Relevant bibliographies by topics / Measurement RSSI

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

Academic literature on the topic 'Measurement RSSI'

Author: Grafiati
Published: 28 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Measurement RSSI"

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Chruszczyk, Łukasz. "Statistical Analysis of Indoor RSSI Read-outs for 433 MHz, 868 MHz, 2.4 GHz and 5 GHz ISM Bands." International Journal of Electronics and Telecommunications 63, no. 1 (March 1, 2017): 33–38. http://dx.doi.org/10.1515/eletel-2017-0005.

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Abstract This paper presents statistical analysis of RSSI read-outs recorded in indoor environment. Many papers concerning indoor location, based on RSSI measurement, assume its normal probability density function (PDF). This is partially excused by relation to PDF of radio-receiver's noise and/or together with influence of AWGN (average white Gaussian noise) radio-channel – generally modelled by normal PDF. Unfortunately, commercial (usually unknown) methods of RSSI calculations, typically as “side-effect” function of receiver's AGC (automatic gain control), results in PDF being far different from Gaussian PDF. This paper presents results of RSSI measurements in selected ISM bands: 433/868 MHz and 2.4/5 GHz. The measurements have been recorded using low-cost integrated RF modules (at 433/868 MHz and 2.4 GHz) and 802.11 WLAN access points (at 2.4/5 GHz). Then estimated PDF of collected data is shown and compared to normal (Gaussian) PDF.
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Zhu, Lin Na, and Hai Feng Jiang. "A Kind of Improved Triangle Centroid Algorithm Based on RSSI-Ranged." Advanced Materials Research 317-319 (August 2011): 1114–18. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1114.

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Node self-localization is one of the key technologies in the wireless sensor networks. The localization technology based on RSSI is a focus studied at the present stage. For the localization error of RSSI-ranged method is relatively great, a kind of triangle centroid algorihms based on RSSI-ranged is proposed. The simulation results show that measurement error of this algorithm effective decrease as to tranditional triangle centroid algorithm based on RSSI-ranged.
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Wang, Haijing, Fangfang Zhang, and Wenli Zhang. "Human Detection through RSSI Processing with Packet Dropout in Wireless Sensor Network." Journal of Sensors 2020 (September 21, 2020): 1–9. http://dx.doi.org/10.1155/2020/4758103.

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This paper presents a device-free human detection method for using Received Signal Strength Indicator (RSSI) measurement of Wireless Sensor Network (WSN) with packet dropout based on ZigBee. Packet loss is observed to be a familiar phenomenon with transmissions of WSNs. The packet reception rate (PRR) based on a large number of data packets cannot reflect the real-time link quality accurately. So this paper firstly raises a real-time RSSI link quality evaluation method based on the exponential smoothing method. Then, a device-free human detection method is proposed. Compared to conventional solutions which utilize a complex set of sensors for detection, the proposed approach achieves the same only by RSSI volatility. The intermittent Karman algorithm is used to filter RSSI fluctuation caused by environment and other factors in data packets loss situation, and online learning is adopted to set algorithm parameters considering environmental changes. The experimental measurements are conducted in laboratory. A high-quality network based on ZigBee is obtained, and then, RSSI can be calculated from the receive sensor modules. Experimental results show the uncertainty of RSSI change at the moment of human through the network area and confirm the validity of the detection method.
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Dolha, Stelian, Paul Negirla, Florin Alexa, and Ioan Silea. "Considerations about the Signal Level Measurement in Wireless Sensor Networks for Node Position Estimation." Sensors 19, no. 19 (September 26, 2019): 4179. http://dx.doi.org/10.3390/s19194179.

