edge computing

Deviation-aware digital twin-enabled joint user association and task partitioning for industrial IoT offloading

Latency-critical industrial Internet of Things (IIoT) applications outstrip on-device computing capability, yet digital twin (DT)-assisted multi-access edge computing (MEC) is typically modeled as a perfectly synchronized mirror and treats edge association and task partitioning separately. This paper proposes a deviati...

Understanding Photon-Counting CT: Physics, Detector Technology, and Image Reconstructions

Photon-counting computed tomography (PCCT) represents a detector-level transformation in CT imaging. Unlike conventional energy-integrating detectors, photon-counting detectors directly convert individual X-ray interactions into electrical pulses and classify them according to energy. This architecture enables electron...

Quantum phenomenology from a classical integer automaton: an emergent Schrödinger sector, exact exchange statistics, CHSH violation without randomness, foliation-independent Bell statistics, a selection-rule theorem for ensemble entanglement, a stimulated parametric pair channel with a capacity wall, a surviving isotropy no-go, two engine-measured forced rationals, and obstruction theorems for dynamic geometry

What this is. A single bit-exactly reversible, integer-valued lattice automaton in which a broad range of quantum phenomenology arises as measurable, checksum-reproducible behaviour. Evolution is a permutation of integer microstates, not approximate numerical integration — which is why the gates below can be bit-exact...

CReIS: Computation Reuse through image similarity in ICN-based 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