Optimization of Power Allocation in Multimedia Wireless Sensor Networks

Optimization of Power Allocation in Multimedia Wireless Sensor Networks

Ming Yang (School of Computing and Software Engineering, Southern Polytechnic State University, Marietta, GA, USA), Dajin Wang (Department of Computer Science, Montclair State University, Montclair, NJ, USA) and Nikolaos Bourbakis (College of Engineering and Computer Science, Wright State University, Dayton, OH, USA)
DOI: 10.4018/ijmstr.2013010104
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Abstract

Wireless Sensor Networks (WSN) have been widely applied in monitoring and surveillance fields in recent years and have dramatically changed the methodologies and technologies in monitoring and surveillance. However, the sensor nodes in WSN have very limited computing resources and power supply, and thus the maximization of network life is a very critical issue. In the newly-emerging Wireless Multimedia Sensor Network (WMSN), the high volume of sensed video data needs to be compressed before transmission. Different video coding schemes have been developed and applied to wireless multimedia sensor networks, and there exists a tradeoff between the power consumption of data compression and that of data transmission. Video compression will reduce the amount of data that needs to be transmitted and thus the amount of power consumed for data transmission; however, too much video compression will consume excessive power which outweighs the power savings on data transmission. Thus, how to reach an optimized balance between compression and transmission and maximize network life becomes a challenging research issue. In this paper, the authors propose mathematical models which describe power consumptions of data compression and transmission of sensor nodes in hexagon-shaped clusters. Under the proposed model, they have achieved the optimized data compression ratio which can minimize the overall power consumption of the whole cluster.
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2. Video Coding Schemes For Wireless Multimedia Sensor Networks

2.1. Predictive Video Coding

In WMSN, usage of video encoders such as predictive encoders such as H.26x, MPEG-x, necessitates higher processing and memory capacities. The key idea of predictive coding is to utilize motion estimation and motion compression to reduce temporal redundancy, use Discrete Cosine Transformation (DCT) to remove spatial redundancy, and then use Huffman/Arithmetic Coding to reduce coding redundancy. The state-of-the-art predictive video encoders have very good rate distortion characteristics by following the classical complex encoder and simple decoder balance. The computational complexity of the encoder is much higher than that of the decoder.

Predictive coding can reach high compression ratios and dramatically reduce the bit rate of the source video. However, predictive coding schemes has the following disadvantages when being applied to WMSN: (1) high memory requirements due to the need to store reference frames (used for motion estimation); (2) processing delay caused by motion search; (3) high power consumption due to the complexity of motion estimation/compensation algorithms; (4) sensitivity to packet loss and errors in wireless transmission (Figure 1).

Figure 1.

Sensitivity to packet loss/damage in predictive coding

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