Image processing based data reduction technique in WVSN for smart agriculture

Nowadays, to improve animal well being in livestock farming application, a wireless video sensor network (WVSN) can be deployed to early detect injury and monitor animals. They are composed of small embedded video and camera motes that capture video frames periodically and send them to a specific node called a sink. Sending all the captured images to the sink consumes a lot of energy on every sensor and may cause a bottleneck at the sink level. Energy consumption and bandwidth limitation are two important challenges in WVSNs because of the limited energy resources of the nodes and the medium scarcity. In this work, we introduce two mechanisms to reduce the overall number of frames sensed and sent to the sink. The first approach is applied on each sensor node, where the FRABID algorithm, a joint data reduction, and frame rate adaptation on sensing and transmission phases mechanism is introduced. This approach reduces the number of sensed frames based on a similarity method. The aim is to adapt the number of sensed frames based on the degree of difference between two consecutive sensed frames in each period. This adaptation technique maintains the accuracy of the video while capturing frames holding new information. This approach is validated through simulations using real data-sets from video sensors (Wang et al. in: 2014 IEEE conference on computer vision and pattern recognition workshops, pp 393–400, 2014). The results show that the amount of sensed data is reduced by more than 70% compared to a recent algorithm in Christian et al. (Multimed Tools Appl 79(3):1801–1819, 2020) while guaranteeing the detection of all the critical events at the sensor node level. The second approach exploits the Spatio-temporal correlation between neighboring nodes to reduce the number of captured frames. For that purpose, Synchronization with Frame Rate Adaptation SFRA algorithm is introduced where overlapping nodes capture frames in a synchronized fashion every $N-1$ periods, where N is the number of overlapping sensor nodes. The results show more than 90% data reduction, surpassing other techniques in the literature at the level of the number of sensed frames by 20% at least.