edge computing

Truthful Online Double Auction-Based Resource Allocation Mechanisms for Partial Computation Offloading in Collaborative Edge Computing

As mobile applications become increasingly computation-intensive, mobile devices (MDs) face growing limitations due to their constrained computational capabilities and battery life. Collaborative Edge Computing (CEC) has emerged as a promising solution to address these challenges by enabling multiple edge service provi...

PreSFC: Predictive SFC Migration via Multi-Slot Mobility Forecasting in MEC Networks

Network Function Virtualization (NFV) is a foundational technology for Mobile Edge Computing (MEC). It delivers network services by chaining Virtual Network Functions (VNFs) into sequential Service Function Chains (SFCs). One of the most critical challenges in MEC is how to provide continuous and stable services to hig...

Service Enhancement and Reliability Assurance in 6G Vehicular Networks via a Stackelberg Game-Theoretic Approach

With the rapid development of 6G and Internet of Vehicles (IoV) technologies, the volume of computation-intensive tasks generated by intelligent vehicles is growing exponentially. Given limited onboard processing capabilities, vehicles increasingly rely on edge servers deployed by service providers (SPs) at roadside un...

MERA: A Green Edge Resource Control System With Privacy-Preservation via Mean-Field Reinforcement Learning

The global rollout of 5G networks has spurred the rapid deployments of edge servers for hosting latency-sensitive web applications, which improves quality of experience (QoE). However, current efforts fall short in the substantial energy costs associated with the 24/7 operation of edge servers and overlook user privacy...

Meta-Reinforcement Learning for Computation Offloading and Resource Allocation in MEC-Enabled Immersive Metaverse

D3NN: Adaptive Partitioning and Cross-Tier Resource Orchestration for Cloud–Edge Collaborative Inference

Deep Neural Networks (DNNs) have become foundational to intelligent systems, yet deploying them efficiently under strict latency, resource, and privacy constraints remains challenging. While cloud-only inference suffers from transmission latency and privacy risks, and edge-only execution is limited by hardware capacity...

Toward 6G Edge Intelligence: Lightweight LLMs for Intent-Driven Network Automation

Future 6G networks are envisaged to tightly integrate communication, sensing, and computing, demanding real-time, intent-driven intelligence at the edge. While large language models (LLMs) excel in intent recognition and semantic reasoning, their application to real-time network lifecycle management at the edge is limi...

Salmonella risk prediction in poultry farms via deep learning image classification Models, Cross-Region Validation, and edge computing

DDPG-Attention-Based Resource Allocation and Trajectory Optimization in Hierarchical MEC

Multi-access Edge Computing (MEC) can effectively process Internet of Things (IoT) data by transferring computing intensive tasks to edge servers, and has become an effective mechanism to meet the growing demand for computing. The flexible Uncrewed Aerial Vehicle (UAV) and High-Altitude Platform (HAP) with powerful res...

Heterogeneous graph-enhanced multi-objective dependency-aware task offloading in vehicular edge computing

FlexiTensor: Adaptive Multi-Task Deployment of LLMs on Resource-Constrained Heterogeneous Edge Devices

The drive for privacy-preserving and low-latency artificial intelligence necessitates executing Large Language Models (LLMs) directly on heterogeneous, resource-constrained edge devices. This paradigm presents a challenge: efficiently running large models across multiple end/edge devices under a strict energy budget. E...

Collaborative task offloading for electric vehicle supply equipment in smart charging networks: A distributed edge computing approach from a generalized Nash equilibrium perspective