Journal articles: '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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Ohara, Kenichi, Yuji Abe, Tomohito Takubo, Yasushi Mae, Tamio Tanikawa, and Tatsuo Arai. "Range Estimation Technique Using Received Signal Strength Indication on Low Frequency Waves." Journal of Robotics and Mechatronics 23, no. 4 (August 20, 2011): 466–74. http://dx.doi.org/10.20965/jrm.2011.p0466.

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Recently, with the downsizing of computers and the development of wireless communication advances, sensor networks are being widely studied. However, it is necessary to know the location of each node, in order to apply sensor data. Many researchers have tried to find a good approach to position estimation in indoor environment. In our study, we focus on position estimation by using Received Signal Strength Indication (RSSI). It has the advantage of implementation with limited resources in the sensor network. However, since RSSI value is affected by multipath and obstacles, position estimation may yield considerable errors. In our research, we propose a range estimation technique with RSSI on Low Frequency (LF) waves. Since RSSI value on LF waves is less affected by multipath and obstacles compared with RSSI on Ultra High Frequency (UHF) waves used for a communication, position estimation with high accuracy can be calculated using this method. We show an RSSI measurement sensor which measures the RSSI on LF waves and a transmitter which sends radio waves on the 125 kHz band. Results of experiments using our developed modules and a ZigBee module demonstrated the robustness of RSSI on LF waves against multipath and obstacles compared with UHF waves. In this paper, a range estimation experiment was performed by applying the proposed modules and range estimation accuracy was evaluated through experiments.

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Yang, Wei, Chundi Xiu, Jiarui Ye, Zhixing Lin, Haisong Wei, Dayu Yan, and Dongkai Yang. "LSS-RM: Using Multi-Mounted Devices to Construct a Lightweight Site-Survey Radio Map for WiFi Positioning." Micromachines 9, no. 9 (September 12, 2018): 458. http://dx.doi.org/10.3390/mi9090458.

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A WiFi-received signal strength index (RSSI) fingerprinting-based indoor positioning system (WiFi-RSSI IPS) is widely studied due to advantages of low cost and high accuracy, especially in a complex indoor environment where performance of the ranging method is limited. The key drawback that limits the large-scale deployment of WiFi-RSSI IPS is time-consuming offline site surveys. To solve this problem, we developed a method using multi-mounted devices to construct a lightweight site-survey radio map (LSS-RM) for WiFi positioning. A smartphone was mounted on the foot (Phone-F) and another on the waist (Phone-W) to scan WiFi-RSSI and simultaneously sample microelectromechanical system inertial measurement-unit (MEMS-IMU) readings, including triaxial accelerometer, gyroscope, and magnetometer measurements. The offline site-survey phase in LSS-RM is a client–server model of a data collection and preprocessing process, and a post calibration process. Reference-point (RP) coordinates were estimated using the pedestrian dead-reckoning algorithm. The heading was calculated with a corner detected by Phone-W and the preassigned site-survey trajectory. Step number and stride length were estimated using Phone-F based on the stance-phase detection algorithm. Finally, the WiFi-RSSI radio map was constructed with the RP coordinates and timestamps of each stance phase. Experimental results show that our LSS-RM method can reduce the time consumption of constructing a WiFi-RSSI radio map from 54 min to 7.6 min compared with the manual site-survey method. The average positioning error was below 2.5 m with three rounds along the preassigned site-survey trajectory. LSS-RM aims to reduce offline site-survey time consumption, which would cut down on manpower. It can be used in the large-scale implementation of WiFi-RSSI IPS, such as shopping malls, hospitals, and parking lots.

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Le, Anh Tuyen, Le Chung Tran, Xiaojing Huang, Christian Ritz, Eryk Dutkiewicz, Son Lam Phung, Abdesselam Bouzerdoum, and Daniel Franklin. "Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks." Sensors 20, no. 14 (July 9, 2020): 3838. http://dx.doi.org/10.3390/s20143838.

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Source positioning using hybrid angle-of-arrival (AOA) estimation and received signal strength indicator (RSSI) is attractive because no synchronization is required among unknown nodes and anchors. Conventionally, hybrid AOA/RSSI localization combines the same number of these measurements to estimate the agents’ locations. However, since AOA estimation requires anchors to be equipped with large antenna arrays and complicated signal processing, this conventional combination makes the wireless sensor network (WSN) complicated. This paper proposes an unbalanced integration of the two measurements, called 1AOA/nRSSI, to simplify the WSN. Instead of using many anchors with large antenna arrays, the proposed method only requires one master anchor to provide one AOA estimation, while other anchors are simple single-antenna transceivers. By simply transforming the 1AOA/1RSSI information into two corresponding virtual anchors, the problem of integrating one AOA and N RSSI measurements is solved using the least square and subspace methods. The solutions are then evaluated to characterize the impact of angular and distance measurement errors. Simulation results show that the proposed network achieves the same level of precision as in a fully hybrid nAOA/nRSSI network with a slightly higher number of simple anchors.

