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Image Similarity based Data Reduction Technique in Wireless Video Sensor Networks for Smart Agriculture

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Abstract

Nowadays, to improve animal well being in livestock farming or beekeeping application, a wireless video sensor network (WVSN) can be deployed to early detect injury or Asiatic hornets attacks. WVSN represents a low-cost monitoring solution compared to other technologies such as the closed circuit television technology (CCTV). WVSNs are composed of low-power resource-constrained video sensor nodes (motes). These nodes capture frames from videos at a given frequency (frame rate) and wirelessly send them to the sink. The big amount of data transferred from the nodes to the sink consumes a lot of energy on the sensor node, which represents a major challenge for energy-limited nodes. In this paper, we introduce two complementary mechanisms to reduce the overall number of frames sent to the sink. First, the Transmission Data Reduction algorithm (TDR) run on the sensor node leverages the similarity degree of consecutive images. Second, the Inter-Nodes Similarity algorithm (INS) exploits the spatio-temporal correlation between neighbouring nodes in order reduce the number of captured frames. The results show a 95% data reduction, surpassing other techniques in the literature by 30% at least.
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Dates and versions

hal-03145329 , version 1 (18-02-2021)

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  • HAL Id : hal-03145329 , version 1

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Christian Salim, Nathalie Mitton. Image Similarity based Data Reduction Technique in Wireless Video Sensor Networks for Smart Agriculture. AINA 2021 - 35th International Conference on Advanced Information Networking and Applications, May 2021, Toronto, Canada. ⟨hal-03145329⟩

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