MultiSense: proportional-share for mechanically steerable sensor networks

Steerable sensors, such as pan-tilt-zoom cameras and weather radars, expose programmable actuators to applications, which steer them to dictate the type, quality, and quantity of data they collect. Applications with different goals steer these sensors in different directions. Although being expensive to deploy and maintain, existing steerable sensor networks allow only a single application to control them due to the slow speed of their mechanical actuators. To address the problem, we design MultiSense to enable fine-grained multiplexing by (1) exposing a virtual sensor to each application and (2) optimizing the time to context-switch between virtual sensors and satisfy requests. We implement MultiSense in Xen, a widely used virtualization platform, and explore how well proportional-share scheduling, along with extensions for state restoration, request batching and merging, and anticipatory scheduling, satisfies the unique requirements of steerable sensors. We present experiments for pan-tilt-zoom cameras and weather radars that show MultiSense efficiently isolates the performance of virtual sensors, allowing concurrent applications to satisfy conflicting goals. As one example, we enable a tracking application to photograph an object moving at nearly 3 mph every 23 ft along its trajectory at a distance of 300 ft, while supporting a security application that photographs a fixed point every 3 s.

[1]  Kim Binsted,et al.  The Lowell Telescope Scheduler: A System to Provide Non-Professional Access to Large Automatic Telescopes , 2005, IMSA.

[2]  Alberto Del Bimbo,et al.  Uncalibrated Framework for On-line Camera Cooperation to Acquire Human Head Imagery in Wide Areas , 2008, 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance.

[3]  Demetri Terzopoulos,et al.  Surveillance camera scheduling: a virtual vision approach , 2005, Multimedia Systems.

[4]  Andrew Warfield,et al.  Facilitating the Development of Soft Devices , 2005, USENIX Annual Technical Conference, General Track.

[5]  Jorge L. Salazar,et al.  Dual-polarization performance of the phase-tilt antenna array in a casa dense network radar , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[6]  Brian N. Bershad,et al.  Recovering device drivers , 2004, TOCS.

[7]  Michael Zink,et al.  Closed-loop architecture for distributed collaborative adaptive sensing of the atmosphere: meteorological command and control , 2010, Int. J. Sens. Networks.

[8]  Amit K. Roy-Chowdhury,et al.  Distributed Camera Networks , 2011, IEEE Signal Processing Magazine.

[9]  Sanjit K. Mitra,et al.  Sampling Rate Conversion in the Frequency Domain [DSP Tips and Tricks] , 2011, IEEE Signal Processing Magazine.

[10]  V. Chandrasekar,et al.  Short wavelength technology and the potential for distributed networks of small radar systems , 2009, 2009 IEEE Radar Conference.

[11]  Philip Levis,et al.  Maté: a tiny virtual machine for sensor networks , 2002, ASPLOS X.

[12]  Chenyang Lu,et al.  Integrating concurrency control and energy management in device drivers , 2007, SOSP.

[13]  V. Chandrasekar,et al.  Development of Scan Strategy for Dual Doppler Retrieval in a Networked Radar System , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[14]  Md. Yusuf Sarwar Uddin,et al.  Virtual Battery: An Energy Reserve Abstraction for Embedded Sensor Networks , 2008, 2008 Real-Time Systems Symposium.

[15]  Thomas Nelson The device driver as state machine , 1992 .

[16]  Prashant J. Shenoy,et al.  MultiSense: fine-grained multiplexing for steerable camera sensor networks , 2011, MMSys.

[17]  Prashant J. Shenoy,et al.  Cello: A Disk Scheduling Framework for Next Generation Operating Systems* , 1998, SIGMETRICS '98/PERFORMANCE '98.

[18]  Michael B. Jones,et al.  CPU reservations and time constraints: efficient, predictable scheduling of independent activities , 1997, SOSP.

[19]  Harrick M. Vin,et al.  Start-time fair queueing: a scheduling algorithm for integrated services packet switching networks , 1996, SIGCOMM '96.

[20]  Tarek F. Abdelzaher,et al.  The LiteOS Operating System: Towards Unix-Like Abstractions for Wireless Sensor Networks , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).

[21]  Peter A. Dinda,et al.  Towards Virtual Passthrough I/O on Commodity Devices , 2008, Workshop on I/O Virtualization.

[22]  Matt Welsh,et al.  Resource aware programming in the Pixie OS , 2008, SenSys '08.

[23]  Faisal Z. Qureshi,et al.  Learning proactive control strategies for PTZ cameras , 2011, 2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras.

[24]  Karsten Schwan,et al.  VMedia: enhanced multimedia services in virtualized systems , 2008, Electronic Imaging.

[25]  Demetri Terzopoulos,et al.  Planning ahead for PTZ camera assignment and handoff , 2009, 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC).

[26]  Banu Özden,et al.  Disk scheduling with quality of service guarantees , 1999, Proceedings IEEE International Conference on Multimedia Computing and Systems.

[27]  Arun Venkataramani,et al.  Multi-user data sharing in radar sensor networks , 2007, SenSys '07.

[28]  Peter Druschel,et al.  Anticipatory scheduling: a disk scheduling framework to overcome deceptive idleness in synchronous I/O , 2001, SOSP.

[29]  Bernhard Rinner,et al.  Video Analysis in Pan-Tilt-Zoom Camera Networks , 2010, IEEE Signal Processing Magazine.