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Vadivukkarasi, K., and R. Kumar. "Investigations on real time RSSI based outdoor target tracking using kalman filter in wireless sensor networks." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (April 1, 2020): 1943. http://dx.doi.org/10.11591/ijece.v10i2.pp1943-1951.

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Target tracking is essential for localization and many other applications in Wireless Sensor Networks (WSNs). Kalman filter is used to reduce measurement noise in target tracking. In this research TelosB motes are used to measure Received Signal Strength Indication (RSSI). RSSI measurement doesn’t require any external hardware compare to other distance estimation methods such as Time of Arrival (TOA), Time Difference of Arrival (TDoA) and Angle of Arrival (AoA). Distances between beacon and non-anchor nodes are estimated using the measured RSSI values. Position of the non-anchor node is estimated after finding the distance between beacon and non-anchor nodes. A new algorithm is proposed with Kalman filter for location estimation and target tracking in order to improve localization accuracy called as MoteTrack InOut system. This system is implemented in real time for indoor and outdoor tracking. Localization error reduction obtained in an outdoor environment is 75%.

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Cao, Meng Long, and Chong Xin Yang. "A Localization Algorithm for Wireless Sensor Network Based on the Weighted Correction in Geometric Measurement." Applied Mechanics and Materials 740 (March 2015): 823–29. http://dx.doi.org/10.4028/www.scientific.net/amm.740.823.

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Firstly, the characteristics of regular Zigbee localization algorithms-the received signal strength indicator algorithm (RSSI) and the weighted centroid localization algorithm are introduced. Then, the factors of the errors existing in the aforementioned algorithms are analyzed. Based on these above, the improved RSSI algorithm-correction geometric measurement based on weighted is proposed. Finally, utilizing this algorithm to design and implement the localization nodes, which have the CC2431 wireless microcontroller on them. The simulation and experimental results show that the accuracy of this localization algorithm improved about 2%, comparing with the regular algorithms.

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Abd-ElKhaliq, S., M. Faied, R. Aboul Seoud, and A. Gody. "Distance and Error Measurement for Wireless Sensor Network System Localization Using RSSI Measurement." International Conference on Electrical Engineering 11, no. 11 (April 1, 2018): 1–14. http://dx.doi.org/10.21608/iceeng.2018.30116.

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Mamun, Md Abdulla Al, David Vera Anaya, Fan Wu, and Mehmet Rasit Yuce. "Landmark-Assisted Compensation of User’s Body Shadowing on RSSI for Improved Indoor Localisation with Chest-Mounted Wearable Device." Sensors 21, no. 16 (August 10, 2021): 5405. http://dx.doi.org/10.3390/s21165405.

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Nowadays, location awareness becomes the key to numerous Internet of Things (IoT) applications. Among the various methods for indoor localisation, received signal strength indicator (RSSI)-based fingerprinting attracts massive attention. However, the RSSI fingerprinting method is susceptible to lower accuracies because of the disturbance triggered by various factors from the indoors that influence the link quality of radio signals. Localisation using body-mounted wearable devices introduces an additional source of error when calculating the RSSI, leading to the deterioration of localisation performance. The broad aim of this study is to mitigate the user’s body shadowing effect on RSSI to improve localisation accuracy. Firstly, this study examines the effect of the user’s body on RSSI. Then, an angle estimation method is proposed by leveraging the concept of landmark. For precise identification of landmarks, an inertial measurement unit (IMU)-aided decision tree-based motion mode classifier is implemented. After that, a compensation model is proposed to correct the RSSI. Finally, the unknown location is estimated using the nearest neighbour method. Results demonstrated that the proposed system can significantly improve the localisation accuracy, where a median localisation accuracy of 1.46 m is achieved after compensating the body effect, which is 2.68 m before the compensation using the classical K-nearest neighbour method. Moreover, the proposed system noticeably outperformed others when comparing its performance with two other related works. The median accuracy is further improved to 0.74 m by applying a proposed weighted K-nearest neighbour algorithm.

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Wang, Xiao Ying, and Ying Ge Chen. "Mine Location Algorithm Based on Multiple Linear Regression." Applied Mechanics and Materials 58-60 (June 2011): 1830–35. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1830.

