Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management
This work addresses the problem of efficient and reliable AI deployment on UAVs for agricultural monitoring in 6G networks, though it appears incremental as it builds on existing techniques like pruning and model selection.
The authors tackled the challenge of enabling on-device AI for agricultural supply-chain management using UAVs in 6G networks by proposing an architecture that combines UAVs, AI, and blockchain for traceability and transparency, and they developed a model selection strategy based on iterative pruning of FCN models for biomass estimation to adapt to runtime resource constraints, achieving multiple task-specific models with various complexities and accuracies.
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.