Performance Comparison of Data Reduction Techniques for Wireless Multimedia Sensor Network Applications

ABSTRACT

With the increased use of smartphones, Wireless Multimedia Sensor Networks (WMSNs) will have opportunities to deploy such devices in several contexts for data collection and processing. While smartphones come with richer resources and can do complex processing, their battery is still limited. Background subtraction (BS) and compression techniques are common data reduction schemes, which have been used for camera sensors to reduce energy consumption in WMSNs.

In this paper, we investigate the performance of various BS algorithms and compression techniques in terms of computation and communication energy, time, and quality. We have picked five different BS algorithms and two compression techniques and implemented the min an Android platform. Considering the fact that these BS algorithms will be run within the context of WMSNs where the data is subject to packet losses and errors, we also investigated the performance in terms of packet loss ratio in the network under various packet sizes.

The experiment results indicated that the most energy efficient BS algorithm could also provide the best quality in terms of the foreground detected. The results also indicate that data reduction techniques including BS algorithms and compression techniques can provide significant energy savings in terms of transmission energy costs.

RELATED WORK

Several performance evaluation studies have been published to examine the weakness and strengths of BS algorithms. In a comparative evaluation of classical approaches has been conducted on background subtraction algorithms for exposing static foreground objects. The previous solutions have been categorized into several classes. Then, representative solutions have been compared using both quantitative and qualitative metrics. The paper concludes that sub sampling based solutions give the best results at the expense of a low computational cost for general purpose static object detection.