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This paper put forward a mine location algorithm based on multiple linear regression, which using only simple RSSI value to get a higher location accuracy under long narrow and sensitive mine environment. General RSSI measurement method and its drawbacks are discussed in the paper. In order to acquire smaller location error, we filtered some abnormal RSSI data through Gaussian filter method. And we deduced regression equation according to multiple linear regression principle. Combined with training sample, we got their regression parameter. We did relevant location experiment again in the same environment---40m long and narrow bomb shelter which may imitate mine tunnel to a great extent, which shows that the total errors are limited in 3m and 75% errors are less than 2m. What’s more, it can be extended to infinite measuring range with the same set regression coefficient in similar environment.

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Bullmann, Markus, Toni Fetzer, Frank Ebner, Markus Ebner, Frank Deinzer, and Marcin Grzegorzek. "Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios." Sensors 20, no. 16 (August 12, 2020): 4515. http://dx.doi.org/10.3390/s20164515.

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With the addition of the Fine Timing Measurement (FTM) protocol in IEEE 802.11-2016, a promising sensor for smartphone-based indoor positioning systems was introduced. FTM enables a Wi-Fi device to estimate the distance to a second device based on the propagation time of the signal. Recently, FTM has gotten more attention from the scientific community as more compatible devices become available. Due to the claimed robustness and accuracy, FTM is a promising addition to the often used Received Signal Strength Indication (RSSI). In this work, we evaluate FTM on the 2.4 GHz band with 20 MHz channel bandwidth in the context of realistic indoor positioning scenarios. For this purpose, we deploy a least-squares estimation method, a probabilistic positioning approach and a simplistic particle filter implementation. Each method is evaluated using FTM and RSSI separately to show the difference of the techniques. Our results show that, although FTM achieves smaller positioning errors compared to RSSI, its error behavior is similar to RSSI. Furthermore, we demonstrate that an empirically optimized correction value for FTM is required to account for the environment. This correction value can reduce the positioning error significantly.

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Joana Halder, Sharly, and Wooju Kim. "A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers." Journal of Computer Networks and Communications 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/790374.

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Due to the ease of development and inexpensiveness, indoor localization systems are getting a significant attention but, with recent advancement in context and location aware technologies, the solutions for indoor tracking and localization had become more critical. Ranging methods play a basic role in the localization system, in which received signal strength indicator- (RSSI-) based ranging technique gets the most attraction. To predict the position of an unknown node, RSSI measurement is an easy and reliable method for distance estimation. In indoor environments, the accuracy of the RSSI-based localization method is affected by strong variation, specially often containing substantial amounts of metal and other such reflective materials that affect the propagation of radio-frequency signals in nontrivial ways, causing multipath effects, dead spots, noise, and interference. This paper proposes an adaptive smoother based location and tracking algorithm for indoor positioning by making fusion of RSSI and link quality indicator (LQI), which is particularly well suited to support context aware computing. The experimental results showed that the proposed mathematical method can reduce the average error around 25%, and it is always better than the other existing interference avoidance algorithms.

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Cho, Seong Yun. "Measurement Error Observer-Based IMM Filtering for Mobile Node Localization Using WLAN RSSI Measurement." IEEE Sensors Journal 16, no. 8 (April 2016): 2489–99. http://dx.doi.org/10.1109/jsen.2015.2512590.

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Hossain, Ferdous, Tan Kim Geok, Tharek Abd Rahman, Mohammad Nour Hindia, Kaharudin Dimyati, Sharif Ahmed, C. P. Tso, et al. "Indoor 3-D RT Radio Wave Propagation Prediction Method: PL and RSSI Modeling Validation by Measurement at 4.5 GHz." Electronics 8, no. 7 (July 3, 2019): 750. http://dx.doi.org/10.3390/electronics8070750.

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This article introduces an efficient analysis of indoor 4.5 GHz radio wave propagation by using a proposed three-dimensional (3-D) ray-tracing (RT) modeling and measurement. The attractive facilities of this frequency band have significantly increased in indoor radio wave communication systems. Radio propagation predictions by simulation method based on a site-specific model, such as RT is widely used to categorize radio wave channels. Although practical measurement provides accurate results, it still needs a considerable amount of resources. Hence, a computerized simulation tool would be a good solution to categorize the wireless channels. The simulation has been performed with an in-house developed software tool. Here, the 3-D shooting bouncing ray tracing (SBRT) and the proposed 3-D ray tracing simulation have been performed separately on a specific layout where the measurement is done. Several comparisons have been performed on the results of the measurement: the proposed method, and the existing SBRT method simulation with respect to received signal strength indication (RSSI) and path loss (PL). The comparative results demonstrate that the RSSI and the PL of proposed RT have better agreements with measurement than with those from the conventional SBRT outputs.