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Wireless Sensor Networks (WSN) are widely used in different monitoring systems. Given the distributed nature of WSN, a constantly increasing number of research studies are concentrated on some important aspects: maximizing network autonomy, node localization, and data access security. The node localization and distance estimation algorithms have, as their starting points, different information provided by the nodes. The level of signal strength is often such a starting point. A system for Received Signal Strength Indicator (RSSI) acquisition has been designed, implemented, and tested. In this paper, experiments in different operating environments have been conducted to show the variation of Received Signal Strength Indicator (RSSI) metric related to distance and geometrical orientation of the nodes and environment, both indoor and outdoor. Energy aware data transmission algorithms adjust the power consumed by the nodes according to the relative distance between the nodes. Experiments have been conducted to measure the current consumed by the node depending on the adjusted transmission power. In order to use the RSSI values as input for distance or location detection algorithms, the RSSI values can’t be used without intermediate processing steps to mitigate with the non-linearity of the measured values. The results of the measurements confirmed that the RSSI level varies with distance, geometrical orientation of the sensors, and environment characteristics.
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Santoso, Budy. "PENGARUH KEBERADAAN OBJEK MANUSIA TERHADAP STABILITAS RECEIVED SIGNAL STRENGTH INDICATOR (RSSI) PADA BLUETOOTH LOW ENERGY 4.0 (BLE)." Telematika 13, no. 1 (January 2, 2016): 11. http://dx.doi.org/10.31315/telematika.v13i1.1715.

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There are many systems with diverse technologies such as GPS, Wi-Fi, Bluetooth, Zigbee, Ultra Wide Band, Ultrasound, Infrared can be used for location-based services. Of these technologies can be developed several applications for positioning purposes such as monitoring patients in hospitals or elderly people who are undergoing treatment at home. This paper proposes a simple method to estimate the presence of the object / user in a fixed area using parameter Received Signal Strength Indicator (RSSI) on Bluetooth 4.0 Low Energy (BLE). To determine the performance of the RSSI, conducted two experiments in a room scenario dimensions 3 x 2.80 x 2.5 m (present and not present). Two experiments were conducted to test the performance of the RSSI signal. The first experiments with conditions not present showed a good performance. However, in the second experiment (present) with the status of various objects that are in the same room, resulting in poor performance of RSSI, where there is a shift in the RSSI value at the first measurement was obtained average RSSI -73 dBm with a range distance of 2 m, the second measurement obtained an average RSSI value of -85 dBm at a distance of 3 m range. With these results it can be concluded that the human presence in the area of research is very influential on the performance positioning signal strength (RSSI) and the significant impact that the shift distance of up to 1 m.
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ARYANTA, DWI. "Analisis Kinerja Subscriber Station WiMAX di Urban Area Bandung." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 1, no. 2 (July 1, 2013): 128. http://dx.doi.org/10.26760/elkomika.v1i2.128.

