Spatio-Temporal Data Reduction Technique in WVSN for Smart Agriculture

Abstract

Nowadays, to improve animal well-being in livestock farming applications, a wireless video sensor network (WVSN) can be deployed for surveillance and livestock monitoring to early detect injury or Asiatic hornets attacks [1]. 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 of nodes and the medium scarcity.In this work, we exploit 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 period, 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.

Energy Efficient and Reliable High Quality Video Transmission Architecture for Wireless Sensor Networks

“…With this method, data can be sent along a reliable route [6] in segments by first negotiating with the chosen parent node at each intermediate step [7]. Probability functions [8] are used by the dropping scheme [9] to select data packets for deletion. Prioritisation [10] of data packets and the hierarchy level [11] of the video source nodes [12] are also taken into account by the dropping scheme.…”