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Kang, Jinsu, Jeonghoon Seo, and Yoojae Won. "Ephemeral ID Beacon-Based Improved Indoor Positioning System." Symmetry 10, no. 11 (November 10, 2018): 622. http://dx.doi.org/10.3390/sym10110622.

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Recently, the rapid development of mobile devices and communication technologies has dramatically increased the demand for location-based services that provide users with location-oriented information and services. User location in outdoor spaces is measured with high accuracy using GPS. However, because the indoor reception of GPS signals is not smooth, this solution is not viable in indoor spaces. Many on-going studies are exploring new approaches for indoor location measurement. One popular technique involves using the received signal strength indicator (RSSI) values from the Bluetooth Low Energy (BLE) beacons to measure the distance between a mobile device and the beacons and then determining the position of the user in an indoor space by applying a positioning algorithm such as the trilateration method. However, it remains difficult to obtain accurate data because RSSI values are unstable owing to the influence of elements in the surrounding environment such as weather, humidity, physical barriers, and interference from other signals. In this paper, we propose an indoor location tracking system that improves performance by correcting unstable RSSI signals received from BLE beacons. We apply a filter algorithm based on the average filter and the Kalman filter to reduce the error range of results calculated using the RSSI values.

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Gou, Pingzhang, Bo He, and Zhaoyang Yu. "A Node Location Algorithm Based on Improved Whale Optimization in Wireless Sensor Networks." Wireless Communications and Mobile Computing 2021 (September 16, 2021): 1–17. http://dx.doi.org/10.1155/2021/7523938.

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With the popularity of swarm intelligence algorithms, the positioning of nodes to be located in wireless sensor networks (WSNs) has received more and more attention. To overcome the disadvantage of large ranging error and low positioning accuracy caused by the positioning algorithm of the received signal strength indication (RSSI) ranging model, we use the RSSI modified by Gaussian to reduce the distance measurement error and introduce an improved whale optimization algorithm to optimize the location of the nodes to be positioned to improve the positioning accuracy. The experimental results show that the improved whale algorithm performs better than the whale optimization algorithm and other swarm intelligence algorithms under 20 different types of benchmark function tests. The positioning accuracy of the proposed location algorithm is better than that of the original RSSI algorithm, the hybrid exponential and polynomial particle swarm optimization (HPSO) positioning algorithms, the whale optimization, and the quasiaffine transformation evolutionary (WOA-QT) positioning algorithm. It can be concluded that the cluster intelligence algorithm has better advantages than the original RSSI in WSN node positioning, and the improved algorithm in this paper has more advantages than several other cluster intelligence algorithms, which can effectively solve the positioning requirements in practical applications.

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Ma, Yu Feng, Shan Liu, and Jian Ping Chai. "Research on 3D Positioning Indoor Simulation Based on RSSI." Applied Mechanics and Materials 696 (November 2014): 247–52. http://dx.doi.org/10.4028/www.scientific.net/amm.696.247.

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The growing demand for location-based services promote the development of positioning technology, but the traditional two-dimensional positioning has not fully meet the actual demand, three-dimensional positioning and has more practical significance and application prospects. RSSI-based ranging technology is a low-cost distance measurement technology and low complexity, has been widely used in wireless networks based on the distance of positioning technology. In this paper, through detailed analysis and Research on the effect of RSSI, simulation implemented based indoor positioning. Experimental results show that after eliminating the effects of environmental factors, after mean filtering and data processing Gaussian model range accuracy has greatly improved, positioning error is smaller.

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Abe, Yuji, Kenichi Ohara, Tomohito Takubo, Yasushi Mae, Tamio Tanikawa, and Tatsuo Arai. "Development of RSSI Measurement Sensor Modules for Robust Position Estimation of Wireless Sensor Network Node." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2010.5 (2010): 545–50. http://dx.doi.org/10.1299/jsmeicam.2010.5.545.

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Kim, Ji-seong, and Yong-kab Kim. "A Study on Distance Calculation Revision Algorithm using the Filtering of RSSI Measurement Results." Journal of the Institute of Internet Broadcasting and Communication 17, no. 1 (February 28, 2017): 25–31. http://dx.doi.org/10.7236/jiibc.2017.17.1.25.

