This article categorizes existing UAV inspection architectures, identifies their key system challenges and architectural requirements, and experimentally assesses the feasibility of semantic edge intelligence on NVIDIA Jetson UAV-class hardware using the COCO-Bridge dataset.
This paper benchmarks the performance trade-offs among fully onboard, cloud-based, and split-computing architectures for lightweight VLMs using SmolVLM-256M as a representative lightweight VLM and shows that no deployment strategy is universally optimal.
Zoha Azimi, Reza Farahani, S. Dustdar et al.· 0 citations
DRLM, a Deep Reinforcement Learning-based LLM query orchestration framework in edge environments shows robust and stable orchestration, and improves latency under increasing workloads up to 61.4%, demonstrating robust and stable orchestration.
Reza Farahani, Zoha Azimi Ourimi, Mario Colosi et al.· 0 citations
Inspection of critical infrastructure, such as power lines, is increasingly conducted using unmanned aerial vehicles (UAVs) that capture aerial video for subsequent human review. Although recent edge-based approaches deploy onboard object detectors to identify predefined defect classes, these pipelines remain closed-se...
Reza Farahani, Zoha Azimi, Ilir Murturi et al.· International Conference on...· 1 citation
It is shown that the structure of service-dependency graphs, modelled as DAGs of compute stages, is a primary determinant of whether decentralised, price-based resource allocation works reliably at scale.
Lauri Lovén, Alaa Saleh, Reza Farahani et al.· arXiv.org· 2 citations
This paper employs five lightweight machine learning models to predict query-specific latency, accuracy, resource usage, and response size for each model-size-quantization-device combination, and design a lightweight heuristic that approximates the BILP solution.
Reza Farahani, Zoha Azimi, Mario Colosi et al.· 0 citations
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