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ABSTRAKTeknologi komunikasi wireless semakin pesat mengalami perkembangan. WiMAX merupakan suatu teknologi broadband yang didukung oleh standar IEEE 802.16d (802.16-2004) yang mampu memberikan layanan data berkecepatan tinggi hingga 75 Mbps dalam radius maksimal 40-50 km pada bandwidth 20 MHz. Alokasi frekuensi yang dipakai Indonesia untuk jaringan WiMAX ini ialah 3,3 – 3,4 GHz. Penelitian ini dilakukan dengan melakukan proses pengukuran kinerja perangkat radio WiMAX yaitu HiMax 331-SS. Proses pengukuran dilakukan antara CPE dan base station dengan antenna sektoral 1200 pada ketinggian 45 m. Lokasi pengukuran dilakukan di beberapa area kota Bandung yang telah ditentukan sebelumnya. Hasil pengukuran memperlihatkan nilai CINR tertinggi adalah 31 dB dengan modulasi 64 QAM – ¾ dan terendah nilai 10 dB dengan modulasi BPSK 1/2. Nilai RSSI tertinggi -54 dBm berada dan nilai RSSI terendah -89 dBm. Nilai throughput tertinggi untuk layanan streaming video sebesar 1000,8 kbps (downlink) dengan modulasi 64 QAM – ¾. Nilai delay terendah sebesar 56,247 ms pada kondisi LOS dan tertinggi sebesar 139,5 ms pada kondisi NLOS. Nilai terbesar packet loss sebesar 20% yaitu pada lokasi pengukuran terjauh 14,3 km.Kata Kunci: delay, packet loss, RSSI, CINR, throughput, CPE, WiMAX . ABSTRACTWireless communication technologies have evolved more rapidly. WiMAX is a broadband technology that is supported by the IEEE standard 802.16-2004/d which is able to provide high speed data services of up to 75 Mbps within a radius of 40-50 km at a maximum bandwidth of 20 MHz. Indonesia frequency allocation used for the WiMAX network is 3.3 to 3.4 GHz. This study was conducted with the performance measurement process that is Himax 331 WiMAX radio - SS. Process measurement is made between the CPE and base station sector antennas at a height of 45 m in 1200. Locations measurements performed in several areas of Bandung predetermined. The measurement results show the highest CINR value is 31 dB with 64 QAM modulation - ¾ and the lowest value of 10 dB with BPSK modulation half. The highest RSSI value of -54 dBm being the lowest and -89 dBm RSSI value. The highest throughput for streaming video services by 1000.8 kbps ( downlink ) with 64 QAM modulation - ¾. The lowest value was 56.247 ms delay in LOS conditions and the highest was 139.5 ms in NLOS conditions. The greatest value by 20 % packet loss is the farthest measurement locations 14.3 km.Keywords: delay, packet loss, RSSI, CINR, throughput, CPE, WiMAX .
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Zheng, Jungang, Chengdong Wu, Hao Chu, and Yang Xu. "An Improved RSSI Measurement In Wireless Sensor Networks." Procedia Engineering 15 (2011): 876–80. http://dx.doi.org/10.1016/j.proeng.2011.08.162.

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Xu, Jiuqiang, Wei Liu, Fenggao Lang, Yuanyuan Zhang, and Chenglong Wang. "Distance Measurement Model Based on RSSI in WSN." Wireless Sensor Network 02, no. 08 (2010): 606–11. http://dx.doi.org/10.4236/wsn.2010.28072.

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Xue, Weixing, Weining Qiu, Xianghong Hua, and Kegen Yu. "Improved Wi-Fi RSSI Measurement for Indoor Localization." IEEE Sensors Journal 17, no. 7 (April 1, 2017): 2224–30. http://dx.doi.org/10.1109/jsen.2017.2660522.

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Ramirez, Ramiro, Chien-Yi Huang, Che-An Liao, Po-Ting Lin, Hsin-Wei Lin, and Shu-Hao Liang. "A Practice of BLE RSSI Measurement for Indoor Positioning." Sensors 21, no. 15 (July 30, 2021): 5181. http://dx.doi.org/10.3390/s21155181.

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Bluetooth Low Energy (BLE) is one of the RF-based technologies that has been utilizing Received Signal Strength Indicators (RSSI) in indoor position location systems (IPS) for decades. Its recent signal stability and propagation distance improvement inspired us to conduct this project. Beacons and scanners used two Bluetooth specifications, BLE 5.0 and 4.2, for experimentations. The measurement paradigm consisted of three segments, RSSI–distance conversion, multi-beacon in-plane, and diverse directional measurement. The analysis methods applied to process the data for precise positioning included the Signal propagation model, Trilateration, Modification coefficient, and Kalman filter. As the experiment results showed, the positioning accuracy could reach 10 cm when the beacons and scanners were at the same horizontal plane in a less-noisy environment. Nevertheless, the positioning accuracy dropped to a meter-scale accuracy when the measurements were executed in a three-dimensional configuration and complex environment. According to the analysis results, the BLE wireless signal strength is susceptible to interference in the manufacturing environment but still workable on certain occasions. In addition, the Bluetooth 5.0 specifications seem more promising in bringing brightness to RTLS applications in the future, due to its higher signal stability and better performance in lower interference environments.
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Dissertations / Theses on the topic "Measurement RSSI"

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Henty, Benjamin E. "Throughput Measurements and Empirical Prediction Models for IEEE 802.11b Wireless LAN (WLAN) Installations." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/34522.

