process Regression." Sensors 18, no. 12 (2018): 4267. http://dx.doi.org/10.3390/s18124267.

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Fingerprinting localization approach is widely used in indoor positioning applications owing to its high reliability. However, the learning procedure of radio signals in fingerprinting is time-consuming and labor-intensive. In this paper, an affinity propagation clustering (APC)-based fingerprinting localization system with Gaussian process regression (GPR) is presented for a practical positioning system with the reduced offline workload and low online computation cost. The proposed system collects sparse received signal strength (RSS) data from the deployed Bluetooth low energy beacons and tr
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Akinbolati, Akinsanmi, Yusuf Isiaq Omotosho, Idris Adamu, Muftahu Suleiman, and Sabiru Aminu Yaradua. "Pathloss Assessment of a Terrestrial Digital UHF Channel over Kano City, Nigeria." Nigerian Journal of Physics 33, S (2024): 15–22. http://dx.doi.org/10.62292/njp.v33(s).2024.228.

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Path loss assessment is an important factor in wireless communications to ensure quality of service. This study involves the measurement of Received Signal Strength (RSS) of Digital Terrestrial Television (DTTV) Star Times Station in Kano at 1 km intervals along three routes. Data were gathered during dry and wet season months in the year 2022. The path loss along the routes were computed based on the Okumura-Hata model. It was observed that path losses were higher during wet than dry season months. The result also indicates a higher negative correlation coefficient (R) of -0.745 between Line
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Akinbolati, Akinsanmi, Yusuf Isiaq Omotosho, Idris Adamu, Muftahu Suleiman, and Sabiru Aminu Yaradua. "Pathloss Assessment of a Terrestrial Digital UHF Channel over Kano City, Nigeria." Nigerian Journal of Physics 33, no. 2 (2024): 15–22. http://dx.doi.org/10.62292/njp.v33i2.2024.228.

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Path loss assessment is an important factor in wireless communications to ensure quality of service. This study involves the measurement of Received Signal Strength (RSS) of Digital Terrestrial Television (DTTV) Star Times Station in Kano at 1 km intervals along three routes. Data were gathered during dry and wet season months in the year 2022. The path loss along the routes were computed based on the Okumura-Hata model. It was observed that path losses were higher during wet than dry season months. The result also indicates a higher negative correlation coefficient (R) of -0.745 between Line
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Li, You, Zhouzheng Gao, Zhe He, et al. "Wireless Fingerprinting Uncertainty Prediction Based on Machine Learning." Sensors 19, no. 2 (2019): 324. http://dx.doi.org/10.3390/s19020324.

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Although wireless fingerprinting has been well researched and widely used for indoor localization, its performance is difficult to quantify. Therefore, when wireless fingerprinting solutions are used as location updates in multi-sensor integration, it is challenging to set their weight accurately. To alleviate this issue, this paper focuses on predicting wireless fingerprinting location uncertainty by given received signal strength (RSS) measurements through the use of machine learning (ML). Two ML methods are used, including an artificial neural network (ANN)-based approach and a Gaussian dis
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Sung, Kwangjae. "Pedestrian Positioning Using an Enhanced Ensemble Transform Kalman Filter." Sensors 23, no. 15 (2023): 6870. http://dx.doi.org/10.3390/s23156870.

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Due to the unavailability of GPS indoors, various indoor pedestrian positioning approaches have been designed to estimate the position of the user leveraging sensory data measured from inertial measurement units (IMUs) and wireless signal receivers, such as pedestrian dead reckoning (PDR) and received signal strength (RSS) fingerprinting. This study is similar to the previous study in that it estimates the user position by fusing noisy positional information obtained from the PDR and RSS fingerprinting using the Bayes filter in the indoor pedestrian positioning system. However, this study diff
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Mom, J. M., S. S. Tyokighir, and G. A. Igwue. "Evaluation of Some Path Reduction Factor Models Performance i n Tropical Location." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 3 (2021): 111–16. http://dx.doi.org/10.35940/ijrte.c6441.0910321.

