Bayesian Landmark Learning for Mobile Robot Localization
Machine Learning
Range-free localization schemes for large scale sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
Localization for mobile sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Hardware design experiences in ZebraNet
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Node Localization Using Mobile Robots in Delay-Tolerant Sensor Networks
IEEE Transactions on Mobile Computing
A kernel-based learning approach to ad hoc sensor network localization
ACM Transactions on Sensor Networks (TOSN)
Erasure-coding based routing for opportunistic networks
Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking
CarTel: a distributed mobile sensor computing system
Proceedings of the 4th international conference on Embedded networked sensor systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
WICON '06 Proceedings of the 2nd annual international workshop on Wireless internet
IEEE Pervasive Computing
Organizing a global coordinate system from local information on an ad hoc sensor network
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Range-Based localization in mobile sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
Techniques for Improving Opportunistic Sensor Networking Performance
DCOSS '08 Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems
CenceMe: injecting sensing presence into social networking applications
EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
IODetector: a generic service for indoor outdoor detection
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
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There is a growing need to support localization in low-power mobile sensor networks, both indoors and outdoors, when mobile sensor nodes (e.g., mote class) are incapable of independently estimating their location (e.g., when GPS is inappropriate or too costly), or are unable to leverage localization schemes designed for static sensor networks. To address this challenge, we propose ambient beacon localization (ABL), an unconventional approach that allows mobile sensors to localize by exploiting their ambient physical environment. Ambient beacon localization combines machine learning and free range beacon-based techniques to bind distinct characteristics of the physical world that appear in sensor data of known locations, which we call ambient beacon points (ABPs). Supervised learning algorithms are used to allow mobile sensors to recognize ABPs, i.e., those physical locations that are sufficiently distinguishable in terms of sensed data from the rest of the sensor field. Ambient beacon localization leverages the very same sensed data that nodes are already collecting on behalf of applications. When a mobile sensor finds itself at an ambient beacon point it starts to beacon that location so that other nodes in range of an ambient beacon can localize themselves, for example, by applying existing beacon based localization schemes. In this paper, we present the design of ambient beacon localization and its initial evaluation in a building-sized testbed. Our work is at an early stage but our experimental testbed and simulation results demonstrate that this unusual approach to localization shows promise.