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Yang, Ning, Shao Shan Zhong, and Hai Ting Zhu. "A Method of Calculating Attenuation Factor Based on RFID." Advanced Materials Research 718-720 (July 2013): 1711–16. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1711.

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As the indoor environment is complex,the attenuation model is imprecise.and the wireless location system is inaccurate.It is a effective method calculating attenuation factor with different distance RSSI data and selecting proper reference distance for solving single reference distance defect. The experiment verified that it can modify distance measurement mode and improve the accuracy and reliability of indoor distance measurement based on RFID.

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Ji, Qiang, Shifeng Zhang, and Haoguang Zhao. "Research on Optimization Algorithm of RSSI Positioning Parameters Based on Improved Particle Swarm Optimization." MATEC Web of Conferences 227 (2018): 02024. http://dx.doi.org/10.1051/matecconf/201822702024.

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The paper put forward to an algorithm based on hybrid mutation particle optimization swarm strategy (HMPOA), it can solve the position coordinates of the unknown nodes. The algorithm uses static sampling to determine the performance index values of particles, then the arc grouping method is used to divide the particle swarm into several subgroups. Finally, the hybrid mutation strategy is used to improve the convergence speed and positioning accuracy of the algorithm, which can overcome the location accuracy of unknown node that overly dependent on the RSSI physical measurement value. Numerical experiments show that the algorithm has fast convergence speed and high positioning accuracy for unknown nodes, and it is feasible for RSSI positioning.

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Veigt, Marius, Elisabeth Hardi, Michael Koerdt, Axel S. Herrmann, and Michael Freitag. "Investigation of using RFID for cure monitoring of glass fiber-reinforced plastics." Production Engineering 14, no. 4 (July 16, 2020): 499–507. http://dx.doi.org/10.1007/s11740-020-00972-x.

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Abstract Fiber composite components play an important role in the turnaround in energy policy as well as in stopping global warming. Therefore, it is essential to improve the manufacturing efficiency of these components. RFID technology is spreading to digitize and organize processes in production and logistics more efficiently. Since cure monitoring is a crucial factor in the manufacturing of composite components, the question arises whether the RFID technology is applicable for cure monitoring. This paper presents two methods of how an into glass fiber-reinforced plastics integrated RFID transponder could monitor the curing. Following the assumption that the change in permittivity of the glass fiber-reinforced plastic during curing influence the RFID signal, experiments in a measuring chamber (low-interference environment) were conducted. It was investigated whether the optimal response frequency of the integrated RFID transponder changes and whether the received signal strength indicator (RSSI) changes at a specific frequency during curing. As a reference method, the dielectric analysis as a well-known method for cure monitoring was used and compared with the RFID measurements. The results indicate that the optimal response frequency remains constant but the RSSI increases and possess a very high linear correlation with the measurement of the dielectric analysis in a low-interference environment. Consequently, the RFID technology is applicable to monitor the curing of glass fiber-reinforced plastics by measuring the RSSI in a low-interference environment.

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Zheng, Na, Yanli Du, and Qinghua Bai. "Robot Navigation Algorithm Based on Sensor Technology and Iterative Maximum a Posteriori Estimation." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 2 (March 20, 2019): 282–86. http://dx.doi.org/10.20965/jaciii.2019.p0282.

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The hybrid sensor network is mainly composed of static and dynamic sensor nodes. The dynamic node is the mobile robot with wireless sensor module installed. This paper proposes a robot navigation algorithm based on sensor technology and iterative maximum a posteriori estimation. It uses Kalman filter and least-squares fitting to improve RSSI measurement accuracy and the mobile robot only needs to use the received signal strength (RSSI) and odometer information to realize autonomous navigation in the sensing area. Moreover, static nodes are randomly deployed in the sensing area without a priori location information. Therefore, this algorithm has the advantages of low cost and ease of deployment. Both simulation and outdoor field experiments show the performance and effectiveness of the algorithm.

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., Amrita A. Agashe. "INDOOR LOCALIZATION IN SENSOR NETWORK WITH ESTIMATION OF DOA AND RSSI MEASUREMENT." International Journal of Research in Engineering and Technology 02, no. 11 (November 25, 2013): 597–601. http://dx.doi.org/10.15623/ijret.2013.0211091.

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ZHAO Xi. "Sensor Node Sell-Positioning Algorithm of WSN using RSSI Distance Measurement Technology." Journal of Convergence Information Technology 8, no. 5 (March 15, 2013): 733–39. http://dx.doi.org/10.4156/jcit.vol8.issue5.85.