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Typically a wireless LAN infrastructure is designed and installed by Networking professionals. These individuals are extremely familiar with wired networks, but are often unfamiliar with wireless networks. Thus, Wireless LAN installations are currently handicapped by the lack of an accurate, performance prediction model that is intuitive for use by non-wireless professionals. To provide a solution to this problem, this thesis presents a method of predicting the expected wireless LAN throughput using a site-specific model of an indoor environment. In order to develop this throughput prediction model, two wireless LAN throughput measurement products, LANFielder and SiteSpy, were created. These two products, which are patent pending, allow site-specific network performance measurements to be made. These two software packages were used to conduct an extensive measurement campaign to evaluate the performance of two IEEE 802.11b access points (APs) under ideal, multiuser, and interference scenarios. The data from this measurement campaign was then used to create empirically based throughput prediction models. The resulting models were first developed using RSSI measurements and then confirmed using predicted signal strength parameters.
Master of Science
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Yılmazer, Şafak Enes. "Integrated Coverage Measurement and Analysis System for Outdoor Coverage WLAN." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2185.

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Daily usage of Wireless Local Area Networks (WLAN) in business life for specific purposes has became much more critical than before since it is sometimes crucial to have wireless connectivity and seamless roaming around the working environment. In this thesis, steps required in order to design and implement a large scale outdoor IEEE 802.11g WLAN will be shown. This WLAN project has been deployed in north of Sweden and target coverage was an open area consisting of a deep pit mine, connecting roads, workshops, offices, dumps and storage areas. All telecommunications equipment used in this project is from the manufacturer Cisco using centralized solution. The special purpose of this project is to collect and analyze a series of coverage measurement data and correlate this data to predict the coverage area. Linux bash scripting and Gnuplot has been used to analyze coverage data. Finally, WRAP spectrum management and radio planning software has been used in modeling and designing of the whole network.
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Šimka, Marek. "Lokalizace uvnitř budov pomocí technologie LoRa." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442421.

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This diploma thesis focuses on possible utilization of LoRa (Long Range) technology for indoor localization purposes. In this thesis, the starter kit SK-iM282A is used to create a LoRa-based wireless link in the 2.4 GHz ISM band. Main attention is focused on the experimental localization using the RSSI method in the various transmission environments, including a description of the localization principle, the procedure of processing the measured data and the evaluation of localized coordinates. The rightness of the proposed measurement setup and methodology is verified by extensive measurements in various environments and compared with state-of-the-art article.
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Botta, Miroslav. "Optimalizace odhadu vzdálenosti v bezdrátové ad-hoc síti." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218968.

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The work deals with processing of radio received signal strength in IEEE 802.15.4 which communicates in 2.4 GHz ISM band. The signal is processed by the three approximation methods. They are tested for their effectiveness for measuring in different radio environments. Furthermore, the work deals with calculation of the most efficient coefficients for distance calculating by radio transmission fucntions. It defines the issues of such solutions on practical examples. The work also deals with the experimental algorithm for implementing dynamic calibration of the coefficients. It describes the design, processing and verification of this system in practice.
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Li, Zeyuan. "Target localization using RSS measurements in wireless sensor networks." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31356.

