CITRIC: A low-bandwidth wireless camera network platform

In this paper, we propose and demonstrate a novel wireless camera network system, called CITRIC. The core component of this system is a new hardware platform that integrates a camera, a frequency-scalable (up to 624 MHz) CPU, 16MB FLASH, and 64MB RAM onto a single device. The device then connects with a standard sensor network mote to form a camera mote. The design enables in-network processing of images to reduce communication requirements, which has traditionally been high in existing camera networks with centralized processing. We also propose a back-end client/server architecture to provide a user interface to the system and support further centralized processing for higher-level applications. Our camera mote enables a wider variety of distributed pattern recognition applications than traditional platforms because it provides more computing power and tighter integration of physical components while still consuming relatively little power. Furthermore, the mote easily integrates with existing low-bandwidth sensor networks because it can communicate over the IEEE 802.15.4 protocol with other sensor network platforms. We demonstrate our system on three applications: image compression, target tracking, and camera localization.

[1]  Pai H. Chou,et al.  eCAM: ultra compact, high data-rate wireless sensor node with a miniature camera , 2006, SenSys '06.

[2]  Andreas Willig,et al.  Wireless Technology in Industrial Networks , 2005, Proceedings of the IEEE.

[3]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[4]  Richard P. Kleihorst,et al.  Camera mote with a high-performance parallel processor for real-time frame-based video processing , 2007, AVSS.

[5]  Prashant J. Shenoy,et al.  SensEye: a multi-tier camera sensor network , 2005, MULTIMEDIA '05.

[6]  Songhwai Oh,et al.  Markov chain Monte Carlo data association for general multiple-target tracking problems , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[7]  Gary Bradski,et al.  Learning-Based Computer Vision with Intels Open Source Computer Vision Library , 2005 .

[8]  Michael Cw Kintner-Meyer.  Opportunities of Wireless Sensors and Controls for Building Operation , 2005 .

[9]  Michael Elad,et al.  Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Kentaro Toyama,et al.  Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[11]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[12]  Eugenio Culurciello,et al.  A Lightweight Camera Sensor Network Operating on Symbolic Information , 2006 .

[13]  Hamid Aghajan,et al.  Development of a Mote for Wireless Image Sensor Networks , 2006 .

[14]  Richard P. Kleihorst,et al.  Camera Mote with a High-Performance Parallel Processor for Real-Time Frame-Based Video Processing , 2007, 2007 First ACM/IEEE International Conference on Distributed Smart Cameras.

[15]  S. Shankar Sastry,et al.  Tracking and Coordination of Multiple Agents Using Sensor Networks: System Design, Algorithms and Experiments , 2007, Proceedings of the IEEE.

[16]  Deborah Estrin,et al.  Habitat monitoring with sensor networks , 2004, CACM.

[17]  E.J. Candes.  Compressive Sampling , 2022 .

[18]  Hamid Gharavi,et al.  Special issue on sensor networks and applications , 2003 .

[19]  Gaurav S. Sukhatme,et al.  Connecting the Physical World with Pervasive Networks , 2002, IEEE Pervasive Comput..

[20]  Justin Romberg,et al.  Practical Signal Recovery from Random Projections , 2005 .

[21]  David E. Culler,et al.  Telos: enabling ultra-low power wireless research , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[22]  Matthew Lease,et al.  Making Sensor Networks Practical with Robots , 2002, Pervasive.

[23]  S. Shankar Sastry,et al.  Automatic Camera Network Localization using Object Image Tracks , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[24]  Deborah Estrin,et al.  Cyclops: in situ image sensing and interpretation in wireless sensor networks , 2005, SenSys '05.

[25]  Wu-chi Feng,et al.  Panoptes: scalable low-power video sensor networking technologies , 2003, ACM Multimedia.