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Mobile and Ubiquitous Systems: Computing, Networking and Services: 20th EAI International Conference, MobiQuitous 2023, Melbourne, VIC, Australia, November 1417, 2023, Proceedings, Part I 2024 ed. [Pehme köide]

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Mobile and Ubiquitous Systems: Computing, Networking and Services: 20th EAI International Conference, MobiQuitous 2023, Melbourne, VIC, Australia, November 1417, 2023, Proceedings, Part I 2024 ed.Suurem pilt
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Püsilink: https://www.kriso.ee/db/9783031639883.html
Märksõnad:
These two-volume proceedings constitute the refereed post-conference proceedings of the 20th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2023, held in Melbourne, Australia, during November 14-17, 2023.





 





The 65 papers presented in these proceedings were carefully reviewed and selected from 161 submissions. The conference papers are organized in topical sections on:





 





Part I - Tracking and Detection; IoT; Federated learning; Networks; Activity recognition; Security Management; Urban/Mobile Crowdsensing.





Part II - Urban/Mobile Crowdsensing; Edge computing; Crowdsourcing, Platforms and localization; Activity recognition and prediction; AI and machine learning; Mobile edge and fog computing; Mobile augmented reality and applications for mobile computing; interaction technologies; AutoQuitous workshop.
.- Tracking and Detection.



.- NoD: Lightweight Continuous Neighbor Discovery on Everyday Devices.



.- Decentralized Collaborative Inertial Tracking.



.- High-Performance Features in Generalizable Fingerprint- based Indoor
Positioning.



.- SCORE: Scalable Contact Tracing Over Uncertain Trajectories.



.- IoT.



.- FaultBit : Generic and Eicient Wireless Fault Detection Using the
Internet of Things.



.- DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT
Sensor Data.



.-  Federated Reinforcement Learning for Automated LoRaWAN Management in
Industrial IoT.



.- A Hybrid Approach to Monitor Context Parameters for Optimising Caching for
Context-Aware IoT Applications.



.- LOADHoC: Towards the Automatic local Distribution of Computation using
Existing IoT Devices.



.- E-Go Bicycle Intelligent Speed Adaptation System for Catching the Green
Light.



.- Federated Learning.



.-  FedGCS: Addressing Class Imbalance in Long-Tail Federated Learning.



.- FedRC: Representational Consistency Guided Model Uploading Mechanism for
Asynchronous Federated Learning.



.- RADEAN: A Resource Allocation Model Based on Deep Reinforcement Learning
and Generative Adversarial Networks in Edge Computing.



.- Networks.



.-  A Stream Data Service Framework for Real-time Vehicle Companion
Discovery.



.- KS-Autoformer: An Autoformer-based SOC Prediction Framework for Electric
Vehicles.



.-  Deep Reinforcement Learning-based Multi-Node Collaborative Task
Offloading Optimization in 6G

Space-Air-Ground Integrated Networks.



.-  Securing Wireless Communication in Critical Infrastructure: Challenges
and Opportunities.



.- Activity Recognition.



.- Cross-user activity recognition via temporal relation optimal transport.



.- SelfAct: Personalized Activity Recognition based on Self-Supervised and
Active Learning.



.- A Novel Method for Wearable Activity Recognition with Feature Evolvable
Streams.



.-  Lets Vibrate with Vibration: Augmenting Structural Engineering with
Low-Cost Vibration Sensing.



.- Security Management.



.- Research on Data Drift and Class Imbalance in Android Malware detection.



.- Reputation-based Dissemination of Trustworthy Information in VANETs.



.- Reputation Systems for Supply Chains: The Challenge of Achieving Privacy
Preservation.



.- Data Management in Appendable-block Blockchains: A Case Study for IT
Life-cycle Management.



.- Exploiting the Potential Anomaly Detection in Automobile Safety Data with
Multi-type Neural Network.



.- Urban/Mobile Crowdsensing.



.- HAUM3: A Height Aware Urban Map Matching Mechanism.



.- A resource-eicient approach of GNSS activation for pedestrian monitoring.