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105
Properties of indoor received signal strength for WLAN location fingerprinting
- Mobile and Ubiquitous Systems - Networking and Services. MOBIQUITOUS. The First Annual International Conference
, 2004
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Indoor Location Sensing Using Geo-Magnetism
- In ACM MobiSys
, 2011
"... We present an indoor positioning system that measures location using disturbances of the Earth's magnetic field caused by structural steel elements in a building. The presence of these large steel members warps the geomagnetic field in a way that is spatially varying but temporally stable. To l ..."
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Cited by 46 (0 self)
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We present an indoor positioning system that measures location using disturbances of the Earth's magnetic field caused by structural steel elements in a building. The presence of these large steel members warps the geomagnetic field in a way that is spatially varying but temporally stable. To localize, we measure the magnetic field using an array of e-compasses and compare the measurement with a previously obtained magnetic map. We demonstrate accuracy within 1 meter 88 % of the time in experiments in two buildings and across multiple floors within the buildings. We discuss several constraint techniques that can maintain accuracy as the sample space increases. Categories and Subject Description
Kernel-Based Positioning in Wireless Local Area Networks
, 2007
"... The recent proliferation of Location-Based Services (LBSs) has necessitated the development of effective indoor positioning solutions. In such a context, Wireless Local Area Network (WLAN) positioning is a particularly viable solution in terms of hardware and installation costs due to the ubiquity ..."
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Cited by 32 (0 self)
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The recent proliferation of Location-Based Services (LBSs) has necessitated the development of effective indoor positioning solutions. In such a context, Wireless Local Area Network (WLAN) positioning is a particularly viable solution in terms of hardware and installation costs due to the ubiquity of WLAN infrastructures. This paper examines three aspects of the problem of indoor WLAN positioning using received signal strength (RSS). First, we show that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results. Second, we explore the problem of access point (AP) selection for positioning and demonstrate the need for further research in this area. Third, we present a kernelized distance calculation algorithm for comparing RSS observations to RSS training records. Experimental results indicate that the proposed system leads to a 17 percent (0.56 m) improvement over the widely used K-nearest neighbor and histogram-based methods.
Secret Keys from Entangled Sensor Motes: Implementation and Analysis
- ACM CONFERENCE ON WIRELESS NETWORK SECURITY (WISEC 2010)
, 2010
"... Key management in wireless sensor networks does not only face typical, but also several new challenges. The scale, resource limitations, and new threats such as node capture and compromise necessitate the use of an on-line key generation, where secret keys are generated by the nodes themselves. Howe ..."
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Cited by 23 (2 self)
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Key management in wireless sensor networks does not only face typical, but also several new challenges. The scale, resource limitations, and new threats such as node capture and compromise necessitate the use of an on-line key generation, where secret keys are generated by the nodes themselves. However, the cost of such schemes is high since their secrecy is based on computational complexity. Recently, several research contributions justified that the wireless channel itself can be used to generate information-theoretic secure keys between two parties. By exchanging sampling messages during movement, a bit string can be derived that is only known to the involved entities. Yet, movement is not the only possibility to generate randomness. The channel response is also strongly dependent on the frequency of the transmitted signal. In our work, we introduce a protocol for key generation based on the frequency-selectivity of channel fading. The great practical advantage of this approach is that we do not rely on node movement as the source of randomness. Thus, the frequent case of a sensor network with static motes is supported. Furthermore, the error correction property of the proposed protocol mitigates the effects of measurement errors and other temporal effects, giving rise to a key agreement rate of over 97%. We show the applicability of our protocol by implementing it on MICAz motes, and evaluate its robustness and secrecy through experiments and analysis.
Understanding the limitations of transmit power control for indoor wlans
- In IMC ’07
, 2007
"... A wide range of transmit power control (TPC) algorithms have been proposed in recent literature to reduce interference and increase capacity in 802.11 wireless networks. However, few of them have made it to practice. In many cases this gap is attributed to lack of suitable hardware support in wirele ..."
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Cited by 22 (3 self)
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A wide range of transmit power control (TPC) algorithms have been proposed in recent literature to reduce interference and increase capacity in 802.11 wireless networks. However, few of them have made it to practice. In many cases this gap is attributed to lack of suitable hardware support in wireless cards to implement these algorithms. In particular, many research efforts have indicated that wireless card vendors need to support power control mechanisms in a finegrained manner – both in the number of possible power levels and the time granularity at which the controls can be applied. In this paper we claim that even if fine-grained power control mechanisms were to be made available by wireless card vendors, algorithms would not be able to properly leverage such degrees of control in typical indoor environments. We prove this claim through rigorous empirical analysis and then build a tunable empirical model (Model-TPC) that can determine the granularity of power control that is actually useful. To illustrate the importance of our solution, we conclude by demonstrating the impact of choice of power control granularity on Internet applications where wireless clients interact with servers on the Internet. We observe that the number of feasible power was found to be between 2-4 for most indoor environments. We believe that the results from this study can serve as the right set of assumptions to build practically realizable TPC algorithms in the future.
Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing
- IEEE TRANS. KNOWLEDGE AND DATA ENG
, 2006
"... In this paper, we present an algorithm for multidimensional vector regression on data that are highly uncertain and nonlinear, and then apply it to the problem of indoor location estimation in a wireless local area network (WLAN). Our aim is to obtain an accurate mapping between the signal space and ..."
