Node density independent localization

Kusý, Branislav; Lédeczi, Ákos; Maróti, Miklós; Meertens, L.G.L.T.

doi:10.1145/1127777.1127844

Cited by 68 publications

(78 citation statements)

References 9 publications

(13 reference statements)

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“…In non-radio map based technologies, RIPS [27] [28] utilized the interference behavior between two nodes with slightly frequency difference to localize target. This is improved in [29] [30] used Doppler effect work to track mobile target and improve the system accuracy.…”

Section: Related Workmentioning

confidence: 99%

MODLoc: Localizing Multiple Objects in Dynamic Indoor Environment

Guo

Zhang

et al. 2014

IEEE Trans. Parallel Distrib. Syst.

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Abstract-Radio Frequency (RF) based technologies play an important role in indoor localization, since Radio Signal Strength (RSS) can be easily measured by various wireless devices without additional cost. Among these, radio map based technologies (also referred as fingerprinting technologies) are attractive due to high accuracy and easy deployment. However, these technologies have not been extensively applied on real environment for two fatal limitations. First, it is hard to localize multiple objects. When the number of target objects is unknown, constructing a radio map of multiple objects is almost impossible. Second, environment changes will generate different multipath signals and severely disturb the RSS measurement, making laborious retraining inevitable. Motivated by these, in this paper, we propose a novel approach, called Line-of-sight radio map matching, which only reserves the LOS signal among nodes. It leverages frequency diversity to eliminate the multipath behavior, making RSS more reliable than before. We implement our system MODLoc based on TelosB sensor nodes and commercial 802.11 NICs with Channel State Information (CSI) as well. Through extensive experiments, it shows that the accuracy does not decrease when localizing multiple targets in a dynamic environment. Our work outperforms the traditional methods by about 60%. More importantly, no calibration is required in such environment. Furthermore, our approach presents attractive flexibility, making it more appropriate for general RF-based localization studies than just the radio map based localization.

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Section: Related Workmentioning

confidence: 99%

MODLoc: Localizing Multiple Objects in Dynamic Indoor Environment

Guo

Zhang

et al. 2014

IEEE Trans. Parallel Distrib. Syst.

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“…To support moderate multi-path environments where a substantial fraction of range estimates have large errors, RIPS performs range estimation and localization in an iterative manner [23]. Obtaining the range from the noisy phase measurements at various carrier frequencies is a least-squares optimization procedure.…”

Section: A Representative Approachmentioning

confidence: 99%

Wireless sensor node localization

Lédeczi

Maróti

2012

Phil. Trans. R. Soc. A.

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For most wireless sensor network (WSN) applications, the positions of the sensor nodes need to be known. Global positioning systems have not fitted into WSNs very well owing to their price, power consumption, accuracy and limitations in their operating environment. Hence, the last decade has brought about a large number of proposed methods for WSN node localization. They show tremendous variation in the physical phenomena they use, the signal properties they measure, the resources they consume, as well as in their accuracy, range, advantages and limitations. This paper provides a high-level, comprehensive overview of this very active research area.

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“…Signal properties such as strength, phase, or frequency are analyzed to derive range data for position estimation. One benefit of using RF is that it has been shown to achieve localization accuracy on the order of centimeters, even in sparse networks [42]. On the other hand, because typical sensor node radios transmit at frequencies between 400 MHz to 2.6 GHz, sampling the raw signal for phase or frequency cannot be done with resource-constrained hardware.…”

Section: Measurement Phasementioning

confidence: 99%

A Survey on Localization for Mobile Wireless Sensor Networks

Amundson

Koutsoukos

2009

Lecture Notes in Computer Science

253

142

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Abstract. Over the past decade we have witnessed the evolution of wireless sensor networks, with advancements in hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of small-profile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. In this paper, we present a survey on localization methods for mobile wireless sensor networks. We provide taxonomies for mobile wireless sensors and localization, including common architectures, measurement techniques, and localization algorithms. We conclude with a description of real-world mobile sensor applications that require position estimation.

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Node density independent localization

Cited by 68 publications

References 9 publications

MODLoc: Localizing Multiple Objects in Dynamic Indoor Environment

MODLoc: Localizing Multiple Objects in Dynamic Indoor Environment

Wireless sensor node localization

A Survey on Localization for Mobile Wireless Sensor Networks

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