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Yihong Qi, P. Jarmuszewski, Qingmai Zhou, M. Certain, and Ji Chen. "An Efficient TIS Measurement Technique Based on RSSI for Wireless Mobile Stations." IEEE Transactions on Instrumentation and Measurement 59, no. 9 (September 2010): 2414–19. http://dx.doi.org/10.1109/tim.2009.2036407.

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Iwata, Kazuhiro, and Akira Ono. "Consideration of Location Estimation by RSSI Measurement of Bluetooth Beacon with Eddystone." IEEJ Transactions on Electronics, Information and Systems 138, no. 10 (October 1, 2018): 1183–84. http://dx.doi.org/10.1541/ieejeiss.138.1183.

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Hwang, Jun Gyu, and Joon Goo Park. "AP Selection Criteria for UAV High-precision Indoor Positioning based on IEEE 802.11 RSSI Measurement." Journal of Institute of Control, Robotics and Systems 20, no. 12 (December 1, 2014): 1204–8. http://dx.doi.org/10.5302/j.icros.2014.14.9049.

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Hartawan, I. Nyoman Buda, and I. Gusti Made Ngurah Desnanjaya. "ANALISIS KINERJA PROTOKOL ZIGBEE DI DALAM DAN DI LUAR RUANGAN SEBAGAI MEDIA KOMUNIKASI DATA PADA WIRELESS SENSOR NETWORK." Jurnal RESISTOR (Rekayasa Sistem Komputer) 1, no. 2 (October 28, 2018): 65–72. http://dx.doi.org/10.31598/jurnalresistor.v1i2.320.

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Wireless sensor network is a technology used in supporting monitoring activities both inside and outside environment. Data communication on wireless sensor networks is done wirelessly. The Zigbee protocol is one of the protocols used in data communication on wireless sensor networks as an implementation of XBEE devices. In this study measurement of Zigbee protocol performance on XBEE devices inside and outside environment. The measurement conditions in the room are limited by the wall partition, while the outdoor conditions are line of sight. Measurements were made by sending packet data using XCTU software, by testing distance parameters, packet delay, packet loss, RSSI, and throughput with 84 Bytes packet data size. The results showed that the measurement results of XBEE Pro S2 devices that were carried out indoors were able to communicate with a maximum distance of 30 meters, while the outdoor measurements showed the communication capability of XBEE Pro S2 devices reached a maximum distance of 600 meters.

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Miao, Yisheng, Huarui Wu, and Lihong Zhang. "The Accurate Location Estimation of Sensor Node Using Received Signal Strength Measurements in Large-Scale Farmland." Journal of Sensors 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/2325863.

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

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Jovalekic, Nikola, Vujo Drndarevic, Ermanno Pietrosemoli, and Iain Zennaro. "Experimental Study of LoRa Transmission over Seawater." Sensors 18, no. 9 (August 29, 2018): 2853. http://dx.doi.org/10.3390/s18092853.

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Low Power Wide Area Networks (LPWANs) are gaining attention in both academia and industry by offering the possibility of connecting a large number of nodes over extended distances. LoRa is one of the technologies used as a physical layer in such networks. This paper investigates the LoRa links over seawater in two typical scenarios: clear Line-of-Sight (LOS) and obstructed path in two different Industrial, Scientific and Medical (ISM) radio bands: 868 MHz and 434 MHz. We used three different LoRa devices in the experiments: the Own Developed LoRa Transceiver (ODT) and two commercial transceivers. Firstly we investigated transceivers’ Receive Signal Strength Indicator (RSSI) and Signal-to-Noise (SNR) measurement chain linearity and provided correction factors for RSSI to correlate it with actual signal levels received at transceivers’ inputs. Next, we carried out field experiments for three different LoRa Spreading Factors, S F ∈ [ 7 , 10 , 12 ] , within a bandwidth of B W = 125 kHz and Coding Rate C R = 4 / 6 . The experiments showed that LoRa links are fully feasible over seawater at distances at least 22 km long, using only low-cost off-the-shelf rubber duck antennas in LOS path condition in both ISM bands. In addition, we showed that LoRa links can be established over 28 km obstructed LOS oversea path in ISM 434 MHz band, but using costly, higher gain antennas. Furthermore, the laboratory experiments revealed that RSSI is linear in a wide range, up to - 50 dBm, whereas the SNR measurement chain goes into saturation for Received Signal Strength (RSS) values higher than - 100 dBm. These findings enabled accurate interpretation of the results obtained in field experiments.

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Nick, T., and J. Götze. "Localization of passive UHF RFID Labels with Kalman Filter." Advances in Radio Science 10 (September 18, 2012): 119–25. http://dx.doi.org/10.5194/ars-10-119-2012.