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Cited by 17 (3 self)
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In this paper, we present an algorithm for multidimensional vector regression on data that are highly uncertain and nonlinear, and then apply it to the problem of indoor location estimation in a wireless local area network (WLAN). Our aim is to obtain an accurate mapping between the signal space and the physical space without requiring too much human calibration effort. This location estimation problem has traditionally been tackled through probabilistic models trained on manually labeled data, which are expensive to obtain. In contrast, our algorithm adopts Kernel Canonical Correlation Analysis (KCCA) to build a nonlinear mapping between the signal-vector space and the physical location space by transforming data in both spaces into their canonical features. This allows the pairwise similarity of samples in both spaces to be maximally correlated using kernels. We use a Gaussian kernel to adapt to the noisy characteristics of signal strengths and a Matérn kernel to sense the changes in physical locations. By using real data collected in an 802.11 wireless LAN environment, we achieve accurate location estimation for pervasive computing while requiring a much smaller set of labeled training data than previous methods.
Indoor localization using multiple wireless technologies
- Mobile Adhoc and Sensor Systems (MASS
, 2007
"... Abstract — Indoor localization techniques using location fingerprints are gaining popularity because of their cost-effectiveness compared to other infrastructure-based location systems. However, their reported accuracy fall short of their counterparts. In this paper, we investigate many aspects of f ..."
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Cited by 12 (3 self)
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Abstract — Indoor localization techniques using location fingerprints are gaining popularity because of their cost-effectiveness compared to other infrastructure-based location systems. However, their reported accuracy fall short of their counterparts. In this paper, we investigate many aspects of fingerprint-based location systems in order to enhance their accuracy. First, we derive analytically a robust location fingerprint definition, and then verify it experimentally as well. We also devise a way to facilitate under-trained location systems through simple linear regression technique. This technique reduces the training time and effort, and can be particularly useful when the surrounding or setup of the localization area changes. We further show experimentally that because of the positions of some access points or the environmental factors around them, their signal strength correlates nicely with distance. We argue that it would be more beneficial to give special consideration to these access points for location computation, owing to their ability to distinguish locations distinctly in signal space. The probability of encountering such access points will be even higher when we denote a location’s signature using the signals of multiple wireless technologies collectively. We present the results of two well-known localization algorithms (K-Nearest Neighbor and Bayesian Probabilistic Model) when the above factors are exploited, using Bluetooth and Wi-Fi signals. We have observed significant improvement in their accuracy when our ideas are implemented. I.
Improving Location Fingerprinting through Motion Detection and Asynchronous Interval Labeling
"... Abstract. Wireless signal strength fingerprinting has become an increasingly popular technique for realizing indoor localization systems using existing WiFi infrastructures. However, these systems typically require a time-consuming and costly training phase to build the radio map. Moreover, since ra ..."
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Cited by 7 (0 self)
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Abstract. Wireless signal strength fingerprinting has become an increasingly popular technique for realizing indoor localization systems using existing WiFi infrastructures. However, these systems typically require a time-consuming and costly training phase to build the radio map. Moreover, since radio signals change and fluctuate over time, map maintenance requires continuous re-calibration. We introduce a new concept called “asynchronous interval labeling ” that addresses these problems in the context of user-generated place labels. By using an accelerometer to detect whether a device is moving or stationary, the system can continuously and unobtrusively learn from all radio measurements during a stationary period, thus greatly increasing the number of available samples. Movement information also allows the system to improve the user experience by deferring labeling to a later, more suitable moment. Initial experiments with our system show considerable increases in data collected and improvements to inferred location likelihood, with negligible overhead reported by users. 1
Robust and distributed stochastic localization in sensor networks: Theory and experimental results
- ACM Trans. Sensor Networks
"... We present a robust localization system allowing wireless sensor networks to determine the phys-ical location of their nodes. The coverage area is partitioned into regions and we seek to identify the region of a sensor based on observations by stationary clusterheads. Observations (e.g., signal stre ..."
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Cited by 7 (4 self)
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We present a robust localization system allowing wireless sensor networks to determine the phys-ical location of their nodes. The coverage area is partitioned into regions and we seek to identify the region of a sensor based on observations by stationary clusterheads. Observations (e.g., signal strength) are assumed random. We pose the localization problem as a composite multi-hypothesis testing problem, develop the requisite theory, and address the problem of optimally placing clus-terheads. We show that localization decisions can be distributed by appropriate in-network pro-cessing. The approach is validated in a testbed yielding promising results.
Crowdsourced indoor localization for diverse devices through radiomap fusion
- in Intl. Conference on Indoor Positioning and Indoor Navigation (IPIN
, 2013
"... Abstract—Crowdsourcing is an emerging field that allows to tackle difficult problems by soliciting contributions from common people, rather than trained professionals. In the post-pc era, where smartphones dominate the personal computing market of-fering both constant mobility and large amounts of s ..."
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Cited by 7 (5 self)
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Abstract—Crowdsourcing is an emerging field that allows to tackle difficult problems by soliciting contributions from common people, rather than trained professionals. In the post-pc era, where smartphones dominate the personal computing market of-fering both constant mobility and large amounts of spatiotempo-ral sensory data, crowdsourcing is becoming increasingly popular. In this context, crowdsourcing stands as the only viable solution for collecting the large amount of location-related network data required to support location-based services, e.g., the signal strength radiomap of a fingerprinting localization system inside a multi-floor building. However, this benefit does not come for free, because crowdsourcing also poses new challenges in radiomap creation. We focus on the problem of device diversity that occurs frequently as the contributors usually carry heterogeneous mobile devices that report network measurements very differently. We demonstrate with simulations and experimental results that the traditional signal strength values from the surrounding network infrastructure are not suitable for crowdsourcing the radiomap. Moreover, we present an alternative approach, based on signal strength differences, that is far more robust to device variations and maintains the localization accuracy regardless of the number of contributing devices. Keywords—crowdsourcing; localization; device diversity I.
