Green, Pervasive, and Cloud Computing
Description
The GPC 2025 proceedings deal with green, pervasive, and cloud computing focusing on pervasive and environmentally sustainable computing.
The 33 full papers and 2 short papaers were carefully reviewed and selected from 69 submissions. They were organized in topical sections as follows: Security, Trust, and Resilience,Wireless, Vehicular, and Underwater Networking,Federated, Edge, and Green Learning Systems,Mobile Crowdsensing, Scheduling, and Collaborative Optimization,Intelligent Sensing, Perception, and Applied Analytics and Graphs, Simulation, and Robust AI Models.
More details
Content
.- Security, Trust, and Resilience
.- CrossDID: A Secure and Accountable Cross-Chain Decentralized Identity System.
.- Lamina: An Efficient Asynchronous BFT through Lightweight Broadcast and Optimistic Paths.
.- Reliable and efficient data sharing framework based on blockchain in VANETs.
.- Cross-Network Malicious Traffic Detection Via Graph Neural Networks and Dual Distribution Alignment.
.- ACCG: A Collaborative-Adversarial Framework for Robust Code Generation.
.- A Game Theory Guided Attack-Defense Framework for Character-Level Modality Perturbations.
.- A Poisoning Attack System Targeting Critical Layers of Backdoors in Federated Learning Models.
.- Wireless, Vehicular, and Underwater Networking
.- Joint Semantic-Fidelity and Energy Optimization for AUV Collaborative Sensing via Multi-Agent Reinforcement Learning.
.- LoRaSim: A LoRa Network simulator via Hybrid Physics-Data Synergy.
.- Research on a Message Transmission Strategy in VANETs Based on Implicit Acknowledgment with Adaptive Beacon Intervals.
.- CICC: Concurrent Inter-channel Collision Cancellation in LoRa Networks.
.- Asymmetric IoT communication and sensing technology based on RF-Computing.
.- P-VBF: A Vertical Projection Vector-Based Forwarding Routing Protocol for Multi-UUV Cooperative Perception.
.- Federated, Edge, and Green Learning Systems
.- FedMIS: Federated Medical Image Segmentation with Knowledge Distillation.
.- DCML: An Efficient Cluster-Based Meta-Learning Algorithm with Knowledge Selective Reuse.
.- Lightweight Gated Spiking Neural Network Based on Temporal Residual Connection.
.- What Data Changed: Empirical Analysis of the Impact Mechanism of Streaming Data on Model Training in AIoT Environment.
.- FUPEC: A Federated Unlearning-Empowered Personalized Edge Caching Paradigm.
.- Programmable Context-Aware Orchestration of Collaborative Inference Using Software-Defined AIoT in Personal Healthcare Wearables.
.- Energy-Efficient Federated Fine-Tuning of Large Language Models in Heterogeneous Edge Environments.
.- Mobile Crowdsensing, Scheduling, and Collaborative Optimization
.- UWCS: A Unified Online Capability Assessment Framework for Hybrid Workers in Mobile Crowdsensing.
.- NSP-MCS: Reinforcement Learning-Based Navigation-Aware Task Scheduling for Mobile Crowdsourcing.
.- Low-Latency Onboard Compression Task Scheduling and Routing Co-Optimization Algorithm for LEO Satellites.
.- Collaborative Optimization of Heterogeneous Multi-Agent Integrated Sensing and Computing Based on QMIX Algorithm.
.- Optimizing Exploration-Coverage Trade-offs for Multi-UAV Systems Under Energy-Constrained Path Planning.
.- Multi-Agent Deep Reinforcement Learning for Cloud-UAV-RSU Collaborative Vehicular Computation Offloading.
.- Intelligent Sensing, Perception, and Applied Analytics
.- Scalable Data-Driven Modeling of Postoperative Pain Risk Factors Using Distributed Machine Learning Algorithms.
.- A Lightweight Traffic Flow Prediction Model Based on Multi-Scale Spatiotemporal Characteristics.
.- Inductive adaptive spatio-temporal Kriging for urban air quality inference.
.- ParaDepth: Distributed Multi-View 3D Reconstruction for Fire Scene.
.- CogPlanner: A Cognitive Navigation Method for Embodied Intelligence Guided by Indoor Floor Plans.
.- MultiMo-Concen: Multimodal Learning Concentration Recognition Based on Vision and Millimeter Wave.
.- MoiréTemp: High-precision Temperature Measurement Based on Moiré Pattern.
.- Graphs, Simulation, and Robust AI Models
.- BadFF: Inspect the weakness and robustness of forward-forward neural networks.
.- Accelerating Graph Similarity Search with Maximum Common Subgraphs.