Samba

Abstract

Monitoring aquatic environment is of great interest to the ecosystem, marine life, and human health. This paper 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 spill and harmful algal blooms. Using the built-in camera of on-board smartphone, Samba can detect spatially dispersed aquatic processes in dynamic and complex environments. To reduce the excessive false alarms caused by the non-water 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 the continuous image segmentation. We propose a novel approach that leverages the power-efficient inertial sensors on smartphone to assist the 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 the image segmentation, significantly reducing 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. Lastly, Samba employs a feedback-based rotation control algorithm to adapt to spatiotemporal evolution 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 94% detection rate, 5% false alarm rate, and a lifetime up to nearly two months.