In this paper, we propose Polaris, a novel approach to provide reliable orientation information for mobile devices in indoor environments. Polaris achieves this by aggregating pictures of the ceiling of an indoor environment and applies computer vision based pat- tern matching techniques to utilize them as orientation references for correcting digital compass readings. To show the feasibility of the Polaris system, we implemented the Polaris system on mobile devices, and field tested the system in multiple office buildings. Our results show that Polaris achieves 4.5-degree average orientation ac- curacy, which is about 3.5 times better than what can be achieved through sole use of raw digital compass readings.

This paper presents the PANDAA system, a zero-configuration spatial localization system for networked devices based on ambient sound sensing. After initial placement of the devices, ambient sounds, such as human speech, music, footsteps, finger snaps, hand claps, or coughs and sneezes, are used to autonomously resolve the spatial relative arrangement of devices using trigonometric bounds and successive approximation. Using only time difference of arrival measurements as a bound for successive estimations, PANDAA is able to achieve an average of 0.17 meter accuracy for device location in the meeting room deployment.

In this paper, we present SensorFly, a controlled-mobile aerial sensor network platform for indoor emergency response application. The miniature, low-cost sensor platform has capabilities to self deploy, achieve 3-D sensing, and adapt to node and network disruptions in harsh environments. We describe hardware design trade-offs, the software architecture, and the implementation that enables limited-capability nodes to collectively achieve application goals. Through the indoor fire monitoring application scenario we validate that the platform can achieve coverage and sensing accuracy that matches or exceeds static sensor networks and provide higher adaptability and autonomy.