Efficient Sensor Localization for Indoor Environments Using Classification of Link Quality Patterns

Ha, Young-Guk; Eun, Ae-cheoun; Byun, Yung-Cheol

doi:10.1155/2013/701259

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…In deterministic methods, environment is divided into cells to establish a radio map and estimated position is obtained by finding the best match between the new measurement and the measurements in the radio map [10,11,12,13]. In probabilistic methods, also called as distributed-based methods, signal strength distributions obtained from Access Points (APs) are used to construct radio map and probability distribution functions are used to estimate the position of subject [14,15,16,17,18].…”

Section: Related Workmentioning

confidence: 99%

“…K-nearest neighbor is applied in [11], where radio map is constructed to use in comparison of each signal strength value in online phase and mean of the best matched K position is determined as the estimated position. In [12], an indoor localization method is proposed for Zigbee sensor networks using classification of link quality patterns between each reference node and a target node in a specific location rather than using calculation with signal strength, arrival time, or angle. To classify link quality patterns for each location, an artificial neural network (ANN) classifier is used.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A comparative study on machine learning algorithms for indoor positioning

Bozkurt¹,

Elibol

Günal

et al. 2015

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA)

117

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Fingerprinting based positioning is commonly used for indoor positioning. In this method, initially a radio map is created using Received Signal Strength (RSS) values that are measured from predefined reference points. During the positioning, the best match between the observed RSS values and existing RSS values in the radio map is established as the predicted position. In the positioning literature, machine learning algorithms have widespread usage in estimating positions. One of the main problems in indoor positioning systems is to find out appropriate machine learning algorithm. In this paper, selected machine learning algorithms are compared in terms of positioning accuracy and computation time. In the experiments, UJIIndoorLoc indoor positioning database is used. Experimental results reveal that k-Nearest Neighbor (k-NN) algorithm is the most suitable one during the positioning. Additionally, ensemble algorithms such as AdaBoost and Bagging are applied to improve the decision tree classifier performance nearly same as k-NN that is resulted as the best classifier for indoor positioning.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A comparative study on machine learning algorithms for indoor positioning

Bozkurt¹,

Elibol

Günal

et al. 2015

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA)

117

View full text Add to dashboard Cite

show abstract

“…As per the definition of RSSI and LQI mentioned above, these two parameters are mostly used for nodes' localization and distance measurement [11][12][13].…”

Section: Using Rssi and Lqi In Wsnmentioning

confidence: 99%

RSSI and LQI Data Clustering Techniques to Determine the Number of Nodes in Wireless Sensor Networks

Wang

Guardiola

2014

International Journal of Distributed Sensor Networks

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With the rapid proliferation of wireless sensor networks, different network topologies are likely to exist in the same geographical region, each of which is able to perform its own functions individually. However, these networks are prone to cause interference to neighbor networks, such as data duplication or interception. How to detect, determine, and locate the unknown wireless topologies in a given geographical area has become a significant issue in the wireless industry. This problem is especially acute in military use, such as spy-nodes detection and communication orientation systems. In this paper, three different clustering methods are applied to classify the RSSI and LQI data recorded from the unknown wireless topology into a certain number of groups in order to determine the number of active sensor nodes in the unknown wireless topology. The results show that RSSI and LQI data are capable of determining the number of active communication nodes in wireless topologies.

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“…Finally, the decision tree of the selected cluster is used to predict the location of the user. Besides using the values of the received signal strength, in [15], the authors proposed to use the link quality indicator (LQI) values for location prediction. They modeled the location prediction problem as a classification problem and used a neural network model to solve the problem.…”

Section: International Journal Of Distributed Sensor Networkmentioning

confidence: 99%

Robust Indoor Sensor Localization Using Signatures of Received Signal Strength

Leu

Lee

Chen

2013

International Journal of Distributed Sensor Networks

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Indoor localization based on the received signal strength (RSS) values of the wireless sensors has recently received a lot of attention. However, due to the interference of other wireless devices and human activities, the RSS value varies significantly over different times. This hinders exact location prediction using RSS values. In this paper, we propose three methods to counter the adverse effect of the RSS value variation on location prediction. First, we propose to use an index location to select the best radio map, among several preconstructed radio maps, for online location prediction. Second, for an observed value of the signal strength of a sensor, we record, respectively, the distances from the sensor to the nearest location and the farthest location where the signal strength value has been observed. The minimal and maximal (min-max) distances for each signal strength value of a sensor are then used to reduce the search space in online location prediction. Third, a location-dependent received signal strength vector, called the RSS signature, is used to predict the location of a user. We have built a system, called the region-point system, based on the proposed three methods. The experimental results show that the region-point system offers less mean position error compared to the existing methods, namely, RADAR, TREE, and CaDet. Furthermore, the index location method correctly selects the best radio map for online location prediction, and the min-max distance method promotes the prediction accuracy of RADAR by restricting the search space of RADAR in location prediction.

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Efficient Sensor Localization for Indoor Environments Using Classification of Link Quality Patterns

Cited by 8 publications

References 13 publications

A comparative study on machine learning algorithms for indoor positioning

A comparative study on machine learning algorithms for indoor positioning

RSSI and LQI Data Clustering Techniques to Determine the Number of Nodes in Wireless Sensor Networks

Robust Indoor Sensor Localization Using Signatures of Received Signal Strength

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