Combining Kalman Filtering with ZigBee Protocol to Improve Localization in Wireless Sensor Network

Madani, Bouchra El; Yao, Anne Paule; Lyhyaoui, Abdelouahid

doi:10.1155/2013/252056

Cited by 13 publications

(11 citation statements)

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“…A statistics-based localization algorithm, such as the Kalman filter algorithm [23][24][25], can be used to predict the location of the target node. The Kalman filter algorithm is a recursive method that makes the variance of error and range regularly decrease until finally arriving at convergence.…”

Section: Related Workmentioning

confidence: 99%

Accurate Wireless Sensor Localization Technique Based on Hybrid PSO-ANN Algorithm for Indoor and Outdoor Track Cycling

et al. 2016

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This study aims to determine the distance between the mobile sensor node (i.e., bicycle) and the anchor node (i.e., coach) in outdoor and indoor environments. Two approaches were considered to estimate such a distance. The first approach was based on the traditional channel propagation model that used the log-normal shadowing model (LNSM), while the second approach was based on a proposed hybrid particle swarm optimizationartificial neural network (PSO-ANN) algorithm to improve the distance estimation accuracy of the mobile node. The first method estimated the distance according to the LNSM and the measured received signal strength indicator (RSSI) of the anchor node, which in turn used the ZigBee wireless protocol. The LNSM parameters were measured based on the RSSI measurements in both outdoor and indoor environments. A feed-forward neural network type and the Levenberg-Marquardt training algorithm were used to estimate the distance between the mobile node and the coach. The hybrid PSO-ANN algorithm significantly improved the distance estimation accuracy more than the traditional LNSM method without additional components. The hybrid PSO-ANN algorithm achieved a mean absolute error of 0.022 m and 0.208 m for outdoor and indoor environments, respectively. The effect of anchor node density on localization accuracy was also investigated in the indoor environment.

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Section: Related Workmentioning

confidence: 99%

Accurate Wireless Sensor Localization Technique Based on Hybrid PSO-ANN Algorithm for Indoor and Outdoor Track Cycling

et al. 2016

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“…Other approaches include developing different path loss models as in Refs. [15,16] and improving the estimated distances or locations through the use of maximum-likelihood estimation [17] or a Kalman filter [18]. Almost all of these methods of RSS ranging involve some sort of calibration (solving for environmental characterization values, calculating a path loss model, etc.)…”

Section: Range-based Localizationmentioning

confidence: 99%

Radio Frequency-Based Indoor Localization in Ad-Hoc Networks

Golestanian¹,

Siva²,

Poellabauer³

2017

Ad Hoc Networks

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The increasing importance of location-aware computing and context-dependent information has led to a growing interest in low-cost indoor positioning with submeter accuracy. Localization algorithms can be classified into range-based and range-free techniques. Additionally, localization algorithms are heavily influenced by the technology and network architecture utilized. Availability, cost, reliability and accuracy of localization are the most important parameters when selecting a localization method. In this chapter, we introduce basic localization techniques, discuss how they are implemented with radio frequency devices and then characterize the localization techniques based on the network architecture, utilized technologies and application of localization. We then investigate and address localization in indoor environments where the absence of global positioning system (GPS) and the presence of unique radio propagation properties make this problem one of the most challenging topics of localization in wireless networks. In particular, we study and review the previous work for indoor localization based on radio frequency (RF) signaling (like Bluetooth-based localization) to illustrate localization challenges and how some of them can be overcome.

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“…A detailed survey of range-based methods can be found in [6]. Several performance improvements have been developed, for example, Least Squares [4] and Kalman filtering [7]. Distances provided by rangebased methods are used in calculating moving object's coordinates, for example, by using trilateration [6].…”

Section: Overview Of Rtlsmentioning

confidence: 99%