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239
Localization for Mobile Sensor Networks
- Proc. MobiCom
, 2004
"... Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimat ..."
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Cited by 292 (0 self)
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Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions.
Practical robust localization over large-scale 802.11 wireless networks
- in Proceedings of the 10th Annual International Conference on Mobile Computing and Networking (MOBICOM
"... We demonstrate a system built using probabilistic techniques that allows for remarkably accurate localization across our entire office building using nothing more than the built-in signal intensity meter supplied by standard 802.11 cards. While prior systems have required significant investments of ..."
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Cited by 188 (2 self)
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We demonstrate a system built using probabilistic techniques that allows for remarkably accurate localization across our entire office building using nothing more than the built-in signal intensity meter supplied by standard 802.11 cards. While prior systems have required significant investments of human labor to build a detailed signal map, we can train our system by spending less than one minute per office or region, walking around with a laptop and recording the observed signal intensities of our building’s unmodified base stations. We actually collected over two minutes of data per office or region, about 28 man-hours of effort. Using less than half of this data to train the localizer, we can localize a user to the precise, correct location in over 95 % of our attempts, across the entire building. Even in the most pathological cases, we almost never localize a user any more distant than to the neighboring office. A user can obtain this level of accuracy with only two or three signal intensity measurements, allowing for a high frame rate of localization results. Furthermore, with a brief calibration period, our system can be adapted to work with previously unknown user hardware. We present results demonstrating the robustness of our system against a variety of untrained time-varying phenomena, including the presence or absence of people in the building across the day. Our system is sufficiently robust to enable a variety of locationaware applications without requiring special-purpose hardware or complicated training and calibration procedures.
WLAN Location Determination via Clustering and Probability Distributions
- In IEEE PerCom 2003
, 2003
"... We present a WLAN location determination technique, the Joint Clustering technique, that uses (1) signal strength probability distributions to address the noisy wireless channel, and (2) clustering of locations to reduce the computational cost of searching the radio map. The Joint Clustering techniq ..."
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Cited by 171 (6 self)
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We present a WLAN location determination technique, the Joint Clustering technique, that uses (1) signal strength probability distributions to address the noisy wireless channel, and (2) clustering of locations to reduce the computational cost of searching the radio map. The Joint Clustering technique reduces computational cost by more than an order of magnitude, compared to the current state of the art techniques, allowing non-centralized implementation on mobile clients. Results from 802.11-equipped iPAQ implementations show that the new technique gives user location to within 7 feet with over 90% accuracy.
Accuracy characterization for metropolitan-scale wi-fi localization
- In Proceedings of Mobisys 2005
, 2005
"... Location systems have long been identified as an important component of emerging mobile applications. Most research on location systems has focused on precise location in indoor environments. However, many location applications (for example, location-aware web search) become interesting only when th ..."
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Cited by 139 (6 self)
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Location systems have long been identified as an important component of emerging mobile applications. Most research on location systems has focused on precise location in indoor environments. However, many location applications (for example, location-aware web search) become interesting only when the underlying location system is available ubiquitously and is not limited to a single office environment. Unfortunately, the installation and calibration overhead involved for most of the existing research systems is too prohibitive to imagine deploying them across, say, an entire city. In this work, we evaluate the feasibility of building a wide-area 802.11 Wi-Fi-based positioning system. We compare a suite of wireless-radio-based positioning algorithms to understand how they can be adapted for such ubiquitous deployment with minimal calibration. In particular, we study the impact of this limited calibration on the accuracy of the positioning algorithms. Our experiments show that we can estimate a user’s position with a median positioning error of 13–40 meters (depending upon the characteristics of the environment). Although this accuracy is lower than existing positioning systems, it requires substantially lower calibration overhead and provides easy deployment and coverage across large metropolitan areas. 1
Enhancing location privacy in wireless LAN through disposable interface identifiers: a quantitative analysis
, 2003
"... The recent proliferation of wireless local area networks (WLAN) has introduced new location privacy risks. An adversary controlling several access points could triangulate a client’s position. In addition, interface identifiers uniquely identify each client, allowing tracking of location over time. ..."
