MAC-Aware and Power-Aware Image Aggregation Scheme in Wireless Visual Sensor Networks

Traditional wireless sensor networks (WSNs) transmit the scalar data (e.g., temperature and irradiation) to the sink node. A new wireless visual sensor network (WVSN) that can transmit images data is a more promising solution than the WSN on sensing, detecting, and monitoring the environment to enhance awareness of the cyber, physical, and social contexts of our daily activities. However, the size of image data is much bigger than the scalar data that makes image transmission a challenging issue in battery-limited WVSN. In this paper, we study the energy efficient image aggregation scheme in WVSN. Image aggregation is a possible way to eliminate the redundant portions of the image captured by different data source nodes. Hence, transmission power could be reduced via the image aggregation scheme. However, image aggregation requires image processing that incurs node processing power. Besides the additional energy consumption from node processing, there is another MAC-aware retransmission energy loss from image aggregation. In this paper, we first propose the mathematical model to capture these three factors (image transmission, image processing, and MAC retransmission) in WVSN. Numerical results based on the mathematical model and real WVSN sensor node (i.e., Meerkats node) are performed to optimize the energy consumption tradeoff between image transmission, image processing, and MAC retransmission.

[1]  H. Coxeter,et al.  Introduction to Geometry , 1964, The Mathematical Gazette.

[2]  Zixiang Xiong,et al.  Distributed source coding , 2006, Signal Processing.

[3]  Hong-Hsu Yen.  Optimization-Based Channel Constrained Data Aggregation Routing Algorithms in Multi-Radio Wireless Sensor Networks , 2009, Sensors.

[4]  Jenhui Chen,et al.  MR2RP: The Multi-Rate and Multi-Range Routing Protocol for IEEE 802.11 Ad Hoc Wireless Networks , 2003, Wirel. Networks.

[5]  Shu-Ping Lin,et al.  Delay QoS and MAC Aware Energy-Efficient Data-Aggregation Routing in Wireless Sensor Networks , 2009, Sensors.

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

[7]  Shu-Ping Lin,et al.  A Novel Energy-Efficient MAC Aware Data Aggregation Routing in Wireless Sensor Networks# , 2009, Sensors.

[8]  Roberto Manduchi,et al.  Energy Consumption Tradeoffs in Visual Sensor Networks , 2006 .

[9]  Bernd Girod,et al.  Distributed Video Coding , 2005, Proceedings of the IEEE.

[10]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

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

[12]  Ahmed Khoumsi,et al.  Modeling and Adapting JPEG to the Energy Requirements of VSN , 2008, 2008 Proceedings of 17th International Conference on Computer Communications and Networks.

[13]  Zixiang Xiong,et al.  Distributed source coding for sensor networks , 2004, IEEE Signal Processing Magazine.

[14]  Robert D. Nowak,et al.  Distributed image compression for sensor networks using correspondence analysis and super-resolution , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[15]  Shu-Ping Lin,et al.  Energy-Efficient Data-Centric Routing in Wireless Sensor Networks , 2005, IEICE Trans. Commun..

[16]  Yang Bai,et al.  Feature-based Image Comparison and Its Application in Wireless Visual Sensor Networks , 2011 .

[17]  Touradj Ebrahimi,et al.  The JPEG 2000 still image compression standard , 2001, IEEE Signal Process. Mag..

[18]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[19]  Jenhui Chen,et al.  MR 2 RP: The Multi-Rate and Multi-Range Routing Protocol for IEEE 802.11 Ad Hoc Wireless Ne , 2003 .

[20]  Luigi Ferrigno,et al.  Balancing computational and transmission power consumption in wireless image sensor networks , 2005, IEEE Symposium on Virtual Environments, Human-Computer Interfaces and Measurement Systems, 2005..

[21]  H. Coxeter,et al.  Introduction to Geometry. , 1961 .

[22]  Prasun Sinha,et al.  On the Potential of Structure-Free Data Aggregation in Sensor Networks , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[23]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[24]  Shu-Ping Lin,et al.  Energy-efficient data-centric routing in wireless sensor networks : Joint special section on autonomous decentralized systems , 2005 .

[25]  Najeem Lawal,et al.  Exploration of local and central processing for a wireless camera based sensor node , 2010, ICSES 2010 International Conference on Signals and Electronic Circuits.