Efficient real-time trajectory tracking

Efficient real-time trajectory tracking Moving objects databases (MOD) manage trajectory information of vehicles, animals, and other mobile objects. A crucial problem is how to efficiently track an object’s trajectory in real-time, in particular if the trajectory data is sensed at the mobile object and thus has to be communicated over a wireless network. We propose a family of tracking protocols that allow trading the communication cost and the amount of trajectory data stored at a MOD off against the spatial accuracy. With each of these protocols, the MOD manages a simplified trajectory that does not deviate by more than a certain accuracy bound from the actual movement. Moreover, the different protocols enable several trade-offs between computational costs, communication cost, and the reduction in the trajectory data: Connection-Preserving Dead Reckoning minimizes the communication cost using dead reckoning, a technique originally designed for tracking an object’s current position. Generic Remote Trajectory Simplification (GRTS) further separates between tracking of the current position and simplification of the past trajectory and can be realized with different line simplification algorithms. For both protocols, we discuss how to bound the space consumption and computing time at the moving object and thereby present an effective compression technique to optimize the reduction performance of real-time line simplification in general. Our evaluations with hundreds of real GPS traces show that a realization of GRTS with a simple simplification heuristic reaches 85–90% of the best possible reduction rate, given by retrospective offline simplification. A realization with the optimal line simplification algorithm by Imai and Iri even reaches more than 97% of the best possible reduction rate. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Efficient real-time trajectory tracking

Loading next page...
 
/lp/springer_journal/efficient-real-time-trajectory-tracking-19ZgnZhli9
Publisher
Springer-Verlag
Copyright
Copyright © 2011 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-011-0237-7
Publisher site
See Article on Publisher Site

Abstract

Moving objects databases (MOD) manage trajectory information of vehicles, animals, and other mobile objects. A crucial problem is how to efficiently track an object’s trajectory in real-time, in particular if the trajectory data is sensed at the mobile object and thus has to be communicated over a wireless network. We propose a family of tracking protocols that allow trading the communication cost and the amount of trajectory data stored at a MOD off against the spatial accuracy. With each of these protocols, the MOD manages a simplified trajectory that does not deviate by more than a certain accuracy bound from the actual movement. Moreover, the different protocols enable several trade-offs between computational costs, communication cost, and the reduction in the trajectory data: Connection-Preserving Dead Reckoning minimizes the communication cost using dead reckoning, a technique originally designed for tracking an object’s current position. Generic Remote Trajectory Simplification (GRTS) further separates between tracking of the current position and simplification of the past trajectory and can be realized with different line simplification algorithms. For both protocols, we discuss how to bound the space consumption and computing time at the moving object and thereby present an effective compression technique to optimize the reduction performance of real-time line simplification in general. Our evaluations with hundreds of real GPS traces show that a realization of GRTS with a simple simplification heuristic reaches 85–90% of the best possible reduction rate, given by retrospective offline simplification. A realization with the optimal line simplification algorithm by Imai and Iri even reaches more than 97% of the best possible reduction rate.

Journal

The VLDB JournalSpringer Journals

Published: Oct 1, 2011

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off