Distributed video transmission reduction approach for energy saving in WMSNs

Wireless Multimedia Sensor Networks (WMSNs) are composed of a large number of sensor nodes that are distributed in a region to collect and transmit data. Video transmission is one of the most important applications of WMSNs because it can provide critical information about monitored areas. WMSNs face challenges related to energy consumption, bandwidth usage, and network congestion related to huge amounts of data collected by sensors. To tackle this problem, this paper proposes the Distributed Video Transmission Reduction Approach for Energy Saving in WMSN (DiViTRA). The method involves two phases: sensing and transmission phases. DiViTRA achieves frame rate adaptation to reduce the number of captured video frames and save energy during the sensing phase. In the transmission phase, three effective techniques, ORB (Oriented FAST and Rotated BRIEF), Brute-Force (BF) Matcher, and Grid-based Motion Statistics (GMS) are applied to decide whether to transmit the current captured frame or remove it and adjust the frame capturing rate of the video sensor accordingly. In the case of frame transmission, the DiViTRA approach compresses the frame using two data reduction approaches: PCA (Principal Component Analysis) and Huffman encoding. Through simulations, DiViTRA demonstrates a 12% reduction in energy consumption, and 71% is a ratio of reduction in sent frames while preserving stream quality. The approach has been validated in scenarios involving critical events, showcasing its efficacy in maintaining data integrity during transmission. We propose a distributed video transmission reduction approach (DiViTRA) for energy conservation in WMSN. It minimizes transmitted data through sensing and transmission phases, removing redundant frames, and maintaining acceptable data quality. DiViTRA uses an adaptive frame rate sensing approach to balance accuracy and energy use, while it uses ORB, BF Matcher, and GMS techniques to determine frame transmission decisions. The results show that the DiViTRA outperforms another method in terms of data reduction, frame rate adaptation, video quality, and power consumption. image