edge computing

Collaborative Federated Learning to Secure 6G-IoT with Deep Convolutional Generative Adversarial Network

A distributed IDS-based collaborative FL (IDS-CFL) across different learning levels: device and fog-cloud, to reduce data transfer and improve accuracy and enable fast processing is proposed, using a Deep Convolutional Generative Adversarial Network model to train data at each level.

Editorial: Education in synthetic biology

Synthetic Biology (SB) has emerged as a highly innovative field over the last two decades, bringing with it a vast array of tools, objectives, and significant recognition within the broader scientific community. As the field has grown, it has attracted researchers from a wide range of disciplines, including biology, bi...

Virtual Power Plants: A Review of Cyber-Physical Infrastructures, Hierarchical Operation, and Coupled Market Participation

The developments of VPPs in key technologies, scheduling and market design are reviewed, showing how the future digital technologies will transform VPPs out of simple resource pooling to multi-market revenue generation.

CA-FMLOps: Evaluating Edge Filtering and Carbon-Aware Scheduling in Sustainable MLOps

Artificial intelligence can support climate mitigation, but the computing systems used to develop and operate it can also intensify energy demand, data movement, and greenhouse-gas emissions. This paper presents and evaluates CA-FMLOps—Carbon-Aware Frugal Machine Learning Operations—as a design-science framework for cl...

6G Fundamentals for Telecom Engineers

This technical white paper provides a practical introduction to 6G and the evolution from 5G-Advanced toward the ITU IMT-2030 framework. It covers the 6G vision, standardisation status, usage scenarios, capability targets, system architecture, AI-native networks, AI/ML in RAN and network operations, integrated sensing...

Laboratory dataset for a locally fabricated flat Coanda-effect screen: 52 clear-water runs and model capacity predictions

Laboratory dataset for a locally fabricated flat Coanda-effect screen Fifty-two clear-water runs on a flat Coanda-effect screen intake fabricated from locally available materials, with the measured capture compared against the predictions of the Coanda-effect screen analysis software distributed by the US Bureau of Rec...

Advanced crop monitoring using unmanned aerial vehicle with Internet of Things and cloud computing

This chapter includes detailed explanation on unmanned aerial vehicles (UAVs), Received Signal Strength Indicator (RSSI) scanning and communication protocols that can be used to maximize agricultural yield. Internet of Things (IoT) devices in agricultural applications can offer important plant data like soil moisture,...

waldowda/spec-echem: v0.3.1

This is the first GitHub release since v0.1.0, so it covers everything added in 0.2.0 and 0.3.0 as well. Full detail is in CHANGELOG.md. spec-echem synchronises a potentiostat with a UV-Vis spectrometer for spectroelectrochemistry, so that optical and electrochemical data share one time base. It is still pre-release: t...

An Efficient Resource Allocation Algorithm with Load Balancing Mechanism to Enhance QoS Parameters in Fog Environment

This study applied an effective approach named Spider Monkey Optimization (SMO) to address the mentioned challenges and employs the proposed framework to ensure the Quality of Service (QoS) parameters performance.

Code, protocols and data for: Null-model treatment of the sensory-motor boundary changes an evolutionary connectome comparison

Code, frozen pre-registration protocols, analysis outputs, figures and manuscripts for the preprint Null-model treatment of the sensory-motor boundary changes an evolutionary connectome comparison. Agents whose brain is a compressed adult Drosophila FlyWire v783 connectome evolve in a 2D foraging world next to populati...

ER-Curriculum-DDQN for Critical Task Offloading in UAV-MEC via Transparent LEO Relays

Hardware-Adaptive Inference Orchestration: Zero-Overhead Local LLM Agents on Resource-Constrained Edge Devices

Autonomous AI agents typically rely on multi-turn ReAct loops that demand repeated system-prompt evaluation, persistent state tracking, and frequent tool selection. On constrained edge hardware—especially CPU-only devices with approximately 8 GB of RAM—this style of orchestration creates two compounding failure modes:...