arXiv:2607. 20490v1 Announce Type: new Abstract: Edge Intelligence has emerged as a key paradigm for enabling real-time applications in smart cities by shifting computation from centralized cloud data centers to the network edge, thereby reducing latency and bandwidth consumption.
Edge computing brings unique challenges as the resources on the edge are highly diverse in capabilities and capacities, and highly distributed across many users and the physical world. Existing distributed computing frameworks cannot adequately handle this level of heterogeneity and distribution.
arXiv:2607. 14489v1 Announce Type: cross Abstract: Edge computing brings unique challenges as the resources on the edge are highly diverse in capabilities and capacities, and highly distributed across many users and the physical world.
The paper introduces a SAREF-compliant ontology designed to represent distributed AI workflows across edge, fog, and cloud environments. It extends the SAREF4SYST ontology with concepts for AI pipelines, executable jobs, resources, deployment constraints, and communication links, creating a unified semantic model for both AI workflows and heterogeneous infrastructures. Evaluation through smart‑grid energy service scenarios and competency questions demonstrates successful deployment, reasoning, and workload adaptation, achieving 90‑100% deployment success and sub‑80 ms orchestration times.
The paper introduces a dynamic framework for partitioning neural network layers across a heterogeneous edge‑cloud continuum, adapting to runtime changes in network conditions and device capabilities. It profiles models at startup, measures link quality, and periodically re‑evaluates the partitioning to optimize performance. Experiments on a Raspberry Pi, laptop, and desktop using VGG16, AlexNet, and MobileNetV2 demonstrate energy savings of 27.09–35.82% and latency reductions of 6.34–22.92% over static partitioning.
The article outlines a vision and recent progress toward a decentralized system that learns from real‑time building data, treating the building itself as a data center to enhance privacy and reduce dependence on external clouds. It introduces a lightweight, Kubernetes‑like orchestration framework for deploying AI services on low‑power microcontrollers, such as those in the Arduino ecosystem, enabling in‑situ learning on sensors. The work also presents experimental results for new online learning algorithms and proposes a cloud‑fog‑edge architecture using KOptim and FIWARE components.
arXiv:2607. 20937v1 Announce Type: new Abstract: We are moving from an information age to the age of intelligence.
arXiv:2604. 26508v2 Announce Type: replace-cross Abstract: Deploying Vision-Language Models (VLMs) on edge devices remains challenging due to their substantial computational and memory demands, which exceed the capabilities of resource-constrained embedded platforms.
arXiv:2506. 03168v2 Announce Type: replace-cross Abstract: Amid the challenges posed by global population growth and climate change, traditional agricultural Internet of Things (IoT) systems is currently undergoing a significant digital transformation to facilitate efficient big data processing.
arXiv:2609.17193v1 Announce Type: new Abstract: Large language model (LLM)-powered agentic AI services increasingly demand low-latency inference, motivating the deployment of LLMs across distributed...
Agentic‑Kube is a cooperative multi‑agent reinforcement learning framework for Kubernetes pod placement that splits the multi‑objective scheduling problem into cost minimisation, anti‑affinity fault tolerance, and vector resource balancing, each handled by a dedicated sub‑agent. It uses a bipartite Graph Convolutional Network to model host‑pod dependencies, a two‑stage monotonic QMIX value factorisation network for joint action coherence, and a plurality voting consensus with action feasibility masking. Evaluations on Google Kubernetes Engine and large‑scale clusters show Pareto‑efficient placements, a 53% reduction in anti‑affinity collisions, a 65% spot instance allocation ratio, and sub‑30 ms decision latencies up to 1,000 nodes without container restarts.
arXiv:2509. 23248v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and autonomous decision-making capabilities.
arXiv:2607. 22805v1 Announce Type: cross Abstract: We propose OrchNAS, an energy-aware, personalised, federated edge intelligence framework that leverages a Neural Architecture Search Service to automatically design service-adaptive models for heterogeneous edge environments.