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Abstract. Localization via Radio Frequency Identification (RFID) is frequently used in different applications nowadays. It has the advantage that next to its ostensible purpose of identifying objects via their unique IDs it can simultaneously be used for the localization of these objects. In this work it is shown how Received Signal Strength Indicator (RSSI) measurements at different antennae of a passive UHF RFID label can be combined for localization. The localization is only done based on the RSSI measurements and a Kalman Filter (KF). Because of non-linearities in the measurement function it is necessary to incorporate an Extended Kalman Filter (EKF) or an Unscented Kalman Filter (UKF) where simulations have shown that the UKF performs better than the EKF. Additionally to the selection of the filter there are different possibilities to increase the localization accuracy of the UKF: The advantages of using Reference Tags (RT) or more than one tag per trolley (relative positioning) in combination with an Unscented Kalman Filter are discussed and simulations results show that the localization error can be decreased significantly via these methods. Another possibility to increase the localization accuracy and in addition to achieve a more realistic simulation is the consideration of the angle between reader antenna and tag. Simulation results with the incorporation of different numbers of fixed antennae lead to the conclusion that this is a useful surplus in the localization.

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Houssaini, Dhouha El, Zina Mohamed, Sabrine Khriji, Kamel Besbes, and Olfa Kanoun. "Distance measurement correction based on feedback filter for RSSI localisation technique in WSNs." International Journal of Space-Based and Situated Computing 8, no. 3 (2018): 160. http://dx.doi.org/10.1504/ijssc.2018.097293.

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Kanoun, Olfa, Kamel Besbes, Dhouha El Houssaini, Zina Mohamed, and Sabrine Khriji. "Distance measurement correction based on feedback filter for RSSI localisation technique in WSNs." International Journal of Space-Based and Situated Computing 8, no. 3 (2018): 160. http://dx.doi.org/10.1504/ijssc.2018.10018390.

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Messous, Sana, Hend Liouane, Omar Cheikhrouhou, and Habib Hamam. "Improved Recursive DV-Hop Localization Algorithm with RSSI Measurement for Wireless Sensor Networks." Sensors 21, no. 12 (June 17, 2021): 4152. http://dx.doi.org/10.3390/s21124152.

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As localization represents the main backbone of several wireless sensor networks applications, several localization algorithms have been proposed in the literature. There is a growing interest in the multi-hop localization algorithms as they permit the localization of sensor nodes even if they are several hops away from anchor nodes. One of the most famous localization algorithms is the Distance Vector Hop (DV-Hop). Aiming to minimize the large localization error in the original DV-Hop algorithm, we propose an improved DV-Hop algorithm in this paper. The distance between unknown nodes and anchors is estimated using the received signal strength indication (RSSI) and the polynomial approximation. Moreover, the proposed algorithm uses a recursive computation of the localization process to improve the accuracy of position estimation. Experimental results show that the proposed localization technique minimizes the localization error and improves the localization accuracy.

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Masek, Pavel, Martin Stusek, Ekaterina Svertoka, Jan Pospisil, Radim Burget, Elena Simona Lohan, Ion Marghescu, Jiri Hosek, and Aleksandr Ometov. "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor." Data 6, no. 6 (June 10, 2021): 62. http://dx.doi.org/10.3390/data6060062.

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This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It covers two measurement locations, one at the university premises, and the second situated near the city center. The dataset’s primary goal is to provide the researchers lacking LoRaWAN devices with an opportunity to compare and analyze the information obtained from 303 different outdoor test locations transmitting to up to 20 gateways operating in the 868 MHz band in a varying metropolitan landscape. To collect the data, we developed a prototype equipped with a Microchip RN2483 Low-Power Wide-Area Network (LPWAN) LoRaWAN technology transceiver module for the field measurements. As an example of data utilization, we showed the Signal-to-noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) in relation to the closest gateway distance.

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Latal, Jan, Lukas Hajek, Ales Vanderka, Jan Vitasek, Petr Koudelka, and Stanislav Hejduk. "Real Measurements and Evaluation of the Influence of Atmospheric Phenomena on FSO Combined with Modulation Formats." Electronics ETF 20, no. 2 (July 14, 2017): 62. http://dx.doi.org/10.7251/els1620062l.

