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S. Dustdar

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Conference Jul 2026

EVLM: Intent-Driven Edge Vision Language Model for UAV-Based Power Line Inspection

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. · 1 citation
#artificial intelligence Review May 2026

Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety

It is argued that evaluation should move beyond static question answering and model accuracy towards workflow-level assessment of evidence quality, tool use, policy and invariant compliance, staged execution, recovery, calibration, cost, and human intervention.

Muhammad Bilal, Jon Crowcroft, Rui-Zhi Wang et al. · 3 citations · ⚡1
Preprint Aug 2026

LYRA: Label-Free Structural Synchronization and Resource Allocation for UAV Edge Networks

A joint model update scheduling and resource allocation framework, aiming to maximize long-term semantic fidelity and resource efficiency of UAV edge intelligence systems, and a Lyapunov-guided discrete reinforcement learning algorithm that performs action space dimensionality reduction and transforms constraints into...

Feng He, Alireza Furutanpey, Paolo Bellavista et al. · 0 citations
Preprint Jul 2026

Agentic Service-Oriented Computing: A Manifesto for the Next Frontier of Service-Oriented Computing

It is argued that the Services Computing community is especially well positioned to provide the conceptual and engineering spine for this emerging field, transforming agentic AI from fragmented demonstrations into dependable, service-based systems worthy of human and organisational trust.

Amin Beheshti, Rong N. Chang, B. Benatallah et al. · 0 citations
Preprint Jul 2026

LMEdge: QoS-Aware LLM Inference Orchestration on Edge Clusters

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
#edge computing Sep 2026

Low-Energy Resource Optimization and Task Assignment for Satellite Edge Computing Networks

Satellite edge computing (SEC) has emerged as a promising paradigm to enhance in-orbit data processing capabilities and reduce transmission latency. However, satellite image processing tasks in SEC environments face critical challenges in efficient data handling, resource coordination, and transmission scheduling. The...

Xiaoteng Yang, Jie Feng, Lei Liu et al. · 0 citations

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