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Earthquake Early Warning and Beyond: Systems Challenges in Smartphone-based Seismic Network

Qingkai Kong, Qin Lv, Richard M. Allen

TL;DR

The paper addresses delivering real-time Earthquake Early Warning (EEW) in regions lacking dense traditional sensor networks by leveraging a smartphone-based seismic network (MyShake). It outlines MyShake’s deployment, architecture, and data collection, and identifies two real-world challenges: sensing hardware diversity and dynamic user mobility, plus issues of scalability, latency, and security. It proposes sensing-quality metrics and a simulation platform to evaluate adaptive detection strategies under heterogeneous devices and mobility patterns. The work highlights potential for global EEW via consumer devices and discusses broader hazard-preparedness and emergency-response applications.

Abstract

Earthquake Early Warning (EEW) systems can effectively reduce fatalities, injuries, and damages caused by earthquakes. Current EEW systems are mostly based on traditional seismic and geodetic networks, and exist only in a few countries due to the high cost of installing and maintaining such systems. The MyShake system takes a different approach and turns people's smartphones into portable seismic sensors to detect earthquake-like motions. However, to issue EEW messages with high accuracy and low latency in the real world, we need to address a number of challenges related to mobile computing. In this paper, we first summarize our experience building and deploying the MyShake system, then focus on two key challenges for smartphone-based EEW (sensing heterogeneity and user/system dynamics) and some preliminary exploration. We also discuss other challenges and new research directions associated with smartphone-based seismic network.

Earthquake Early Warning and Beyond: Systems Challenges in Smartphone-based Seismic Network

TL;DR

The paper addresses delivering real-time Earthquake Early Warning (EEW) in regions lacking dense traditional sensor networks by leveraging a smartphone-based seismic network (MyShake). It outlines MyShake’s deployment, architecture, and data collection, and identifies two real-world challenges: sensing hardware diversity and dynamic user mobility, plus issues of scalability, latency, and security. It proposes sensing-quality metrics and a simulation platform to evaluate adaptive detection strategies under heterogeneous devices and mobility patterns. The work highlights potential for global EEW via consumer devices and discusses broader hazard-preparedness and emergency-response applications.

Abstract

Earthquake Early Warning (EEW) systems can effectively reduce fatalities, injuries, and damages caused by earthquakes. Current EEW systems are mostly based on traditional seismic and geodetic networks, and exist only in a few countries due to the high cost of installing and maintaining such systems. The MyShake system takes a different approach and turns people's smartphones into portable seismic sensors to detect earthquake-like motions. However, to issue EEW messages with high accuracy and low latency in the real world, we need to address a number of challenges related to mobile computing. In this paper, we first summarize our experience building and deploying the MyShake system, then focus on two key challenges for smartphone-based EEW (sensing heterogeneity and user/system dynamics) and some preliminary exploration. We also discuss other challenges and new research directions associated with smartphone-based seismic network.

Paper Structure

This paper contains 13 sections, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. MyShake System
  3. MyShake Current Status
  4. MyShake System Design
  5. EEW Challenges for MyShake
  6. Diversity of Sensing Hardware
  7. Dynamics of Users and System
  8. Other Challenges
  9. Tackling the EEW Challenges
  10. Tackling Diversity of Sensing Hardware
  11. Tackling Dynamics of Users and Systems
  12. Related Work
  13. Conclusions and Future Directions

Figures (8)

  • Figure 1: MyShake global user distribution. Brighter color indicates higher user density. Data used here are from all registered users with locations available during the period of 2016-02-12 to 2018-08-12.
  • Figure 2: An overview of the MyShake system.
  • Figure 3: Top 5 accelerometer types in MyShake users' phones. Data are from 276,140 users.
  • Figure 4: Distribution of MyShake phones' time offset based on 1 million randomly sampled NTP records using hourly synchronization.
  • Figure 5: MyShake user distribution (sampled every 2 hours) in the San Francisco Bay Area. (Left) During the day from 7am to 12pm; (Right) During the night from 12am to 5am. Data used here are from 2017-07-01 to 2018-07-01.
  • ...and 3 more figures