Distance based heuristic for power and rate allocation of video sensor networks

Video sensor networks (VSNs) offer an alternative to several existing surveillance technologies. However, unlike in conventional sensor network, video processing and transmission requires large amount of resources both in signal processing, i.e., encoding, and transmission of the encoded data. For such networks, an optimal encoding power and rate allocation method based on a power-rate-distortion (PRD) analysis has previously been proposed, where the power consumption of video encoding can be controlled by managing some encoding parameters. However, these parameters are currently obtained offline by examining the stored video, an approach which may not be suitable for surveillance applications. In this paper, a distance based heuristic for encoding power and rate allocation of VSNs is proposed. The proposed technique is a practical solution since the video coding parameter is controlled by the node's location in the network. Although the proposed technique offers a sub-optimal solution, in some scenarios it achieves performance up to 94% of the optimal solution in terms of network lifetime.

[1]  Tomi Räty,et al.  Survey on Contemporary Remote Surveillance Systems for Public Safety , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  Ling Guan,et al.  Network Lifetime Maximization in Wireless Visual Sensor Networks using a Distributed Algorithm , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[3]  Zhihai He,et al.  Resource allocation and performance analysis of wireless video sensors , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

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

[5]  Christos G. Cassandras,et al.  On maximum lifetime routing in Wireless Sensor Networks , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[6]  Syed Ali Khayam,et al.  Energy efficient video compression for wireless sensor networks , 2009, 2009 43rd Annual Conference on Information Sciences and Systems.

[7]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[8]  J.C. Yang,et al.  Vision based Fire/Flood Alarm Surveillance System via Robust Detection Strategy , 2008, 2008 IEEE Instrumentation and Measurement Technology Conference.

[9]  Zoltan Safar,et al.  Multimodal Wireless Networks: Communication and Surveillance on the Same Infrastructure , 2007, IEEE Transactions on Information Forensics and Security.

[10]  Ling Guan,et al.  Distributed Algorithms for Network Lifetime Maximization in Wireless Visual Sensor Networks , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[11]  Wen-Tsuen Chen,et al.  Design and Implementation of a Real Time Video Surveillance System with Wireless Sensor Networks , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[12]  Deborah Estrin,et al.  A wireless sensor network For structural monitoring , 2004, SenSys '04.

[13]  Anthony Rowe,et al.  DSPcam: A camera sensor system for surveillance networks , 2009, 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC).

[14]  W.B. Heinzelman,et al.  On the coverage problem in video-based wireless sensor networks , 2005, 2nd International Conference on Broadband Networks, 2005..

[15]  Ishfaq Ahmad,et al.  Power-rate-distortion analysis for wireless video communication under energy constraints , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[16]  Zhigang Yang,et al.  Research on the key issue in video sensor network , 2010, 2010 3rd International Conference on Computer Science and Information Technology.