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Cited by 127 (4 self)
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The recent proliferation of wireless local area networks (WLAN) has introduced new location privacy risks. An adversary controlling several access points could triangulate a client’s position. In addition, interface identifiers uniquely identify each client, allowing tracking of location over time. We enhance location privacy through frequent disposal of a client’s interface identifier. The described system curbs the adversary’s ability to continuously track a client’s position. Design challenges include selecting new interface identifiers, detecting address collisions at the MAC layer, and timing identifier switches to balance network disruptions against privacy protection. Using a modified authentication protocol, network operators can still control access to their network. An analysis of a public WLAN usage trace shows that disposing addresses before reassociation already yields significant privacy improvements.
Localization of Wireless Sensor Networks with a Mobile Beacon
, 2003
"... Wireless sensor networks have the potential to become the pervasive sensing (and actuating) technology of the future. For many applications, a large number of inexpensive sensors is preferable to a few expensive ones. The large number of sensors in a sensor network and most application scenarios ..."
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Cited by 114 (2 self)
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Wireless sensor networks have the potential to become the pervasive sensing (and actuating) technology of the future. For many applications, a large number of inexpensive sensors is preferable to a few expensive ones. The large number of sensors in a sensor network and most application scenarios preclude hand placement of the sensors. Determining the physical location of the sensors after they have been deployed is known as the problem of localization. In this paper, we present a localization technique based on a single mobile beacon aware of its position (e.g. by being equipped with a GPS receiver). Sensor nodes
Towards Mobility as a Network Control Primitive
- In MobiHoc ’04: Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing
, 2004
"... In the near future, the advent of large-scale networks of mobile agents autonomously performing long-term sensing and communication tasks will be upon us. However, using controlled node mobility to improve communication performance is a capability that the mobile networking community has not yet inv ..."
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Cited by 108 (1 self)
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In the near future, the advent of large-scale networks of mobile agents autonomously performing long-term sensing and communication tasks will be upon us. However, using controlled node mobility to improve communication performance is a capability that the mobile networking community has not yet investigated. In this paper, we study mobility as a network control primitive. More specifically, we present the first mobility control scheme for improving communication performance in such networks. Our scheme is completely distributed, requiring each node to possess only local information. Our scheme is self-adaptive, being able to transparently encompass several modes of operation, each respectively improving power efficiency for one unicast flow, multiple unicast flows, and many-to-one concast flows. We provide extensive evaluations on the feasibility of mobility control, showing that controlled mobility can improve network performance in many scenarios. This work constitutes a novel application of distributed control to networking in which underlying network communication serves as input to local control rules that guide the system toward a global objective.
Modeling of Indoor Positioning Systems Based on Location Fingerprinting
, 2004
"... In recent years, positioning systems for indoor areas using the existing wireless local area network infrastructure have been suggested. Such systems make use of location fingerprinting rather than time or direction of arrival techniques for determining the location of mobile stations. While experim ..."
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Cited by 99 (1 self)
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In recent years, positioning systems for indoor areas using the existing wireless local area network infrastructure have been suggested. Such systems make use of location fingerprinting rather than time or direction of arrival techniques for determining the location of mobile stations. While experimental results related to such positioning systems have been presented, there is a lack of analytical models that can be used as a framework for designing and deploying the positioning systems. In this paper, we present an analytical model for analyzing such positioning systems. We develop the framework for analyzing a simple positioning system that employs the Euclidean distance between a sample signal vector and the location fingerprints of an area stored in a database. We analyze the effect of the number of access points that are visible and radio propagation parameters on the performance of the positioning system and provide some preliminary guidelines on its design.
Bayesian indoor positioning systems
- In Infocom
, 2005
"... Abstract — In this paper, we introduce a new approach to location estimation where, instead of locating a single client, we simultaneously locate a set of wireless clients. We present a Bayesian hierarchical model for indoor location estimation in wireless networks. We demonstrate that our model ach ..."
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Cited by 99 (15 self)
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Abstract — In this paper, we introduce a new approach to location estimation where, instead of locating a single client, we simultaneously locate a set of wireless clients. We present a Bayesian hierarchical model for indoor location estimation in wireless networks. We demonstrate that our model achieves accuracy that is similar to other published models and algorithms. By harnessing prior knowledge, our model eliminates the requirement for training data as compared with existing approaches, thereby introducing the notion of a fully adaptive zero profiling approach to location estimation. Index Terms — Experimentation with real networks/Testbed, Statistics, WLAN, localization,