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The subject of this thesis is the development of localization algorithms for target localization in wireless sensor networks using received signal strength (RSS) measurements or Quantized RSS (QRSS) measurements. In chapter 3 of the thesis, target localization using RSS measurements is investigated. Many existing works on RSS localization assumes that the shadowing components are uncorrelated. However, here, shadowing is assumed to be spatially correlated. It can be shown that localization accuracy can be improved with the consideration of correlation between pairs of RSS measurements. By linearizing the corresponding Maximum Likelihood (ML) objective function, a weighted least squares (WLS) algorithm is formulated to obtain the target location. An iterative technique based on Newtons method is utilized to give a solution. Numerical simulations show that the proposed algorithms achieves better performance than existing algorithms with reasonable complexity. In chapter 4, target localization with an unknown path loss model parameter is investigated. Most published work estimates location and these parameters jointly using iterative methods with a good initialization of path loss exponent (PLE). To avoid finding an initialization, a global optimization algorithm, particle swarm optimization (PSO) is employed to optimize the ML objective function. By combining PSO with a consensus algorithm, the centralized estimation problem is extended to a distributed version so that can be implemented in distributed WSN. Although suboptimal, the distributed approach is very suitable for implementation in real sensor networks, as it is scalable, robust against changing of network topology and requires only local communication. Numerical simulations show that the accuracy of centralized PSO can attain the Cramer Rao Lower Bound (CRLB). Also, as expected, there is some degradation in performance of the distributed PSO with respect to the centralized PSO. In chapter 5, a distributed gradient algorithm for RSS based target localization using only quantized data is proposed. The ML of the Quantized RSS is derived and PSO is used to provide an initial estimate for the gradient algorithm. A practical quantization threshold designer is presented for RSS data. To derive a distributed algorithm using only the quantized signal, the local estimate at each node is also quantized. The RSS measurements and the local estimate at each sensor node are quantized in different ways. By using a quantization elimination scheme, a quantized distributed gradient method is proposed. In the distributed algorithm, the quantization noise in the local estimate is gradually eliminated with each iteration. Simulations show that the performance of the centralized algorithm can reach the CRLB. The proposed distributed algorithm using a small number of bits can achieve the performance of the distributed gradient algorithm using unquantized data.
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Stenström, Jonathan. "Simultaneous Trajectory Optimization and Target Estimation Using RSS Measurements to Land a UAV." Thesis, Linköpings universitet, Reglerteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-131117.

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The use of autonomous UAV’s is a progressively expanding industry. This thesisfocuses on the landing procedure with the main goal to be independent of visualaids. That means that the landing site can be hidden from the air, the landingcan be done in bad weather conditions and in the dark. In this thesis the use ofradio signals is investigated as an alternative to the visual sensor based systems.A localization system is needed to perform the landing without knowing wherethe landing site is. In this thesis an Extended Kalman Filter (EKF) is derived andused for the localization, based on the received signal strength from a radio beaconat the landing site. There are two main goals that are included in the landing,to land as accurate and as fast as possible. To combine these two goals a simultaneoustrajectory optimization and target estimation problem is set up that can bepartially solved while flying. The optimal solution to this problem produces thepath that the UAV will travel to get the best target localization while still reachingthe target. It is shown that trying to move directly towards the estimated landingsite is not the best strategy. Instead, the optimal trajectory is a spiral that jointlyoptimizes the information from the sensors and minimizes the arrival time.
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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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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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Lin, Ting-Wei, and 林廷瑋. "Using Bluetooth Technique in the RSSI Path Loss Measurement and Channel Model Construction in Indoor Wireless Transmission." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/54385920107056049621.

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碩士
淡江大學
電機工程學系碩士班
103
With the prosperous development of smart phones new smart phone applications constantly emerge in the daily life. Whenever there is a new application of smart phone introduced in the market it will always generate pro and con heated discussions. Following the development of BLE (Bluetooth Low Energy) Bluetooth related applications also become a hot topic at the present time and the study of applying Bluetooth in the indoor positioning has also appeared in many research fields. In this paper, we make field measurement of the path loss in the Bluetooth wireless transmission path and also in the measurement including obstacles and human blockings to investigate their effect on the wireless signal transmission so as to establish an accurate positioning methodology. 　　In this paper, we use MATLAB program to create a GUI interface. The user can choose the test scenario that he would like to review, and then the program will automatically plot the path loss curves for the selected test scenario and will mark the test data points; and consequently from the plotted curves the user can make the decision to determine the distance between the transmitter and receiver being a near-range, mid-range or a long-range and then this information will be used as reference when using Bluetooth as a positioning technique.
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LIN, CHING-KAI, and 林敬凱. "Optimization of GPS Positioning Accuracy with Wi-Fi RSSI Measurements and Machine Learning Algorithms." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/z53tsa.

