edge computing

Telemetry Dataset for Federated Learning on Heterogeneous Edge Devices Using Raspberry Pi 5 and NVIDIA Jetson Nano

This dataset accompanies the manuscript "Telemetry-aware federated learning on heterogeneous edge devices: an experimental study". The dataset contains telemetry measurements collected from a federated learning testbed consisting of a Raspberry Pi 5 and an NVIDIA Jetson Nano running the Flower federated learning framew...

Telemetry Dataset for Federated Learning on Heterogeneous Edge Devices Using Raspberry Pi 5 and NVIDIA Jetson Nano

This dataset accompanies the manuscript "Telemetry-aware federated learning on heterogeneous edge devices: an experimental study". The dataset contains telemetry measurements collected from a federated learning testbed consisting of a Raspberry Pi 5 and an NVIDIA Jetson Nano running the Flower federated learning framew...

Smart aquaponics: trends, challenges, and future directions

Future progress requires stronger integration of prediction, control, calibration, biological benchmarking, and operator usability, and this systematic literature review addresses this gap through 10 research questions.

Blockchain fog attention framework for collusion detection and automated accountability in internet of things networks

The proposed Blockchain-Fog Computing Collaborative Framework with Deep Attention-based Collusion Detection and Automated Accountability (BF3-ACDA) framework combines a hierarchical blockchain–fog architecture with an attention-based collusion graph neural network (AttnCol-GNN), whose reputation-aware attention coeffic...

Interpretable Digital Twin-Safe Scheduling for Deadline-Aware Task Allocation in Dynamic Fog-Edge Computing: A Comparative and Prediction-Validity Evaluation

Edge AI System-of-Systems Reference Architecture Engineering Foundations and Multi-Dimensional Views

Edge AI systems are emerging from the convergence of IoT, edge computing, AI, agentic AI, and embodied, physical generative edge AI delivering adaptive, autonomous behaviour under physical, cyber, and operational constraints while remaining trustworthy. This article frames edge AI as a complex system-of-systems in whic...

An Honest Effect Size for Contingency Tables: Why Nothing Can Be Unbiased, Where to Put the Error Instead, and How to Route the Report (m0f)

An Honest Effect Size for Contingency Tables: Why Nothing Can Be Unbiased, Where to Put the Error Instead, and How to Route the Report William J. Dwyer, MD, MPH, FAAP — Department of Mathematics and Statistics, University of Massachusetts Lowell. ORCID 0009-0004-0855-7222. Concept DOI (always resolves to the latest ver...

SpikeGate: An Event-Driven Neuromorphic SNN Gatekeeper for Duty-Cycled On-Device SLM Inference

Probabilistic Disaggregation of Behind-the-Meter PV Systems Using Conformal Prediction

Disaggregating solar photovoltaics (PV) profiles from smart electricity meter data has attracted attention, as Distribution System Operators (DSOs) need street-level PV generation profiles to improve grid operations and planning. Given the importance of reliability in operational decisions, probabilistic results are pr...

Physically implemented analog in-memory distance computing with InGaZnO transistor-based capacitive unit

Modern edge devices increasingly require real-time adaptation to their environment without relying on cloud-based updates, which can introduce latency and security risks. To meet these demands, memory-augmented neural networks (MANNs) have gained traction for enabling adaptive on-device learning. Hardware implementatio...

Joint Optimization of Memory and Computing Frequency for Energy-Efficient DNN Inference

The impact of memory frequency and computing frequency on DNN inference time is considered, and a low-complexity heuristic algorithm is proposed to effectively solve the overall problem with polynomial time complexity.

UVirtio: Enabling Ubiquitous Resource Sharing for RISC-V Industrial Edge Devices

UVirtio introduces a device-profile-based virtual hardware abstraction layer that minimizes performance overhead, and implements a live migration mechanism using differential packing, providing a scalable and agile virtualization solution for the ubiquitous computing frontier.