A Bayesian Sampling Approach to In-Door Localization of Wireless Devices Using Received Signal Strength Indication

Seshadri, V.; Záruba, Gergely; Huber, Manfred

doi:10.1109/percom.2005.1

Cited by 100 publications

(57 citation statements)

References 10 publications

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“…WiFi-based location systems can be either deterministic [5][6][7][8] or probabilistic [1][2][3][4] in matching RSSI frommobile devices to a radio map. The best systems claim 90% accuracy with an error of less than 1~2 meters.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Localization: Enhancing WiFi-Based Position Estimation with Neighborhood Links in Clusters

Chan

Chiang

Chen

et al. 2006

Lecture Notes in Computer Science

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Abstract. Location-aware services can benefit from accurate and reliable indoor location tracking. The widespread adoption of 802.11x wireless LAN as the network infrastructure creates the opportunity to deploy WiFi-based location services with few additional hardware costs. While recent research has demonstrated adequate performance, localization error increases significantly in crowded and dynamic situations due to electromagnetic interferences. This paper proposes collaborative localization as an approach to enhance position estimation by leveraging more accurate location information from nearby neighbors within the same cluster. The current implementation utilizes ZigBee radio as the neighbor-detection sensor. This paper introduces the basic model and algorithm for collaborative localization. We also report experiments to evaluate its performance under a variety of clustering scenarios. Our results have shown 28.2-56% accuracy improvement over the baseline system Ekahau, a commercial WiFi localization system.

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Section: Related Workmentioning

confidence: 99%

Collaborative Localization: Enhancing WiFi-Based Position Estimation with Neighborhood Links in Clusters

Chan

Chiang

Chen

et al. 2006

Lecture Notes in Computer Science

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“…Localization of targets based on received signal strength in WSN is a promising technique which has received extensive attention [17][18][19][20][21]. However, little attention has been given to the real-time CS-based DFL and efficient measurement method.…”

Section: Related Workmentioning

confidence: 99%

A real-time device-free localization system using correlated RSS measurements

Yang

Huang

Guo

et al. 2013

J Wireless Com Network

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Device-free localization (DFL) with wireless sensor networks (WSN) is an emerging technology for target localization, which has received much attention in the area of Internet of Things. Received signal strength (RSS) measurements are the key to realize DFL and mainly affects the localization performance. Most existing approaches need to measure the RSS of all the wireless links in WSN, which take much time on measurement process and localization algorithm due to the large amounts of RSS data, thus they are inefficient, especially in the case of target tracking. In this paper, by making full use of the consecutiveness of motion, we present an efficient measurement strategy based on a small set of correlated wireless links. Furthermore, a lightweight compressed maximum matching select (CMMS) algorithm is proposed to localize target, which only needs a small-scale matrix-vector product operating for one estimation. The proposed approach can significantly reduce the number of RSS measurements and improve the real-time capability of the DFL system. Experimental results demonstrate the superior performance of the proposed method in the context of target localization and tracking.

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“…In many localization schemes, the distance between two nodes is obtained on the basis of the received signal strength indicator (RSSI) [15][16][17], which is calculated on the basis of the strength of the received signals. However, the RSSI tends to get affected by environmental conditions such as reflections from metallic surfaces and superposition of electromagnetic fields.…”

Section: Network Structurementioning

confidence: 99%

An Anchor-Free Localization Scheme with Kalman Filtering in ZigBee Sensor Network

Isokawa

Motomura

Ohtsuka

et al. 2013

ISRN Sensor Networks

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A localization scheme for wireless sensor networks is proposed and its performances are investigated in this paper. The proposed scheme is an anchor-free scheme, in which no geometrical information of sensor nodes is required for their localization. Thus, only local interaction among sensor nodes is used to estimate their locations. This scheme employs the link quality indicator and hop count between sensor nodes for location estimation. A weighted averaging and Kalman filtering schemes are incorporated in order to reduce the effects of the measurement errors in the obtained values of the link quality indicator. We performed experiments by implementing the proposed scheme on ZigBee sensor modules. The results of our experiments indicate that estimation could be successfully performed for networks comprising four sensor nodes.

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A Bayesian Sampling Approach to In-Door Localization of Wireless Devices Using Received Signal Strength Indication

Abstract: Abstract

Cited by 100 publications

References 10 publications

Collaborative Localization: Enhancing WiFi-Based Position Estimation with Neighborhood Links in Clusters

Collaborative Localization: Enhancing WiFi-Based Position Estimation with Neighborhood Links in Clusters

A real-time device-free localization system using correlated RSS measurements

An Anchor-Free Localization Scheme with Kalman Filtering in ZigBee Sensor Network

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