Collaborative Computing: Networking, Applications and Worksharing : 19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part III / edited by Honghao Gao, Xinheng Wang, Nikolaos Voros.

Format:

Book

Contributor:

Gao, Honghao, editor.

Wang, Xinheng, editor.

Voros, Nikolaos, editor.

Series:

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 1867-822X ; 563

Language:

English

Subjects (All):

Computer systems.

Information storage and retrieval systems.

Computer networks.

Data protection.

Software engineering.

Computers, Special purpose.

Computer System Implementation.

Information Storage and Retrieval.

Computer Communication Networks.

Data and Information Security.

Software Engineering.

Special Purpose and Application-Based Systems.

Local Subjects:

Computer System Implementation.

Information Storage and Retrieval.

Computer Communication Networks.

Data and Information Security.

Software Engineering.

Special Purpose and Application-Based Systems.

Physical Description:

1 online resource (412 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

The three-volume set LNICST 561, 562 563 constitutes the refereed post-conference proceedings of the 19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023, held in Corfu Island, Greece, during October 4-6, 2023. The 72 full papers presented in these proceedings were carefully reviewed and selected from 176 submissions. The papers are organized in the following topical sections: Volume I : Collaborative Computing, Edge Computing & Collaborative working, Blockchain applications, Code Search and Completion, Edge Computing Scheduling and Offloading. Volume II: Deep Learning and Application, Graph Computing, Security and Privacy Protection and Processing and Recognition. Volume III: Onsite Session Day2, Federated learning and application, Collaborative working, Edge Computing and Prediction, Optimization and Applications.

Contents:

Onsite Session Day 2

Multi-agent Reinforcement Learning based Collaborative Multi-task Scheduling for Vehicular Edge Computing

A Novel Topology Metric for Indoor Point Cloud SLAM Based on Plane Detection Optimization

On the Performance of Federated Learning Network

Federated learning and application

FedECCR: Federated Learning Method with Encoding Comparison and Classification Rectification

CSA_FedVeh: Cluster-based Semi-Asynchronous Federated Learning framework for Internet of Vehicles

Efficiently Detecting Anomalies in IoT: A Novel Multi-Task Federated Learning Method

A Novel Deep Federated Learning-based and Profit-Driven Service Caching Method

A Multi-Behavior Recommendation Algorithm Based on Personalized Federated Learning

FederatedMesh: Collaborative Federated Learning for Medical Data Sharing in Mesh Networks

Collaborative working

Enhance broadcasting throughput by associating network coding with UAVs relays deployment in emergency communications

Dynamic Target User Selection Model For Market Promotion with Multiple Stakeholders

Collaborative Decision-making Processes Analysis of Service Ecosystem: A Case Study of Academic Ecosystem Involution

Operationalizing the Use of Sensor Data in Mobile Crowdsensing: A Systematic Review and Practical Guidelines

Enriching Process Models with Relevant Process Details for Flexible Human-Robot Teaming

Edge Computing

Joint Optimization of PAoI and Queue Backlog with Energy Constraints in LoRa Gateway Systems

Enhancing Session-based Recommendation with Multi-granularity User Interest-aware Graph Neural Networks

Delay-constrained Multicast Throughput Maximization in MEC Networks for High-Speed Railways

An Evolving Transformer Network based on Hybrid Dilated Convolution for Traffic Flow Prediction

Prediction, Optimization and Applications

DualDNSMiner: A Dual-stack Resolver Discovery Method Based on Alias Resolution

DT-MUSA: Dual Transfer Driven Multi-Source Domain Adaptation for WEEE Reverse Logistics Return Prediction

A Synchronous Parallel Method with Parameters Communication Prediction for Distributed Machine Learning.

Notes:

Includes bibliographical references and index.

ISBN:

9783031545313