Mobile Computing, Applications, and Services: 13th EAI International Conference, MobiCASE 2022, Messina, Italy, November 17-18, 2022, Proceedings

Description

This book constitutes the thoroughly refereed post-conference proceedings of the 13th International Conference on Mobile Computing, Applications, and Services, MobiCASE 2022, held in Messina, Italy, in November 17-18, 2022. The 9 full papers were carefully reviewed and selected from 25 submissions. The papers are organized in topical tracks: mobile computing; machine learning/deep learning; dependable systems; and emerging applications. Preface Organization Contents Mobile Computing Configuring Unconnected Embedded Devices with Smartphones 1 Introduction 2 Related Work 3 Configuration of Embedded Devices 4 Architecture 5 Device Specification of Configuration Data 6 Tool Chain 7 Generic Configuration App 8 Evaluation 9 Conclusion References Decentralized Federated Learning Methods for Reducing Communication Cost and Energy Consumption in UAV Networks 1 Introduction 2 Background and Related Work 2.1 Definition of Federated Learning 2.2 Overview of the Federated Average Algorithm (FedAvg) 2.3 Decentralised Federated Learning for UAV Networks 2.4 Decentralised Federated Learning (DFL) 3 Design and Implementation 3.1 UAV Model 3.2 Aggregation Model 3.3 Energy Consumption Model 3.4 Communication Cost Model 4 Simulation Result 5 Conclusion References Centralized Multicasting AODV Routing Protocol Optimized for Intermittent Cognitive Radio Ad Hoc Networks 1 Introduction 2 Related Work 3 System Model 4 Results 4.1 Throughput Simulation Results for MAODV and AODV 4.2 The SM Delay Simulation Results of the MAODV and AODV Routing Protocols 4.3 The NR Delay Simulation Results of the MAODV and AODV Routing Protocols 5 Conclusion A Appendix References Machine Learning/Deep Learning YouBrush: Leveraging Edge-Based Machine Learning in Oral Care 1 Introduction 2 Related Work 2.1 Oral Health Care and Mobile Apps 2.2 Oral Care and Gamification 2.3 Brushing Sensing 3 Application Design and Implementation 3.1 Audio Processing 3.2 GUI Design 3.3 Privacy and Latency Concerns 3.4 Gamification 4 Data Collection and Labeling 4.1 Methodology 4.2 Ethical Consideration 4.3 Labeling 5 Machine Learning Implementation 5.1 Preprocessing 5.2 Model Creation 5.3 Model Testing 5.4 Model Implementation 6 Conclusion References Construction and Evaluation of a Return Prediction Model for One-Way Car Sharing 1 Introduction 2 Related Work 3 Challenges of a One-Way ECS 3.1 One-Way ECS 3.2 Vehicle Relocation 3.3 Vehicle Deployment Prediction 4 Proposed Method 4.1 Summary of Proposed Methodology 4.2 Features Used in the Model 4.3 Return Station Prediction Model 4.4 Return Time Prediction Model 5 Evaluation 5.1 Data Collection 5.2 Evaluation of Return Station Prediction 5.3 Evaluation of Return Time Prediction 6 Conclusion References Dependable Systems A Bi-directional Attribute Synchronization Mechanism for Access Control in IoT Environments 1 Introduction 2 Related Work 3 Assumptions and System Modeling 3.1 Problem Formulation 4 Bi-directional Attribute Synchronization Mechanism 4.1 Attribute Mapping 4.2 Attribute Synchronization 4.3 Access Revalidation 5 Results 5.1 The UPPAAL Tool 5.2 Scenario and Models 5.3 Formal Verification of Models 6 Conclusion References Fault Tolerance in Cloud Manufacturing: An Overview 1 Introduction 1.1 Contributions 1.2 Paper Layout 2 Taxonomy 2.1 Cloud Manufacturing in Practice 2.2 Features of the Edge Devices 2.3 Reliability in Edge-Cloud Environment 2.4 Reasons for System Failures 2.5 Dealing with Failures 3 Findings and Research Gaps 4 Conclusion and Future Directives References Emerging Applications Investigating the Adoption of Mobile Government from Citizen’s Perspectives in Saudi Arabia 1 Introduction 2 Literature Review 2.1 Factors Affecting the Adoption/Acceptance of M-Government 3 Methodology 3.1 Sample of Study 3.2 Questionnaire 3.3 Variables and Measurement 4 Research Model and Hypothesis Development 5 Result and Analysis 6 Discussion 7 Conclusion References Search by Pattern in GPS Trajectories 1 Introduction 2 Related Work 3 Psychology of Search by Pattern 4 Algorithms Overview 4.1 Needleman-Wunsch Algorithm (NWA) 4.2 Smith-Waterman Algorithm (SWA) 5 Methodology 5.1 Pre-processing Geolife Data Set 5.2 Our Approach Based on Geohashing 5.3 Comparison of Methods 6 Experimental Results 6.1 Searching with Needleman-Wunsch Algorithm 6.2 Searching with Smith-Waterman Algorithm 6.3 Searching with Geohashing 6.4 Search by Pattern Starting with Identical Geohashes 7 Conclusion References Author Index