Abstract
Devices in a visual sensor network (VSN) are mostly powered by batteries, and in such a network, energy consumption and bandwidth utilization are the most critical issues that need to be taken into consideration. The most suitable solution to such issues is to compress the captured visual data before transmission takes place. Compressive sensing (CS) has emerged as an efficient sampling mechanism for VSN. CS reduces the total amount of data to be processed such that it recreates the signal by using only fewer sampling values than that of the Nyquist rate. However, there are few open issues related to the reconstruction quality and practical implementation of CS. The current studies of CS are more concentrated on hypothetical characteristics with simulated results, rather than on the understanding the potential issues in the practical implementation of CS and its computational validation. In this paper, a low power, low cost, visual sensor platform is developed using an Arduino Due microcontroller board, XBee transmitter, and uCAM-II camera. Block compressive sensing (BCS) is implemented on the developed platform to validate the characteristics of compressive sensing in a real-world scenario. The reconstruction is performed by using the joint multi-phase decoding (JMD) framework. To the best of our knowledge, no such practical implementation using off the shelf components has yet been conducted for CS.
Highlights
A visual sensor network (VSN) [1] is a wireless platform consists of a set of visual nodes, intermediate nodes, and a gateway
Research suggests that compressive sensing (CS) [4], an emerging technique, has the potential to serve as an efficient compression method for a visual sensor network (VSN), due to the simple-encoder complex-decoder paradigm, which is the inverse of traditional compression
This research work is limited to multi-view image scenario, and based compressive sensing (BCS) along with joint multi-phase decoding (JMD) framework [17,18,19,20] is implemented on low power applications (VSN) to evaluate and analyze its performance based on the reconstruction quality and computational complexity of BCS rather than measuring the bandwidth utilization
Summary
A visual sensor network (VSN) [1] is a wireless platform consists of a set of visual nodes, intermediate nodes, and a gateway. As this research work is focused towards the implementation and analysis of compression technique BCS on VSN, capturing of images in raw format is important. A practical visual sensor platform is developed with efficient BCS mechanism using an Arduino Due microcontroller board, XBee transmitter, and uCAM-II camera In this case, images or videos captured by the camera are first compressed using block compressive sensing [16] (BCS) on the microcontroller. This research work is limited to multi-view image scenario, and BCS along with JMD framework [17,18,19,20] is implemented on low power applications (VSN) to evaluate and analyze its performance based on the reconstruction quality and computational complexity of BCS rather than measuring the bandwidth utilization.
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