A Pervasive Network Control Algorithm for Multicamera Networks

Owing to the increasingly large volume and complexity of captured videos, renewable energy systems based on solar energy are of particular interest in the design of energy harvesting (EH) wireless visual sensor networks (WVSNs). Since additional energy consumption following image capture occurs owing to image processing, mote operation, data transmission, and reception, the capture rate significantly affects the lifetime of a node. To this end, we explore a novel energy-efficient framework for EH-WVSN design by developing an optimal algorithm named capture rate and pervasive network control for multicamera networks where the quality of service is maximized by obtaining optimal values for the capture rate, allocated energy, and transmit power, based on field of view-based networking in the presence of event and power acquisition patterns. Through simulations, we demonstrate the feasibility of EH-WVSNs in terms of energy consumption, energy allocation, and capture rate in a realistic scenario (parking surveillance).

[1]  Sinem Coleri Ergen,et al.  ZigBee/IEEE 802.15.4 Summary , 2004 .

[2]  Xiaodong Wang,et al.  Distributed Joint Routing and Medium Access Control for Lifetime Maximization of Wireless Sensor Networks , 2007, IEEE Transactions on Wireless Communications.

[3]  Amit K. Roy-Chowdhury,et al.  Distributed multi-target tracking in a self-configuring camera network , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Wendi B. Heinzelman,et al.  A Survey of Visual Sensor Networks , 2009, Adv. Multim..

[5]  Aníbal Ollero,et al.  Vision-based multi-UAV position estimation , 2006, IEEE Robotics & Automation Magazine.

[6]  Michael W. Marcellin,et al.  A method for coordinating the distributed transmission of imagery , 2006, IEEE Transactions on Image Processing.

[7]  S. Shankar Sastry,et al.  An Invitation to 3-D Vision: From Images to Geometric Models , 2003 .

[8]  Vijay Raghunathan,et al.  Design and Power Management of Energy Harvesting Embedded Systems , 2006, ISLPED'06 Proceedings of the 2006 International Symposium on Low Power Electronics and Design.

[9]  Prashant J. Shenoy,et al.  SensEye: a multi-tier camera sensor network , 2005, ACM Multimedia.

[10]  Philip S. Yu,et al.  ViCo: an adaptive distributed video correlation system , 2006, MM '06.

[11]  S. Shankar Sastry,et al.  Pursuit-evasion games with unmanned ground and aerial vehicles , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[12]  Ian F. Akyildiz,et al.  A survey on wireless multimedia sensor networks , 2007, Comput. Networks.

[13]  Amit K. Roy-Chowdhury,et al.  Tracking and Activity Recognition Through Consensus in Distributed Camera Networks , 2010, IEEE Transactions on Image Processing.

[14]  David E. Culler,et al.  Perpetual environmentally powered sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[15]  Ian F. Akyildiz,et al.  Wireless Multimedia Sensor Networks: Applications and Testbeds , 2008, Proceedings of the IEEE.

[16]  Martin Reisslein,et al.  A survey of multimedia streaming in wireless sensor networks , 2008, IEEE Communications Surveys & Tutorials.

[17]  Zoran Zivkovic,et al.  Smart Cameras for Wireless Camera Networks: Architecture Overview , 2009, Multi-Camera Networks.

[18]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[19]  Janusz Konrad,et al.  A Wireless Video Sensor Network for Autonomous Coastal Sensing , 2007 .

[20]  Demetri Terzopoulos,et al.  Distributed Coalition Formation in Visual Sensor Networks: A Virtual Vision Approach , 2007, DCOSS.

[21]  Mani B. Srivastava,et al.  Power management in energy harvesting sensor networks , 2007, TECS.

[22]  Bruce H. Krogh,et al.  Energy-efficient surveillance system using wireless sensor networks , 2004, MobiSys '04.

[23]  Wu-chi Feng,et al.  Panoptes: A Scalable Architecture for Video Sensor Networking Applications , 2004 .

[24]  Mani B. Srivastava,et al.  Heliomote: enabling long-lived sensor networks through solar energy harvesting , 2005, SenSys '05.

[25]  Edmund Y. Lam,et al.  Efficient On-Demand Image Transmission in Visual Sensor Networks , 2007, EURASIP J. Adv. Signal Process..

[26]  Richard P. Kleihorst,et al.  Xetal-II: A Low-Power Massively-Parallel Processor for Video Scene Analysis , 2011, J. Signal Process. Syst..

[27]  Andrea J. Goldsmith,et al.  Cross-Layer Design for Lifetime Maximization in Interference-Limited Wireless Sensor Networks , 2006, IEEE Transactions on Wireless Communications.