#edge computing
36 resultsA Taxonomy of Performance Metrics for the Distributed Computing Continuum
Praveen Kumar Donta, Boris Sedlak, Alfreds Lapkovskis +6
The paper proposes a structured taxonomy of performance metrics for distributed computing continuum systems, categorizing metrics across computing, network, and application levels…
When Robots Exchange Meaning: A Demo of Goal-Oriented Semantic Communications for Collaborative Robotics
Peizheng Li, Xinyi Lin, Sajida Gufran +1
The paper demonstrates a robot‑edge semantic communication testbed that compresses visual data into VQ‑VAE tokens on a mobile robot and reconstructs it on an edge device for semant…
Layered Architecture for Mobile Intelligence
Qingwen Liu, Mingqing Liu
The paper proposes a mobility‑aware architectural framework called the Mobile AI Stack, which integrates energy networks, efficient AI chips, cloud‑edge‑mobile infrastructure, dist…
Adaptive Security at the Edge for 6G-Enabled Healthcare IoT
Ijaz Ahmad, Erkki Harjula
The paper introduces NANOEDGEGUARD, a kernel‑plane controller that adaptively enforces multi‑tier rate limits at edge gateways for healthcare IoT, improving alarm latency and reduc…
A Cloud Continuum Research Infrastructure for Distributed CPS Experimentation
Fabio Orazio Mirto, Giuseppe Tricomi, Luca D'Agati +7
The paper proposes a two‑level reference architecture for experimenting with Cloud Continuum applications, separating infrastructure management from application workflows across Ed…
When Fish Look Alike: Tracking Identities with Dual-branch Elasticity
Vran Lee, Xin Liu, Yijie Wei +3
The paper introduces TIDE, a dual‑branch tracking system that forgoes heavy appearance models and instead uses adaptive geometric correspondence to track dense, homogeneous targets…
An Informativeness-based Clustered Federated Learning Method for Reliable Traffic Prediction in Managed Wi-Fi Networks
Luca Barbieri, Gianluca Fontanesi, Lorenzo Galati Giordano +2
The paper proposes a clustered federated learning framework that selects informative AP clusters using differential entropy to improve Wi‑Fi traffic prediction while reducing commu…
RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment
Hansi Karunarathna, Nirhoshan Sivaroopan, Chamara Madarasingha +2
The paper presents RAG-HAR+, a cost‑efficient system that combines retrieval of similar sensor data with large language model (LLM) assistance to recognize human activities on edge…
Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents
Amirmohammad Farzaneh, Osvaldo Simeone
The paper introduces Think Short, Defer Smart (TSDS), a framework for edge-deployed LLM agents that stops on-device reasoning when actions stabilize and defers uncertain actions to…
VAD to the Bone: Ultra-Tiny Speech Activity Detection for Edge Deployment
Stephen Bauer, Sheila Seidel, Shanza Iftikhar +2
The paper introduces kiloVAD, an ultra‑tiny, CNN‑only voice activity detection model designed for edge devices, using standard Mel features, structured pruning with self‑distillati…
VetClaw: An Edge-Cloud Multimodal Agentic System for Veterinary Disease Screening
Syed Mhamudul Hasan, Anas AlSobeh, Hussein Zangoti +1
VetClaw is an edge‑cloud system that captures animal images (and optional symptom descriptions) on a device, sends them to a vision‑language model, and uses an agentic workflow to…
Lightweight Image Classification of Raptor Species for Edge Devices: Rare-Species Dataset Expansion via Video Frame Extraction, Knowledge Distillation, and TensorRT Deployment
Takeshi Nishikawa
The paper presents a lightweight model ensemble for classifying raptor species on edge devices, using knowledge distillation from a large teacher model and expanding the dataset vi…
MIND-CAVs: Multi-Intelligence Negotiation and Decision System for CAVs based on Intent-Driven Autonomy
Mainak Mondal, Yihang Feng, Yangchao Luo +2
The paper introduces MIND-CAVs, a framework where connected autonomous vehicles share high‑level intent information via V2X and receive coordinated decisions from edge servers, imp…
Modeling and Validation of Quality of Control for Edge-Offloaded Collaborative Navigation
Neelabhro Roy, Mikael Hammarling, Victor Nan Fernandez-Ayala +4
The paper extends the quality of control (QoC) framework to practical robotic navigation with edge-offloaded collaborative control, modeling how 5G network delay and reliability af…
CODA: Algorithm-Hardware Co-design for Edge Video Diffusion via NMP-Enabled Compute-Cache Operator Disaggregation
Yuanpeng Zhang, YuXuan Wu, Yitong Xiao +6
The paper introduces CODA, a hardware-software co-designed architecture that separates compute and cache operations for edge video diffusion models, using near‑memory processing to…
PolyQ: Codesigning End-to-End Quantization Framework for Scalable Edge CPU LLM Inference
Hyunwoo Oh, Suyeon Jang, Hanning Chen +4
PolyQ is a co-designed compiler and quantization framework that assigns per‑channel bit‑widths to LLM activations on CPUs, enabling fine‑grained fractional‑bit precision while keep…
EdgeFaaS: A Function-based Framework for Edge Computing
Neha Vadnere, Yu-Ting Wang, Yitao Chen +2
EdgeFaaS is a function‑as‑a‑service framework that abstracts heterogeneous IoT, edge, and cloud resources to run edge applications such as video analytics, federated learning, and…
SmartRAG: Native Graph-Based RAG for Mobile Device
Zhihan Jiang, Meng Li, Shenghao Liu +6
SmartRAG is an on-device framework that combines a small quantized language model with a graph-based retrieval system and a continually learnable named-entity recognizer to enable…
CoVStream: Edge-Cloud Collaboration for Understanding of Long Video Streams
Xu Liu, Guikun Chen, Zihao Yan +2
The paper introduces CoVStream, an edge‑cloud system that compresses raw video into compact visual features and captions on the device, sends them to the cloud for graph‑based reas…
GHR-VLM: Making Zero-Shot Transit Video Analytics Realizable with Grounded Hybrid Reasoning
Kaicong Huang, Weiheng Oh, Ruimin Ke +2
The paper introduces GHR-VLM, a framework that combines edge-based visual grounding with a vision‑language model to perform zero‑shot analytics of bus surveillance video, identifyi…
Green Deep Reinforcement Learning for IoT Edge Intrusion Detection
Saeid Jamshidi, Foutse Khomh, Rolando Herrero +2
The paper introduces two carbon‑aware deep reinforcement learning intrusion detection systems for IoT edge gateways, one label‑free (DeepEdgeIDS) and one supervised (AutoDRL‑IDS),…
Profiling and Scheduling Complex O-RAN Applications Across the 5G Edge and Cloud
Yoonjae Hwang, Bhaskar Krishnamachari
The paper introduces O-DAG, a framework that profiles and schedules O‑RAN AI/ML pipelines modeled as DAGs across far‑edge, near‑edge, and cloud resources, and evaluates several sch…
A Comprehensive Evaluation of Deep Learning Object Detection Models on Heterogeneous Edge Devices
Daghash K. Alqahtani, Muhammad Aamir Cheema, Maria A. Rodriguez +1
The paper benchmarks several deep learning object detection models on various edge devices, measuring accuracy, latency, and energy use while also analyzing performance as scene co…
Data Replication Meets Function Scheduling in the Edge-Cloud Continuum
Matteo Cenzato, Dario d'Abate, Arianna Dragoni +2
The paper studies the joint problem of placing replicated data and scheduling serverless functions in edge‑cloud environments under strong and eventual consistency, offering an opt…