Experimental analysis of RSSI-based distance estimation for wireless sensor networks

Mahapatra, Ranjan Kumar; Shet, N. Shekar V.

doi:10.1109/discover.2016.7806221

Cited by 22 publications

(10 citation statements)

References 26 publications

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“…定位技术有 GPS [1] [2]，红外线 [3]，超声波 [4]，蓝牙 [5]，WiFi [6]等，这些技术运用广泛。测距的方法有 TDOA [7] [8]，TOA [9] [10]，AOA [11]，RSSI [12] 1 2 3 , , …”

Section: 随着物联网相关技术的兴起，基于无线传感网络(Wireless Sensing Network Wsn)和无线局域网 unclassified

Research on Steepest Descent Method Based Indoor Location Estimation Technology

黄¹

2017

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Indoor positioning technology solves the problems that the satellite signal is weak when it reaches the ground and the signal cannot penetrate the structure. Due to the RSSI being limited by multipath effect and the volatility of time, the traditional indoor positioning technology is unstable when positioning. Aiming at the key issues of research in indoor positioning of WSN, a method based on RSSI positioning of the steepest descent algorithm WSN is proposed in the paper. The method uses three access points, namely the reference node, to find the coordinates of the nodes. In addition, the method combines log-normal shadow fading model, trilateral localization algorithm and steepest descent method. Firstly, using the log-normal shadow fading model the value of RSSI has been gotten from the node under test to the reference signal; besides, the trilateral localization algorithm is used to estimate the locating node coordinates; finally, the estimated coordinates are optimized by the optimization method. The simulation results show that, compared with the traditional positioning method based on RSSI, the indoor positioning technology based on the steepest descent method has better positioning accuracy.

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Section: 随着物联网相关技术的兴起，基于无线传感网络(Wireless Sensing Network Wsn)和无线局域网 unclassified

Research on Steepest Descent Method Based Indoor Location Estimation Technology

黄¹

2017

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“…Spectrum monitoring is not the only application where RSS measurements play a central role. Others include indoor localization [5], [6], tracking [7], distance estimation [8] and distributed asynchronous regression [9].…”

Section: Introductionmentioning

confidence: 99%

Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes

Santos

Murillo-Fuentes

Djurić

2019

IEEE Trans. Signal Process.

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In this paper, we address the estimation of a timevarying spatial field of received signal strength (RSS) by relying on measurements from randomly placed and not very accurate sensors. We employ a radio propagation model where the path loss exponent and the transmitted power are unknown with Gaussian priors whose hyper-parameters are estimated by applying the empirical Bayes method. We consider the locations of the sensors to be imperfectly known, which entails that they represent another source of error in the model. The propagation model includes shadowing which is considered to be a zero-mean Gaussian process where the correlation of attenuation between two spatial points is quantified by an exponential function of the distance between the points. The location of the transmitter is also unknown and is estimated from the data. We propose to estimate time-varying RSS fields by a recursive Bayesian method and crowdsourcing. The method is based on Gaussian processes (GP), and it produces the joint distribution of the spatial field. Further, it summarizes all the acquired information by keeping the size of the needed memory bounded. We also present the Bayesian Cramér-Rao bound (BCRB) of the estimated parameters. Finally, we illustrate the performance of our method with experimental results on synthetic and real data sets.

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“…transmitters based on the technology of Bluetooth Low Energy (BLE). When considering the measured power of a signal, called RSSI (Received Signal Strength Indicator) [4], a distance from the transmitter can be estimated. In addition, a more accurate value of distance can be determined using various filtration methods.…”

Section: Introductionmentioning

confidence: 99%

An IoT Approach to Positioning of a Robotic Vehicle

Dvorak

Doležel

2018

Advances in Intelligent Systems and Computing

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This paper presents and evaluates one approach to the problems of automatic control of a vehicle movement in a large outdoor area. The positioning of the vehicle in the area is provided by iBeacons, located at the edges of the given surface. The iBeacon is a small and lowpower device which periodically transmits its UUID (Universally Unique Identifier) number through the interface of a Bluetooth 4.x. The vehicle should be able to calculate its position according to the power of the signal, considering the location of the iBeacons. To be more specific, the triangulation method is applied to determine the position. According to the set of experiments presented at the end of the paper, the position error of a robotic vehicle is mostly less then 1m.

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Experimental analysis of RSSI-based distance estimation for wireless sensor networks

Cited by 22 publications

References 26 publications

Research on Steepest Descent Method Based Indoor Location Estimation Technology

Research on Steepest Descent Method Based Indoor Location Estimation Technology

Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes

An IoT Approach to Positioning of a Robotic Vehicle

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