Energy-efficient aquatic environment monitoring using smartphone-based robots

Yu Wang, Rui Tan, Guoliang Xing, Jianxun Wang, Xiaobo Tan, Xiaoming Liu

Research output: Contribution to journalArticle

Abstract

Monitoring aquatic environment is of great interest to the ecosystem, marine life, and human health. This article presents the design and implementation of Samba-an aquatic surveillance robot that integrates an off-the-shelf Android smartphone and a robotic fish to monitor harmful aquatic processes such as oil spills and harmful algal blooms. Using the built-in camera of the smartphone, Samba can detect spatially dispersed aquatic processes in dynamic and complex environment. To reduce the excessive false alarms caused by the nonwater area (e.g., trees on the shore), Samba segments the captured images and performs target detection in the identified water area only. However, a major challenge in the design of Samba is the high energy consumption resulted from continuous image segmentation. We propose a novel approach that leverages the power-efficient inertial sensors on smartphones to assist image processing. In particular, based on the learned mapping models between inertial and visual features, Samba uses real-time inertial sensor readings to estimate the visual features that guide image segmentation, significantly reducing the energy consumption and computation overhead. Samba also features a set of lightweight and robust computer vision algorithms, which detect harmful aquatic processes based on their distinctive color features. Last, Samba employs a feedback-based rotation control algorithm to adapt to spatiotemporal development of the target aquatic process. We have implemented a Samba prototype and evaluated it through extensive field experiments, lab experiments, and trace-driven simulations. The results show that Samba can achieve a 94% detection rate, a 5% false alarm rate, and a lifetime up to nearly 2 months.

LanguageEnglish (US)
Article number25
JournalACM Transactions on Sensor Networks
Volume12
Issue number3
DOIs
StatePublished - Jul 1 2016

Profile

Smartphones
Robots
Image segmentation
Monitoring
Energy utilization
Aquatic ecosystems
Sensors
Oil spills
Target tracking
Fish
Computer vision
Robotics
Image processing
Experiments
Cameras
Health
Color
Feedback
Water

Keywords

  • Computer vision
  • Inertial sensing
  • Object detection
  • Robotic sensor
  • Smartphone

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Energy-efficient aquatic environment monitoring using smartphone-based robots. / Wang, Yu; Tan, Rui; Xing, Guoliang; Wang, Jianxun; Tan, Xiaobo; Liu, Xiaoming.

In: ACM Transactions on Sensor Networks, Vol. 12, No. 3, 25, 01.07.2016.

Research output: Contribution to journalArticle

@article{22afd1187e89478eabba2e1fb6616214,
title = "Energy-efficient aquatic environment monitoring using smartphone-based robots",
abstract = "Monitoring aquatic environment is of great interest to the ecosystem, marine life, and human health. This article presents the design and implementation of Samba-an aquatic surveillance robot that integrates an off-the-shelf Android smartphone and a robotic fish to monitor harmful aquatic processes such as oil spills and harmful algal blooms. Using the built-in camera of the smartphone, Samba can detect spatially dispersed aquatic processes in dynamic and complex environment. To reduce the excessive false alarms caused by the nonwater area (e.g., trees on the shore), Samba segments the captured images and performs target detection in the identified water area only. However, a major challenge in the design of Samba is the high energy consumption resulted from continuous image segmentation. We propose a novel approach that leverages the power-efficient inertial sensors on smartphones to assist image processing. In particular, based on the learned mapping models between inertial and visual features, Samba uses real-time inertial sensor readings to estimate the visual features that guide image segmentation, significantly reducing the energy consumption and computation overhead. Samba also features a set of lightweight and robust computer vision algorithms, which detect harmful aquatic processes based on their distinctive color features. Last, Samba employs a feedback-based rotation control algorithm to adapt to spatiotemporal development of the target aquatic process. We have implemented a Samba prototype and evaluated it through extensive field experiments, lab experiments, and trace-driven simulations. The results show that Samba can achieve a 94{\%} detection rate, a 5{\%} false alarm rate, and a lifetime up to nearly 2 months.",
keywords = "Computer vision, Inertial sensing, Object detection, Robotic sensor, Smartphone",
author = "Yu Wang and Rui Tan and Guoliang Xing and Jianxun Wang and Xiaobo Tan and Xiaoming Liu",
year = "2016",
month = "7",
day = "1",
doi = "10.1145/2932190",
language = "English (US)",
volume = "12",
journal = "ACM Transactions on Sensor Networks",
issn = "1550-4859",
publisher = "Association for Computing Machinery (ACM)",
number = "3",

