Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Near-optimal sensor placements: maximizing information while minimizing communication cost
Proceedings of the 5th international conference on Information processing in sensor networks
Truth discovery with multiple conflicting information providers on the web
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Sensor Selection for Minimizing Worst-Case Prediction Error
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
The Journal of Machine Learning Research
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
VTrack: accurate, energy-aware road traffic delay estimation using mobile phones
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Hidden Markov map matching through noise and sparseness
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Knowing what to believe (when you already know something)
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
EWSN'11 Proceedings of the 8th European conference on Wireless sensor networks
EasyTracker: automatic transit tracking, mapping, and arrival time prediction using smartphones
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
On truth discovery in social sensing: a maximum likelihood estimation approach
Proceedings of the 11th international conference on Information Processing in Sensor Networks
| Hi-index | 0.00 |
As industrial and academic communities become increasingly interested in Indoor Positioning Systems (IPSs), a plethora of technologies are gaining maturity and competing for adoption in the global smartphone market. In the near future, we expect busy places, such as schools, airports, hospitals and large businesses, to be outfitted with multiple IPS infrastructures, which need to coexist, collaborate and / or compete for users. In this paper, we examine the novel problem of estimating the accuracy of co-located positioning systems, and selecting which one to use where. This is challenging because 1) we do not possess knowledge of the ground truth, which makes it difficult to empirically estimate the accuracy of an indoor positioning system; and 2) the accuracy reported by a positioning system is not always a faithful representation of the real accuracy. In order to address these challenges, we model the process of a user moving in an indoor environment as a Hidden Markov Model (HMM), and augment the model to take into account vector (instead of scalar) observations, and prior knowledge about user mobility drawn from personal electronic calendars. We then propose an extension of the Baum-Welch algorithm to learn the parameters of the augmented HMM. The proposed HMM-based approach to learning the accuracy of indoor positioning systems is validated and tested against competing approaches in several real-world indoor settings.