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The influence of atmospheric environment is fundamental for Free-Space Optical link (FSO). The atmosphere can significantly degrade the communication quality of FSO up to so low received power/RSSI level that it can lead to the loss of communication. For this reason, authors used a professional weather station built on site of FSO link for measurement of real atmospheric conditions such as wind speed, temperature, relative air humidity, air pressure and solar radiation. Random changing of these atmosphere parameters creates atmospheric turbulences, absorption and dispersion centers. It is necessary to specify the value of refractive index structure parameter Cn2 because it determines the influence of atmosphere on the FSO. The first part of this article includes the theoretical calculation of Cn2, there are used two models PAMELA and Macroscale-Meteorological model. The evaluation of the atmospheric influences and the RSSI value of received power level and also simulation of different types of modulation formats OOK-RZ, OOK-NRZ and BPSK in Optiwave is in tegral part of this article.

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TÜRKORAL, Türker, Özgür TAMER, Suat YETİŞ, Enes İNANÇ, and Levent ÇETİN. "Relative Localization of Wireless Sensor Nodes by Using the RSSI Data." Network Protocols and Algorithms 10, no. 1 (April 1, 2018): 1. http://dx.doi.org/10.5296/npa.v10i1.11533.

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Wireless sensor network is an emerging research field and a crucial infrastructure for Internet of Things (IoT) applications. Sensor nodes of a wireless network may connect via different wireless technologies like Wi-Fi, Bluetooth, ZigBee or WiMAX. In many applications, like field monitoring or smart buildings, the location of the nodes with respect to a global or relative coordinate system is essential information. The main focus of this work is the localization of wireless sensor nodes, using the Received Signal Strength Indicator (RSSI) metric of the wireless telecommunication infrastructure. We present both simulation and measurement results of the proposed method and compare the results with similar work.

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Poulose, Alwin, Jihun Kim, and Dong Seog Han. "A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements." Applied Sciences 9, no. 20 (October 16, 2019): 4379. http://dx.doi.org/10.3390/app9204379.

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Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data and Wi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and trilateration for Wi-Fi positioning. Additionally, a pedestrian dead reckoning (PDR) algorithm is used for position estimation in indoor scenarios. The proposed framework achieves a maximum of 1.17 m localization error for the rectangular motion of a pedestrian and a maximum of 0.44 m localization error for linear motion.

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Bian, He Shan, Zhao Hui Li, and Fang Zhao. "A Method of Indoor Localization Based on BLE4.0 Protocol of Using Beacons." Applied Mechanics and Materials 740 (March 2015): 765–68. http://dx.doi.org/10.4028/www.scientific.net/amm.740.765.

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In this paper we discuss our attempt to solve the problem of HAIP(High Accuracy Indoor Position) by using BLE4.0(Bluetooth Low Energy). According to previous research, Wi-Fi Positioning has mainly faced some big challenges. Accuracy is deteriorated by directional handset antennas, which affect the relative AP signal strength; Practical maximum reachable accuracy is 3-10 meters depending on environment; Wi-Fi activities is a big consumption of battery on Mobile Terminal; Now, The Bluetooth Low Energy technology is getting mature. In this paper, we use Bluetooth low energy on iOS device to solve the problem of high accuracy indoor position. In the data-preprocessing step, we use Kalman filter to process the RSSI. In the transition step of RSSI to Distance, we propose a novelty method to adjust the parameters of Log-Distance model dynamically and adaptively according to diagonal beacons’ measurement. We implement our technique and algorithm on iOS device with iOS7.0 SDK. The result shows that error reduced to 0.5m-1.2m range depending on the distance, achieved smaller power consumption.

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Zheng, Mei Lin, Pei Pei Zhai, and Xu Zou. "Research of Location Technology Based on Wireless Sensor Network." Advanced Materials Research 860-863 (December 2013): 2817–24. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2817.

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The node localization algorithms based on distance measurement are mainly studied in this paper.A stepwise weighted positioning algorithm based on distance measurement is designed on the basis of analyzing several typical positioning algorithms,static nodes positioning under natural environment is achieved. On this basis,with STMS32 selected as a core chip in the paper,the basic system circuit and peripheral interface circuit of node device is designed,and then using the development software BeeKit and CodeWarrior for node software design,localization algorithm is testedthrough experiment.After analyzing the results,the algorithm is mostly influenced by the RSSI ranging error,the coarse positioning of node can be achieved.

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M, Faleela Farzana, and Valarmathi A. "Secure architecture to circumvent collision using RSSI measurement in WSN: a cross layer design approach." Multimedia Tools and Applications 79, no. 13-14 (October 17, 2018): 8969–84. http://dx.doi.org/10.1007/s11042-018-6780-0.

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Journal articles on the topic 'Measurement RSSI'

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