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Performance evaluation of the ITU-R. P.530-17, Ghiani and Budalal model are considered for this work. It is found that the predicted values from the ITU-R and Ghiani distance factor models are seen to gradually decrease with an increase in path length for distances below 1km. Results further suggest that for a link length of 300 m, the Ghiani model predicts a 0.2499 dB (1.059 w) to 0.3273 dB (1.078 w) precipitation loss across all four (4) stations. For the ITU-R. P.530-17 model, a 3.4741 dB (2.225 w) to 5.329 dB (3.411 w) precipitation loss is estimated across all stations while the Budalal m
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Joseph, Mom, Tyokighir Soo, and Igwue Gabriel. "Evaluation of Some Path Reduction Factor Models Performance in Tropical Location." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 3 (2021): 111–16. https://doi.org/10.35940/ijrte.C6441.0910321.

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Performance evaluation of the ITU-R. P.530-17, Ghiani and Budalal model are considered for this work. It is found that the predicted values from the ITU-R and Ghiani distance factor models are seen to gradually decrease with an increase in path length for distances below 1km. Results further suggest that for a link length of 300 m, the Ghiani model predicts a 0.2499 dB (1.059 w) to 0.3273 dB (1.078 w) precipitation loss across all four (4) stations. For the ITU-R. P.530-17 model, a 3.4741 dB (2.225 w) to 5.329 dB (3.411 w) precipitation loss is estimated across all stations while the Budalal m
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Hua, Qiaozhi, Keping Yu, Zheng Wen, and Takuro Sato. "A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications." Applied Sciences 9, no. 3 (2019): 556. http://dx.doi.org/10.3390/app9030556.

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Cellular vehicle-to-everything (C-V2X) communication facilitates the improved safety, comfort, and efficiency of vehicles and mobility by exchanging information between vehicles and other entities. In general, only the macrocell or only the femtocell is the communication infrastructure for C-V2X. Currently, a macro-femtocell network is used as the new C-V2X networking architecture. However, there are two unresolved problems for C-V2X in macro-femtocell networks. Firstly, vehicle mobility requires the frequent switching of connections between different base stations; invalid switching results i
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Mohammed, Alaelddin F. Y., Salman Md Sultan, Joohyung Lee, and Sunhwan Lim. "Deep-Reinforcement-Learning-Based IoT Sensor Data Cleaning Framework for Enhanced Data Analytics." Sensors 23, no. 4 (2023): 1791. http://dx.doi.org/10.3390/s23041791.

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The Internet of things (IoT) combines different sources of collected data which are processed and analyzed to support smart city applications. Machine learning and deep learning algorithms play a vital role in edge intelligence by minimizing the amount of irrelevant data collected from multiple sources to facilitate these smart city applications. However, the data collected by IoT sensors can often be noisy, redundant, and even empty, which can negatively impact the performance of these algorithms. To address this issue, it is essential to develop effective methods for detecting and eliminatin
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Dissertations / Theses on the topic "Predicted Received Signal Strength (RSS)"

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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 involve
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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
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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 st
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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. Th
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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
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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 U
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Shuaieb, Wafa S. A. "Radio-Location Techniques for Localization and Monitoring Applications. A study of localisation techniques, using OFDM system under adverse channel conditions and radio frequency identification for object identification and movement tracking." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/18185.

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A wide range of services and applications become possible when accurate position information for a radio terminal is available. These include: location-based services; navigation; safety and security applications. The commercial, industrial and military value of radio-location is such that considerable research effort has been directed towards developing related technologies, using satellite, cellular or local area network infrastructures or stand-alone equipment. This work studies and investigates two location techniques. The first one presents an implementation scheme for a wide
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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 (A
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Obeidat, Huthaifa A. N., Raed A. Abd-Alhameed, James M. Noras, et al. "Indoor localization using received signal strength." 2013. http://hdl.handle.net/10454/9702.

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No<br>A comparison between two indoor localization algorithms using received signal strength is carried out. The first algorithm is the vector algorithm; the second is the matrix algorithm. The comparison considered the effects of the reference points, the access point, and the frequency on the accuracy of the localization process. The experiments were carried out using ray tracing software and MATLAB. This paper justifies the use of adopting the matrix algorithm.
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Gutierrez, Felix. "An experimental comparison of wireless position locating algorithms based on received signal strength." 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2395.

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This thesis presents and discusses research associated with locating wireless devices. Several algorithms have been developed to determine the physical location of the wireless device and a subset of these algorithms only rely on received signal strength (RSS). Two of the most promising RSS-based algorithms are the LC and dwMDS algorithms; however each algorithm has only been tested via computer simulations with different environmental parameters. To determine which algorithm performs better (i.e., produces estimates that are closer to the true location of the wireless device), a fair comparis
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Book chapters on the topic "Predicted Received Signal Strength (RSS)"

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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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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 i
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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
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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. Moreov
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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
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Chen, Liang, Heidi Kuusniemi, Yuwei Chen, et al. "Information Filter-Assisted Indoor Bluetooth Positioning." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6034-2.ch007.