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碩士
國立臺北科技大學
電子工程系
107
With the advancement of technology, the combination of mobile devices and geographic information has developed a Location-Based Service (LBS). The most basic function of LBS is positioning. The accuracy of positioning also affects the application level of LBS. The higher the positioning accuracy, the wider the application level. The most common outdoor positioning is the Global Positioning System (GPS), and the indoor positioning is to make a positioning by receiving different Wi-Fi signal sources and receiving strengths, because the streets in the bustling metropolitan area tend to be too much. The dynamic environment of high-rise buildings and crowds of people makes the satellite signal and Wi-Fi signal strength subject to multipath effects, resulting in positioning errors ranging from tens of meters to hundreds of meters, and even unable to locate. This thesis wants to use Wi-Fi signal strength positioning and GPS cooperative operation to improve the overall accuracy. Wi-Fi positioning uses the signal pattern identification method, combined with the latitude and longitude collected by GPS, to establish a Wi-Fi signal database online, using various applications. The positioning and comparison of the machine learning techniques ( K Nearest Neighbor Algorithm, K – Means Clustering Algorithm and K Nearest Neighbor Algorithm combined with K – Means Clustering Algorithm) of the positioning system are performed. The hardware equipment uses STMicroelectronics STM32F407ZET6 embedded development board, combined with UBlox NEO-6M GPS module and ESP8266 Wi-Fi module to study according to this design.
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Books on the topic "Measurement RSSI"

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Walthall, C. L. BOREAS RSS-3 atmospheric measurements from a helicopter-mounted sunphotometer. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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BOREAS RSS-3 atmospheric measurements from a helicopter-mounted sunphotometer. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Sara, Loechel, Halthore Rangasayi Narayan 1954-, and Goddard Space Flight Center, eds. BOREAS RSS-3 atmospheric measurements from a helicopter-mounted sunphotometer. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Sara, Loechel, Halthore Rangasayi Narayan 1954-, and Goddard Space Flight Center, eds. BOREAS RSS-3 atmospheric measurements from a helicopter-mounted sunphotometer. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Book chapters on the topic "Measurement RSSI"

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Wong, Wallace, Lin Shen Liew, Chean Hung Lai, and Llewellyn Liu. "Accurate Indoor Positioning Technique Using RSSI Assisted Inertial Measurement." In Lecture Notes in Electrical Engineering, 121–29. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6516-0_14.

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Vechet, Stanislav, and Jiri Krejsa. "Pedestrian Indoor Localization Using IoT Sensors RSSI Signal Strength Measurement." In Advances in Intelligent Systems and Computing, 164–71. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29993-4_21.

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Wolosz, Krzysztof, Ulf Bodin, and Laurynas Riliskis. "A Measurement Study for Predicting Throughput from LQI and RSSI." In Multiple Access Communications, 89–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34976-8_10.

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Adewumi, Omotayo, Karim Djouani, and Anish Kurien. "Performance Evaluation of RSSI Based Distance Measurement for Localization in Wireless Sensor Networks." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 74–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41178-6_8.

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Park, Jinhyung, Hyunhun Cho, Seunghae Kim, Dowoo Park, Ahsol Kim, and Joongoo Park. "An Adaptive Parameter Estimation Method for Wireless Localization Using RSSI Measurements." In Communications in Computer and Information Science, 238–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22309-9_29.

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Chen, Yin, and Andreas Terzis. "On the Mechanisms and Effects of Calibrating RSSI Measurements for 802.15.4 Radios." In Lecture Notes in Computer Science, 256–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11917-0_17.

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Li, Xianqing, and Zhansheng Duan. "Nonlinear Filtering for Emission Source Tracking Using Biased RSS Measurements." In Communications in Computer and Information Science, 548–55. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5230-9_53.

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Mo, Lufeng, Xiaoping Wu, and Guoying Wang. "Convex Optimization Algorithm for Wireless Localization by Using Hybrid RSS and AOA Measurements." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 39–51. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36442-7_3.

