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Conference Papers Year : 2022

Spatio-Temporal Data Reduction Technique in WVSN for Smart Agriculture

Jana Koteich
  • Function : Author
  • PersonId : 1166088
Christian Salim

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.
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Dates and versions

hal-03781866 , version 1 (20-09-2022)

Identifiers

  • HAL Id : hal-03781866 , version 1

Cite

Jana Koteich, Christian Salim, Nathalie Mitton. Spatio-Temporal Data Reduction Technique in WVSN for Smart Agriculture. STWiMob 2022 - The 15th International Workshop on Selected Topics in Wireless and Mobile computing, Oct 2022, Thessaloniki, Greece. ⟨hal-03781866⟩

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