Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Efficient Streaming Mass Spatio-Temporal Vehicle Data Access in Urban Sensor Networks Based on Apache Storm.

Lianjie Zhou1, Nengcheng Chen2,3, Zeqiang Chen4

  • 1State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Luoyu Road 129, Wuhan 430079, China. zlj0808@whu.edu.cn.

Sensors (Basel, Switzerland)
|April 11, 2017
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Urbanization-induced compression effect accelerates and amplifies dry-wet abrupt alternations in Chinese cities.

Science bulletin·2026
Same author

A self-wrapping, bioresorbable neural interface for wireless multimodal therapy of localized peripheral nerve injury.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Wireless battery-free ultrathin lithium-niobate resonator as wearable and implantable electronics for continuous monitoring of mechanical vital signs.

Nature communications·2025
Same author

An Active, Multimodal Neural Interface for Real-Time Monitoring of Cortical Electrical, Thermal, and Optical Dynamics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

Optimal design of the splicing structure for metal working molds of cube corner retroreflective sheeting.

Applied optics·2025
Same author

Low-cost video-based air quality estimation system using structured deep learning with selective state space modeling.

Environment international·2025

We developed an efficient streaming spatio-temporal data access (ESDAS) system using Apache Storm for real-time vehicle data processing in smart cities. ESDAS significantly outperforms traditional methods like MongoDB, offering a threefold increase in efficiency for mass data access and cleaning.

Area of Science:

  • Computer Science
  • Data Science
  • Urban Computing

Background:

  • Smart cities generate massive amounts of real-time vehicle data, essential for traffic analysis.
  • Existing methods struggle with the scale and speed of streaming vehicle data access due to computational limits.
  • Efficient data access is crucial for understanding and managing urban traffic environments.

Purpose of the Study:

  • To propose an efficient system for real-time streaming spatio-temporal data access and cleaning.
  • To leverage Apache Storm for handling large-scale vehicle data streams.
  • To improve the performance of vehicle data processing in smart city applications.

Main Methods:

  • Developed an efficient streaming spatio-temporal data access (ESDAS) system based on Apache Storm.
Keywords:
Apache StormBeiDou bus networkSensor Observation Servicecloud computingstreaming spatio-temporal mass data access

Related Experiment Videos

  • Designed a Spout/Bolt workflow within Apache Storm, including a specialized 'speeding bolt'.
  • Utilized Taiyuan BeiDou bus location data as a real-world dataset for experiments.

    • Main Results:

    • ESDAS demonstrated effective real-time data access and cleaning capabilities.
    • Experimental results visualized data access and filtered bus aggregations.
    • Performance tests showed ESDAS processed 10,000 records/second in ~300ms, compared to ~1300ms for MongoDB.

    Conclusions:

    • The proposed ESDAS system significantly enhances the efficiency of accessing and cleaning mass streaming vehicle data.
    • ESDAS achieves approximately three times the performance of MongoDB for spatio-temporal data processing.
    • This approach provides a foundation for advanced analysis and utilization of vehicle data in smart cities.