Underwater wireless sensor nodes for ecosystem monitoring applications using two-layer binary traversal based optimal image compression technique

In current years, underwater wireless sensor networks (UWSNs) have attracted huge attention from researchers. Generally, UWSN encompasses a huge quantity of sensors, and underwater vehicles are collaboratively arranged for performing the collection of data, interpretation, and, processing. However, its difficult nature makes position updates or including new devices more challenging. Moreover, owing to the limitations of UWSN energy storage of end devices, restricted bandwidth, and its difficulty in recharging or repairing the underwater device, this is extremely important to improve the energy performance of UWSN. The power consumption imbalance can cause restricted network lifetime and less performance. In order to overcome these issues, an optimal threshold-driven image compression approach is proposed in UWSN. The run length encoding model is applied for improving the compression performance in UWSN. The performance of the designed optimal image compression approach is assessed by means of various performance measures, such as compression time (s), decompression time (s), space-saving (%), and compression ratio, Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR), Mean Square Error (MSE), Structural Similarity (SSIM), and Mean Absolute Error (MAE). Thus, the devised image compression model attained less, compression time, compression ratio, decompression time, MSE, and MAE of 5.426, 2.0294, 5.064, 0.0550, and 0.141, respectively.