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This chapter studies wireless positioning using a network of Bluetooth signals. Fingerprints of Received Signal Strength Indicators (RSSI) are used for localization. Due to the relatively long interval between the available consecutive Bluetooth signal strength measurements, the authors applied an information filter method with speed detection, which combines the estimation information from the RSSI measurements with the prior information from the motion model. Speed detection is assisted to correct the outliers of position estimation. The field tests show the effectiveness of the information
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Xu, Junjun, Haiyong Luo, Fang Zhao, Rui Tao, Yiming Lin, and Hui Li. "The WiMap." In Global Applications of Pervasive and Ubiquitous Computing. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2645-4.ch004.

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As positioning technology is an important foundation of the Internet of Things, a dynamic indoor WLAN localization system is proposed in this paper. This paper mainly concentrates on the design and implementation of the WiMap-a dynamic indoor WLAN localization system, which employs grid-based localization method using RSS (received signal strength). To achieve high localization accuracy and low computational complexity, Gaussian mixture model is applied to approximate the signal distribution and a ROI (region of interest) is defined to limit the search region. The authors also discuss other te
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Tu-Ly Nga, Le Tien Thuong, and Mai Linh. "Power Adaptive-Resampling based on Particle Filters for Wireless Patient Tracking in Health Care System." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-637-8-148.

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In this paper, we consider a typical health care system via the help of Wireless Sensor Network (WSN) for wireless patient tracking. The wireless patient tracking module of this system performs localization out of samples of Received Signal Strength (RSS) variations and tracking through a Particle Filter (PF) for WSN assisted by multiple transmit-power information. However, during the course of transmission power control, localization based on the RSS is a challenging problem because of the inconsistent RSS indication (RSSI) measurements in WSN. Therefore, we propose an adaptive-resampling, i.
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Gustafsson, Fredrik, and Fredrik Gunnarsson. "Measurements Used in Wireless Sensor Networks Localization." In Localization Algorithms and Strategies for Wireless Sensor Networks. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-396-8.ch002.

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Wireless sensor networks (WSN) localization relies on measurements. Availability of, and the information content in, these measurements depend on the network architecture, connectivity, node time synchronization and the signaling bandwidth between the sensor nodes. This chapter addresses wireless sensor networks measurements in a general framework based on a set of nodes, where each node either emits or receives signals. The emitted signal can for example be a radio, acoustic, seismic, infrared or sonic wave that is propagated in a certain media to the receiver. This general observation model
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Miah M. Suruz and Gueaieb Wail. "On the Implementation of an Efficient Mobile Robot Navigation System: An RFID Approach." In Intelligent Autonomous Systems 11. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-613-3-194.

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In this manuscript, we pioneer an efficient indoor mobile robot navigation system using signal strength of a customized RFID system. The RFID reader is mounted on the robot and a set of RFID tags are attached to 3-D points which are known as targets to be reached by the mobile robot in an indoor workspace. First, the direction of a current target is estimated through received signal strength (RSS) measurements of the customized RFID reader. The robot's current orientation is updated to head approximately towards the target. It then applies necessary actions to its actuator to reach the current
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Conference papers on the topic "Predicted Received Signal Strength (RSS)"

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Yao, Albert W. L., and H. T. Liao. "Development of an Intelligent Grey-RSS Navigation System for Mobile Robots." In ASME/ISCIE 2012 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/isfa2012-7175.

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This study aims at developing an intelligent navigation system for mobile robots to eliminate the cumulative errors caused by using a conventional optical encoder. The optical encoder for mobile robots is supplanted by an intelligent Grey-RSS navigation system (IGRNS) with a RFID system embedded with a RSS (Received Signal Strength) location estimator and a Grey predictor for robot rotation angle prediction. The RFID system identifies the target with active RFID tag. The RSS location estimator then calculates the possible route of robot. The Grey controller optimizes the traveling route quickl
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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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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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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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You might also be interested in the extended bibliographies on the topic 'Predicted Received Signal Strength (RSS)' for particular source types:
Journal articles Dissertations / Theses
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