Edge Computing
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
Rotatable Antenna-Enabled Mobile Edge Computing
Announce Type: replace Abstract: In the evolving landscape of mobile edge computing (MEC), enhancing communication reliability and computation efficiency to support increasingly stringent low-latency services remains a fundamental challenge. Rotatable antenna (RA) is a promising technology that introduces new spatial degrees of freedom (DoFs) to tackle this challenge. In this letter, we investigate an RA-enabled MEC system where antenna boresight directions can be independently adjusted to...
Compact LLM Deployment and World Model Assisted Offloading in Mobile Edge Computing
Announce Type: replace Abstract: This paper investigates compact large language model (LLM) deployment and world-model-assisted inference offloading in mobile edge computing (MEC) networks. We first propose an edge compact LLM deployment (ECLD) framework that jointly applies structured pruning, low-bit quantization, and knowledge distillation to construct edge-deployable LLM variants, and we evaluate these models using four complementary metrics: accessibility, energy consumption,...
Predictive Autoscaling in Cloud-Native and Federated Cloud-Edge Computing Environments: A Taxonomy and Future Directions
arXiv:2606.07046v1 Announce Type: new Abstract: Autoscaling is a key capability in cloud-native systems, where dynamic workloads, heterogeneous environments, and latency-sensitive applications require efficient and adaptive resource management. Traditional reactive approaches based on fixed thresholds often respond too late, leading to resource imbalance, performance degradation, and unstable scaling behavior. Recent advances in predictive models, Kubernetes Custom Resource Definitions...
AIGaitor: Privacy-preserving and cloud-free motion analysis for everyone, using edge computing
Announce Type: replace Abstract: Motion capture is the gold standard for measuring human movement, but clinical use remains limited by cost, technical complexity, and privacy concerns. AIGaitor is a privacy-preserving, cloud-free motion analysis system that runs markerless monocular motion-capture pipelines and downstream deep-learning analysis entirely on a consumer smartphone using on-device neural accelerators. To motivate its design, we surveyed 74 rehabilitation clinicians: 92 percent...
CANS: Accelerating Multiuser Collaborative Edge Inference via Cooperative Autodidactic NeuroSurgeon
Announce Type: new Abstract: Recently, mobile edge computing (MEC)-enabled collaborative deep neural network (DNN) inference has emerged as a promising approach for delivering intelligent services to resource-constrained mobile devices. A representative scenario is multi-user collaborative edge inference, where distinct devices independently partition their DNN models and offload backend computation to a common edge server over wireless networks. However, determining the optimal DNN...
Q-FE: A Quantum-Native 6G Far-Edge Architecture Securing Industrial IoT Digital Twins via CSIDH-PQC and Asynchronous Federated Learning
arXiv:2606.03611v1 Announce Type: new Abstract: Sixth-generation (6G) wireless networks will underpin ultra-dense Industrial IoT (IIoT) ecosystems in which resource-constrained Far-Edge devices -- autonomous mobile robots, industrial actuators, connected vehicles -- must simultaneously satisfy sub-millisecond latency, $10^{-7}$-class reliability, and decades-long cryptographic security. Current architectures delegate Digital Twin (DT) computation to centralised cloud or Mobile Edge Computing...
AISC deployment in dynamic UAV-assisted MEC network: a reinforcement learning method based on heterogeneous graph attention neural network
Announce Type: new Abstract: Unmanned aerial vehicles-assisted mobile edge computing (UMEC) can execute compute-intensive and latency-critical artificial intelligence (AI) services, which can be provided by multiple UAVs collaborating in the air to perform inference tasks. Completing an AI service requires multiple inferences, each of which is implemented by an AI service chain consisting of multiple virtual network functions (VNFs). The application of AISC relies on an efficient AISC...
Hosting Capacity Assessment and Enhancement for Edge Data Centers in Active Distribution Networks
new Abstract: With the increasing demand for edge computing and AI-driven workloads, integrating small and medium-sized edge data centers into distribution networks has become increasingly important. This paper investigates the hosting capacity of distribution networks for data center integration and identifies the key physical mechanisms that limit the maximum allowable data center load. The baseline analysis shows that data center hosting capacity varies significantly across candidate...
Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks
arXiv:2602.21949v2 Announce Type: replace Abstract: In this paper, we investigate a problem of minimizing total energy consumption for secure federated learning (FL) over wireless edge networks. To address the high computational cost and privacy challenges in conventional FL with neural networks (NN) for resource-constrained users, we propose a novel FL with hyperdimensional computing and differential privacy (FL-HDC-DP) framework. In the considered model, each edge user employs...
Characterizing the Impact of NVFP4 Quantization for Low-Power Edge AI Deployment
arXiv:2606.06527v2 Announce Type: replace Abstract: Energy-efficient neural-network inference at the edge requires reducing arithmetic cost, memory traffic, computation energy, and storage overhead while maintaining acceptable accuracy. This paper presents an ablation-focused study of NVFP4 quantization for edge-efficient neural networks, with emphasis on the relationship between activation precision, weight precision, block-size scaling, retraining, and model accuracy. NVFP4 activations are...