Received signal strength based room level accuracy indoor localisation method

Buchman, Attila; Lung, Claudiu

doi:10.1109/coginfocom.2013.6719222

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…Whereas, in position estimation lateration, trilateration and multilateration techniques are used [2] [14]. For the range-free scheme there is DV-Hop and pattern matching method are used such as fingerprinting [10] [15]. Localization is very important in many real time applications.…”

Section: Figure 1 Localization Schemementioning

confidence: 99%

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RSSI Based Localization Scheme in Wireless Sensor Networks: A Survey

Mistry¹,

Mistry²

2015

2015 Fifth International Conference on Advanced Computing &Amp; Communication Technologies

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Now a days, Wireless Sensor Networks (WSNs) are most growing research area because of its low cost, infrastructure less, increase capabilities of nodes, real time and accurate. Localization is a major issue in the wireless sensor networks because it has a number of sensor nodes which are deployed at positions and they may not be fixed at their own position. In localization different techniques are used for distance and position estimation. In this paper we will focus on RSSI based localization in WSN which will show how to reduce location errors and improve accuracy by using various models and techniques. After that we will focus on how to make an algorithm scalable and improve the energy efficiency by providing authentication and key management.

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Section: Figure 1 Localization Schemementioning

confidence: 99%

“…Attila Buchman el at [15] proposed localization method to achieve a room level accuracy. Path loss model is used for distance measurement and fingerprinting method is used to map room level accurate indoor localization with method filtering in WLAN infrastructure.…”

Section: Multilaterationmentioning

confidence: 99%

RSSI Based Localization Scheme in Wireless Sensor Networks: A Survey

Mistry¹,

Mistry²

2015

2015 Fifth International Conference on Advanced Computing &Amp; Communication Technologies

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“…It has no prior knowledge about the node. For the range-free scheme, there is DV-Hop and pattern matching method is used such as fingerprinting [19] [20]. Localization is very important in many real time applications.…”

Section: Localization Overviewmentioning

confidence: 99%

Survey of Localization Techniques based on Mobile Beacon in Wireless Sensor Network

Ubnare¹,

Yadav²,

Chourasiya³

2017

International Journal of Innovative Research in Electrical, Ele

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Nowadays, Wireless Sensor Networks (WSNs) are most growing research area because of its low cost, infrastructure less, increased capabilities of nodes, real-time and accurate monitoring. It is employed to gather and forward information to the destination. Location of the event or collected information is very crucial for successive operations. This information may be obtained using the global positioning system, but it is not feasible for energy constraints networks. Location of sensor nodes may be obtained through localization techniques. Localization of nodes in a sensor network is a motivating analysis space, and a lot of works are done to this point. It is highly required to design energy aware, economical and scalable localization techniques for WSNs. In this paper, we have done analyze of various localization techniques, and few possible future research directions.

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“…Moreover, it can benefit from the room level accuracy [27][28][29] to extract statistical information which can be deployed to better market to customers, find hangouts in airports, and even most popular shop in shopping malls, and so forth. In this work, we focus on a room localization which is the prediction of an occupied room in which the mobile device is currently in.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

Rezgui

Pei

Chen

et al. 2017

Mobile Information Systems

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This paper proposes an efficient and effective WiFi fingerprinting-based indoor localization algorithm, which uses the Received Signal Strength Indicator (RSSI) of WiFi signals. In practical harsh indoor environments, RSSI variation and hardware variance can significantly degrade the performance of fingerprinting-based localization methods. To address the problem of hardware variance and signal fluctuation in WiFi fingerprinting-based localization, we propose a novel normalized rank based Support Vector Machine classifier (NR-SVM). Moving from RSSI value based analysis to the normalized rank transformation based analysis, the principal features are prioritized and the dimensionalities of signature vectors are taken into account. The proposed method has been tested using sixteen different devices in a shopping mall with 88 shops. The experimental results demonstrate its robustness with no less than 98.75% correct estimation in 93.75% of the tested cases and 100% correct rate in 56.25% of cases. In the experiments, the new method shows better performance over the KNN, Naïve Bayes, Random Forest, and Neural Network algorithms. Furthermore, we have compared the proposed approach with three popular calibration-free transformation based methods, including difference method (DIFF), Signal Strength Difference (SSD), and the Hyperbolic Location Fingerprinting (HLF) based SVM. The results show that the NR-SVM outperforms these popular methods.

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Received signal strength based room level accuracy indoor localisation method

Cited by 9 publications

References 6 publications

RSSI Based Localization Scheme in Wireless Sensor Networks: A Survey

RSSI Based Localization Scheme in Wireless Sensor Networks: A Survey

Survey of Localization Techniques based on Mobile Beacon in Wireless Sensor Network

An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

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