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Zhu, Xinxin, Yue Wang, Yuling Guo, Jiayu Chen, Na Li, and Bin Zhang. "Effect of Inaccurate Range Measurements on Hybrid TOA/RSS Linear Least Squares Localization." In Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems, 523–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49831-6_52.

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Jiang, Nanyong, Kaide Huang, Yao Guo, Guoli Wang, and Xuemei Guo. "A Diffraction Based Modified Exponential Model for Device-Free Localization with RSS Measurements." In Intelligent Robotics and Applications, 342–53. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13963-0_35.

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Conference papers on the topic "Measurement RSSI"

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Jianwu, Zhang, and Zhang Lu. "Research on distance measurement based on RSSI of ZigBee." In 2009 ISECS International Colloquium on Computing, Communication, Control, and Management (CCCM). IEEE, 2009. http://dx.doi.org/10.1109/cccm.2009.5267883.

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Xuhui, Zhang, Gao Baojiang, Liu Yukun, Wang Juan, and Chang Huimin. "The Optimization of RSSI-Neural Network Positioning Algorithm." In 2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC). IEEE, 2014. http://dx.doi.org/10.1109/imccc.2014.135.

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Capriglione, Domenico, Luigi Ferrigno, Eleonora D'Orazio, Vincenzo Paciello, and Antonio Pietrosanto. "Reliability analysis of RSSI for localization in small scale WSNs." In 2012 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2012. http://dx.doi.org/10.1109/i2mtc.2012.6229301.

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Barai, Suvankar, Debajyoti Biswas, and Buddhadeb Sau. "Estimate distance measurement using NodeMCU ESP8266 based on RSSI technique." In 2017 IEEE Conference on Antenna Measurements & Applications (CAMA). IEEE, 2017. http://dx.doi.org/10.1109/cama.2017.8273392.

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Sheng-Hua Yang, Yang-Han Lee, R. Y. Yen, Yu-Jie Zheng, Shiann-Tsong Sheu, Chih-Hui Ko, and Meng-Hong Chen. "A wireless LAN measurement method based on RSSI and FER." In Proceedings of APCC/OECC'99 - 5th Asia Pacific Conference on Communications/4th Optoelectronics and Communications Conference. IEEE, 1999. http://dx.doi.org/10.1109/apcc.1999.825029.

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Hartung, Salke, Henrik Brosenne, and Dieter Hogrefe. "Practical RSSI long distance measurement evaluation in Wireless Sensor Networks." In 2013 IEEE Conference on Wireless Sensor (ICWISE). IEEE, 2013. http://dx.doi.org/10.1109/icwise.2013.6728770.

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Xiong, Jianqiao, Qin Qin, and Kemin Zeng. "A Distance Measurement Wireless Localization Correction Algorithm Based on RSSI." In 2014 7th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2014. http://dx.doi.org/10.1109/iscid.2014.246.

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Halder, Sharly Joana, Tae-Young Choi, Jin-Hyung Park, Sung-Hun Kang, Sung-Jo Yun, and Joon-Goo Park. "On-Line Ranging for Mobile Objects Using ZIGBEE RSSI Measurement." In 2008 Third International Conference on Pervasive Computing and Applications (ICPCA). IEEE, 2008. http://dx.doi.org/10.1109/icpca.2008.4783693.

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Jun, Zhang Guo, Li Xin, Xu Zhen Long, and Li Han Chao. "Weighted Least Square Localization Algorithm Based on RSSI Values." In 2015 Fifth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC). IEEE, 2015. http://dx.doi.org/10.1109/imccc.2015.265.

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Benetazzo, L., M. Bertocco, A. Dalla Chiara, G. Gamba, A. Sona, and M. Ponzin. "Enhanced use of RSSI-based wireless network nodes for power measurement purposes." In 2009 IEEE Intrumentation and Measurement Technology Conference (I2MTC). IEEE, 2009. http://dx.doi.org/10.1109/imtc.2009.5168606.

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