edge computing

FedOTO: Toward Low-Latency Drowsiness Detection Service in IoV via Structured Federated Pruning

Drowsy driving continues to be a leading cause of motor vehicle accidents (MVAs), accounting for nearly 30% of all incidents according to recent studies. While numerous drowsiness detection methods have been developed, existing solutions often struggle with practical deployment due to limitations in detection accuracy,...

A Novel Parallel Approach With Index Optimization for Truss Maintenance

The <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="bai-ieq1-3702362.gif"/></alternatives></inline-formula>-truss is a type of cohesive subgraph where each edge is contained in at least <inline-formula><tex-math notation="LaTeX">$...

Demand-Aware Multi-Area Multi-UAV Empowered Mobile Edge Computing: A Joint Energy and Delay Optimization

Multi-UAV-assisted mobile edge computing (MEC) has emerged as a promising paradigm for smart city infrastructures, where multiple uncrewed aerial vehicles (UAVs) collaboratively provide airborne computing services by bringing computational resources closer to end users, thereby reducing both communication costs and ser...

Synchronization-Free Backdoor Attacks on Edge-Deployed Neural Network for IoT Systems

Deep Neural Networks integrated into sensor-driven embedded systems have become fundamental to edge computing applications. While Inertial Measurement Units (IMUs) are ubiquitous in these architectures due to their cost-efficiency, the reliance on data-driven models expands the attack surface of the embedded computing...

Prediction-Assisted Multi-UAV Online Service Migration and Trajectory Control for MEC-Empowered Vehicular Networks

The rapid proliferation of intelligent vehicular networks and mobile edge computing (MEC) demands substantial improvements in computation efficiency, latency reduction, and service continuity, especially under high mobility and stochastic task generation.Conventional single-uncrewed aerial vehicle (UAV) solutions with...

Efficient Serverless Federated Learning With Edge Model Migration on Non-IID Data

Federated Learning (FL) has emerged as a transformative distributed learning paradigm, offering a privacy-preserving solution for collaborative model training. However, its conventional cloud-centric architecture suffers from a significant communication bottleneck casused by frequent model transmissions and heterogeneo...

A Holistic Remote Fork Strategy Toward Fast Function Scaling in Serverless Edge Computing

Serverless edge computing, despite its flexibility and efficiency, is hindered by high startup latency during peak load. Remote fork, employing either Checkpoint/Restore (C/R) or Remote Direct Memory Access (RDMA), offers a potential solution for function scaling acceleration. Although RDMA fork is faster, the opportun...

Secure Split Offloading and Trajectory Design for UAV-Assisted Multi-Exit Collaborative DNN Inference

Collaborative inference (CI) has emerged as a promising paradigm in mobile edge computing, where deep neural network (DNN) models are split and collaboratively processed by wireless devices and edge servers to reduce communication overhead and improve inference efficiency. Unmanned aerial vehicles (UAVs) with agile mob...

Diffusion-Guided Multi-Objective Reinforcement Learning for Hybrid Action Optimization in Collaborative Edge AIGC Networks

Enforcing Service Level Agreements With Verifiability and Privacy in Pervasive Edge Computing Environments

Edge computing gained popularity for its promises of low latency and high-quality computing services to users. However, it has also introduced the challenge of mutual untrust between user and edge devices for service level agreement (SLA) compliance. This obstacle hampers wide adoption of edge computing, especially in...

HFedAR: Resource Allocation for Adaptive Retransmission-Based Hierarchical Federated Learning in Open RAN

The flexible architecture of the open radio access network (O-RAN) provides effective support for the deployment of federated learning (FL). However, existing FL schemes in wireless networks often suffer from packet errors, which lead to reduced model test accuracy and increased training delay. In this paper, we propos...

Feature Map Compression for Split Learning in Mobile Wireless Environment: Theory and Practice

Split Learning (SL) enables collaborative model training by distributing computation between resource-constrained mobile edge devices and a central server. In this distributed framework, mobile edge devices compute the early layers of the model and transmit feature maps to the server. However, due to practical mobile w...