Coefficient Permuted Adaptive Block Compressed Sensing for Camera Enabled Underwater Wireless Sensor Nodes

Underwater wireless sensor network (UWSN) comprises of large number of sensors and underwater vehicles deployed collaboratively to perform data collection, interpretation and processing. These sensors and vehicles have been equipped with cameras to capture a visual picture of underwater targets and precious resources in recent times. These cameras in the sensors generate large volumes of data as they are continuously involved in surveillance of the aquatic environment. Transmitting or storing the data as a whole drains the power of the battery-operated nodes. Therefore it is necessary to reduce the data to save energy and improve the lifetime of the sensors. To serve this purpose, block compressed sensing (BCS) based image compression can be used. However, BCS has two significant issues: low sampling efficiency for poorly sparsed real-time underwater images and fixing the samples chosen from image blocks. The former can be overcome by permuting and evenly distributing the transform coefficients to all blocks. Similarly, the latter can be overcome by adopting adaptive block compressed sensing (ABCS). A combination of coefficient permutation and ABCS, namely coefficient permuted adaptive block compressed sensing (CP-ABCS), is proposed for better image reconstruction with fewer samples. This proposed approach operates in the processor of the sensors to compress data within the nodes and then transmit the reduced data. It has improved PSNR of 4-8dB, SSIM of 0.1-0.3 and space-saving (SS) of 5-10% compared with other literature schemes. Also, it has used only significantly fewer samples of about 10-20% for reconstruction.

Automated summary

Underwater Wireless Sensor Networks (UWSNs) are used for data collection and processing of underwater targets and resources. To save energy and improve the sensor's lifetime, a new image compression method called Coefficient Permuted Adaptive Block Compressed Sensing (CP-ABCS) is proposed. This method reduces data volume by permuting coefficients and adopting adaptive block compressed sensing, achieving better image reconstruction with fewer samples.