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Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management

arXiv 2203.06465 ↗First posted 12 Mar 2022cs.AI cs.LG cs.NIcited by 0

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Abstract (arXiv)

6G envisions artificial intelligence (AI) powered solutions for enhancing the quality-of-service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial vehicles (UAVs), AI and blockchain for agricultural supply-chain management with the purpose of ensuring traceability, transparency, tracking inventories and contracts. We propose a solution to facilitate on-device AI by generating a roadmap of models with various resource-accuracy trade-offs. A fully convolutional neural network (FCN) model is used for biomass estimation through images captured by the UAV. Instead of a single compressed FCN model for deployment on UAV, we motivate the idea of iterative pruning to provide multiple task-specific models with various complexities and accuracy. To alleviate the impact of flight failure in a 6G enabled dynamic UAV network, the proposed model selection strategy will assist UAVs to update the model based on the runtime resource requirements.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 12 Mar 2022.

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References · 9

  1. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  2. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
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  6. 2019Artificial Intelligence-Enabled Intelligent 6G Networkscited by 27
  7. 2019UAV Communications for 5G and Beyond: Recent Advances and Future Trendscited by 24
  8. 2019A Speculative Study on 6Gcited by 14
  9. 2018Density Weighted Connectivity of Grass Pixels in Image Frames for Biomass Estimationcited by 1

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