}

TY - JOUR

T1 - Energy-efficient aquatic environment monitoring using smartphone-based robots

AU - Wang,Yu

AU - Tan,Rui

AU - Xing,Guoliang

AU - Wang,Jianxun

AU - Tan,Xiaobo

AU - Liu,Xiaoming

PY - 2016/7/1

Y1 - 2016/7/1

N2 - Monitoring aquatic environment is of great interest to the ecosystem, marine life, and human health. This article presents the design and implementation of Samba-an aquatic surveillance robot that integrates an off-the-shelf Android smartphone and a robotic fish to monitor harmful aquatic processes such as oil spills and harmful algal blooms. Using the built-in camera of the smartphone, Samba can detect spatially dispersed aquatic processes in dynamic and complex environment. To reduce the excessive false alarms caused by the nonwater area (e.g., trees on the shore), Samba segments the captured images and performs target detection in the identified water area only. However, a major challenge in the design of Samba is the high energy consumption resulted from continuous image segmentation. We propose a novel approach that leverages the power-efficient inertial sensors on smartphones to assist image processing. In particular, based on the learned mapping models between inertial and visual features, Samba uses real-time inertial sensor readings to estimate the visual features that guide image segmentation, significantly reducing the energy consumption and computation overhead. Samba also features a set of lightweight and robust computer vision algorithms, which detect harmful aquatic processes based on their distinctive color features. Last, Samba employs a feedback-based rotation control algorithm to adapt to spatiotemporal development of the target aquatic process. We have implemented a Samba prototype and evaluated it through extensive field experiments, lab experiments, and trace-driven simulations. The results show that Samba can achieve a 94% detection rate, a 5% false alarm rate, and a lifetime up to nearly 2 months.

AB - Monitoring aquatic environment is of great interest to the ecosystem, marine life, and human health. This article presents the design and implementation of Samba-an aquatic surveillance robot that integrates an off-the-shelf Android smartphone and a robotic fish to monitor harmful aquatic processes such as oil spills and harmful algal blooms. Using the built-in camera of the smartphone, Samba can detect spatially dispersed aquatic processes in dynamic and complex environment. To reduce the excessive false alarms caused by the nonwater area (e.g., trees on the shore), Samba segments the captured images and performs target detection in the identified water area only. However, a major challenge in the design of Samba is the high energy consumption resulted from continuous image segmentation. We propose a novel approach that leverages the power-efficient inertial sensors on smartphones to assist image processing. In particular, based on the learned mapping models between inertial and visual features, Samba uses real-time inertial sensor readings to estimate the visual features that guide image segmentation, significantly reducing the energy consumption and computation overhead. Samba also features a set of lightweight and robust computer vision algorithms, which detect harmful aquatic processes based on their distinctive color features. Last, Samba employs a feedback-based rotation control algorithm to adapt to spatiotemporal development of the target aquatic process. We have implemented a Samba prototype and evaluated it through extensive field experiments, lab experiments, and trace-driven simulations. The results show that Samba can achieve a 94% detection rate, a 5% false alarm rate, and a lifetime up to nearly 2 months.

KW - Computer vision

KW - Inertial sensing

KW - Object detection

KW - Robotic sensor

KW - Smartphone

UR - http://www.scopus.com/inward/record.url?scp=84979915841&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84979915841&partnerID=8YFLogxK

U2 - 10.1145/2932190

DO - 10.1145/2932190

M3 - Article

VL - 12

JO - ACM Transactions on Sensor Networks

T2 - ACM Transactions on Sensor Networks

JF - ACM Transactions on Sensor Networks

SN - 1550-4859

IS - 3

M1 - 25

ER -