Smart Objects and Technologies for Social Goods : 8th EAI International Conference, GOODTECHS 2022, Aveiro, Portugal, November 16-18, 2022, Proceedings

This book constitutes the refereed post-conference proceedings of the 8th EAI International Conference on Smart Objects and Technologies for social Goods, GOODTECHS 2022, held in Aveiro, Portugal, in November 16-18, 2022 The 7 full papers presented were selected from 18 submissions and issue design, implementation, deployment, operation, and evaluation of smart objects and technologies for social good. Social goods are products and services provided through private enterprises, government, or non-profit institutions and are related to healthcare, safety, sports, environment, democracy, computer science, and human rights. Preface Organization Acknowledgement Contents Internet of Things Multi-UAV Network Logistics Task Allocation Algorithm Based on Mean-Field-Type Game 1 Introduction 2 Related Work 2.1 Mean-Field-Type Game in UAVs 2.2 Incentives with Consensus-Based Bundle Algorithm 3 System Model 3.1 Network Model 3.2 Task Model 3.3 Motion Model 3.4 Communication Model 4 Problem Formulation 4.1 Utility Function 4.2 Optimization Problem 5 Static Mean-Field-Type Game Analysis 6 Consensus-Based Bundle Algorithm Based Optimal Strategy Decision for Dynamic MFTG 6.1 Score Function 6.2 Task Allocation Model 6.3 Consensus-Based Bundle Algorithm 7 Performance Evaluation 7.1 Simulation Setup 7.2 Numerical Results 8 Conclusion References On the Influence of Grid Cell Size on Taxi Demand Prediction 1 Introduction 2 Related Work 2.1 Convolutional Neural Networks 2.2 Recurrent Neural or Long Short-Term Memory Networks 2.3 Graph Neural Networks 2.4 Research Gap, Question, and Hypothesis 3 Methodology 3.1 Dataset 3.2 Evaluation 3.3 Selection of Demand Prediction Approaches 3.4 Baseline Models 4 Experimental Results 4.1 M1-MLRNN: Multi-level Recurrent Neural Network 4.2 M2-pmlLSTM: Parallel Multi-task Learning Model 4.3 M3-STDGAT: Spatial-Temporal Dynamic Graph Attention Network 4.4 M4-CNNFC: Convolutional Neural Network with Fully Connected Layer 4.5 M5-CNN: Convolutional Neural Network 5 Discussion 5.1 RNN-Based Models 5.2 CNN-Based Models 5.3 Comparison Among Models and to Baselines 5.4 Limitations and Future Work 6 Conclusion References Social Parents as Active Agents in Building Emotionally Stable, and Healthy Communities: Testing PSsmile App 1 Introduction 1.1 Supporting the Development of Key Social and Emotional Competence 2 Opening Social-Emotional Learning to the Educational Community 2.1 Building Social and Emotional Competencies in the Educational Community 3 The PSsmile App: An Integrative Tool for a Community-Based Education 3.1 Drivers of the App 3.2 The Five-Week Program and Its Content 3.3 The Structure 3.4 Using the Program 3.5 A Closer Look to the App: Starting the Activities 3.6 Data Collection and Participant Profile 4 PSsmile App’s Modules 5 PSsmile App in Practice: What to Learn and How to Use It 6 PSsmile App – Alpha Testing and Pilot Use 7 Conclusions References Haptic-Based Cognitive Mapping to Support Shopping Malls Exploration 1 Introduction 2 The Test Application 2.1 Preliminary Evaluation 3 Open Issues and Future Work 4 Conclusions References Towards Design Recommendations for Social Engagement Platforms Supporting Volunteerism Targeting Older People in Local Communities 1 Introduction 2 Methodology 2.1 Finding and Selecting Social Engagement Platforms 2.2 Evaluations Methods 3 Evaluated Social Engagement Platforms 4 Recommendations 5 Conclusions References Experimentation of a Nighttime Wandering Assistance System Based on AAL Solutions to Foster Aging at Home 1 Introduction 2 Related Work 2.1 Smart Homes 2.2 Ambient Assisted Living System 3 Nighttime Wandering System 4 Experimental Assessment 4.1 Methodology 4.2 Material and Methods 4.3 Processing 5 Results and Discussion 6 Conclusion References Health Improving the Recommendations of Meals in the PROMISS Application 1 Introduction 2 Background 2.1 PROMISS Application 2.2 Meal and Product Recommendation 3 Method 3.1 Data Usage 3.2 Evaluation of the Task Completion Time 4 Implementation 4.1 Overview and General Flow 4.2 Preset/Protein Algorithm 4.3 Core + Addition Algorithm 4.4 Preference Algorithm 4.5 Association Rule Learning Algorithm 5 Results 5.1 Training Data Duration Results 5.2 Results on the Number of Clicks 6 Conclusion and Discussion References Author Index